JP7820464B2 - system - Google Patents

Description

The technology disclosed herein relates to a system.

Patent document 1 discloses a persona chatbot control method executed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Japanese Patent Application Laid-Open No. 2022-180282

Users who want to learn to move like professional athletes face the challenge of finding the exact movement corrections they need to make due to differences in their own physique and that of professional athletes.

As a means for solving this problem, the system of the present invention includes a measuring means for measuring the user's physique, a recording means for recording specific movements performed by the user, an emotion engine for recognizing the user's emotions, a means for transmitting physique data measured by the measuring means and movement data recorded by the recording means to a server, a comparison means for comparing the user's movements with those of a specific professional athlete based on the physique data and the movement data and inputting prompt sentences that suggest modifications to the user's movements into a generative AI model, thereby comparing the user's physique with that of the professional athlete, and, based on the comparison results, comparing the recorded user movements with the movements of the specific professional athlete whose physique is similar to that of the user, a means for suggesting modifications to the movements to the user based on the comparison results and taking information from the emotion engine into consideration, and a means for displaying the suggested modifications on the user's device.

1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. 10 is a conceptual diagram illustrating an example of the configuration of a data processing system according to a second embodiment. FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. FIG. 10 is a conceptual diagram illustrating an example of the configuration of a data processing system according to a third embodiment. FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. FIG. 10 is a conceptual diagram illustrating an example of the configuration of a data processing system according to a fourth embodiment. FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. 1 shows an emotion map onto which multiple emotions are mapped. 1 shows an emotion map onto which multiple emotions are mapped. FIG. 10 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. FIG. 10 is a sequence diagram showing a processing flow of the data processing system in Application Example 1 of Form Example 1. FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2. FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a third embodiment of the third form example. FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3. FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined.

Below, an example of an embodiment of a system relating to the technology disclosed herein will be described with reference to the accompanying drawings.

First, let me explain the terminology used in the following explanation.

In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, a processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose Computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

In the following embodiments, coded random access memory (RAM) is memory in which information is temporarily stored and is used as work memory by the processor.

In the following embodiments, the coded storage refers to one or more non-volatile storage devices that store various programs, parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

In the following embodiments, a communication I/F (Interface) with a symbol is an interface that includes a communication processor, an antenna, etc. The communication I/F controls communication between multiple computers. Examples of communication standards that can be applied to the communication I/F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

In the following embodiments, "A and/or B" is synonymous with "at least one of A and B." In other words, "A and/or B" means that it may be just A, just B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and/or B" also applies when three or more things are expressed connected by "and/or."

[First embodiment]

Figure 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and/or a LAN (Local Area Network).

The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I/F 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

The reception device 38 is equipped with a touch panel 38A, a microphone 38B, etc., and receives user input. The touch panel 38A detects contact with an indicator (e.g., a pen or finger) to receive user input via the indicator. The microphone 38B detects the user's voice to receive user input via voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

The output device 40 is equipped with a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible by the user 20 (e.g., audio and/or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

The communication I/F 44 is connected to the network 54. The communication I/Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54.

Figure 2 shows an example of the main functions of the data processing device 12 and smart device 14.

As shown in FIG. 2, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

Storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

Next, we will explain the identification process performed by the identification processing unit 290 of the data processing device 12.

"Example 1"

In one embodiment of the present invention, a user first measures their own physique using a 3D body scan. This measurement is performed, for example, using a device that provides a 3D body scan. Next, the user performs a specific movement, such as batting or pitching in baseball. This movement is recorded using a camera or sensor. The recorded movement is analyzed by AI. The AI uses deep learning to compare the user's movement with that of a professional athlete with a similar physique. Then, based on the comparison results, suggestions are made to the user regarding movement modifications. These suggestions are presented to the user, for example, through an application on a device such as a smartphone or tablet.

"Example 2"

In one embodiment of the present invention, a user first measures their own physique using a 3D body scan. This measurement is performed, for example, using a device that provides a 3D body scan. Next, the user performs a specific movement, such as batting or pitching in baseball. This movement is recorded using a camera or sensor. The recorded movement is analyzed by AI. The AI uses deep learning to compare the user's movement with that of a professional athlete with a similar physique. Then, based on the comparison results, suggestions are made to the user regarding movement modifications. These suggestions are presented to the user, for example, through an application on a device such as a smartphone or tablet.

The processing flow for each example form is explained below.

"Example 1"

Step 1: The user measures their physique using a 3D body scan. This measurement is performed, for example, using a device that provides a 3D body scan.

Step 2: Next, the user performs a specific action, such as batting or pitching a baseball. This action is recorded using cameras and sensors.

Step 3: The recorded movements are analyzed by AI, which uses deep learning to compare the user's movements with those of professional athletes with a similar physique.

Step 4: Based on the comparison results, the user is then given suggestions for modifying their behavior. These suggestions are presented to the user, for example, through an application on a device such as a smartphone or tablet.

"Example 2"

Step 1: The user measures their physique using a 3D body scan. This measurement is performed, for example, using a device that provides a 3D body scan.

Step 2: Next, the user performs a specific action, such as batting or pitching a baseball. This action is recorded using cameras and sensors.

Step 3: The recorded movements are analyzed by AI, which uses deep learning to compare the user's movements with those of professional athletes with a similar physique.

Step 4: Based on the comparison results, the user is then given suggestions for modifying their behavior. These suggestions are presented to the user, for example, through an application on a device such as a smartphone or tablet.

(Example 1)

Next, we will explain Example 1 of Form Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

Conventional motion analysis systems have struggled to accurately measure and record a user's physique and movements and provide appropriate feedback. Furthermore, they lacked the means to perform highly accurate analysis when comparing a user's movements with those of professional athletes. This made it difficult for users to effectively improve their own movements.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, means for transmitting recorded movement data and physique data to the server, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement corrections to the user based on the comparison results, and means for displaying the suggested corrections on the user's device. This allows users to accurately measure and record their own physique and movements, and compare them with the movements of professional athletes to obtain specific corrections.

"User" refers to an individual who uses the system to measure and record their physique and movements and receive feedback.

"Physique" refers to a user's physical characteristics and measurements, including data measured by means such as a 3D body scan.

"Movement" refers to specific physical movements or performances performed by the user, which are recorded using cameras and sensors.

"Server" refers to a computer system that receives data sent by users, analyzes it using AI means, and provides the results to users.

"3D body scanning" refers to technology and equipment that measures a user's physique in three dimensions and is used to obtain detailed data about the user's body.

"AI means" refers to means that use artificial intelligence technology to analyze a user's movements and compare them with those of professional athletes.

"Deep learning" is a field of artificial intelligence that uses multi-layer neural networks to learn the characteristics of data and perform advanced analysis and predictions.

"Fixes" refer to specific changes or suggestions suggested to improve user experience.

"Terminal" refers to the device used by the user to access the system, including smartphones and tablets.

"Comparison results" refers to the results of comparing the user's movements with those of a professional athlete using AI means, and includes data that serves as the basis for feedback to the user.

This invention is a system that measures and records a user's physique and movements, analyzes them using AI, and then suggests specific movement modifications to the user. A specific embodiment of this system is described below.

First, the user measures their physique using a device that provides a 3D body scan. Examples of devices used include Kinect and Structure Sensor. This acquires the user's physique data and stores it on the user's device (smartphone or tablet).

Next, the user performs a specific movement, such as a baseball batting or pitching form. This movement is recorded using a smartphone camera or a GoPro. The recorded video data is saved on the user's device.

The user's device sends recorded movement and physique data to the server. An internet connection is required for transmission. The user presses the "Send Data" button in the application, and the device uploads the data to the server.

The server inputs the received movement data and physique data into an AI model. This AI model uses TENSORFLOW (registered trademark) and PyTorch, and uses deep learning to compare the user's movements with those of professional athletes with a similar physique. The server inputs the data into the AI model and begins the comparison process. Once the process is complete, the comparison results are generated.

The server generates corrections for the user's movements based on the comparison results of the AI model. These corrections are compiled into specific advice and improvement methods. For example, specific advice such as "To increase the swing speed of your batting form, you need to rotate your hips more quickly" is generated.

The server sends the generated corrections to the user's device, which then displays the received corrections within the application. The user opens the application and checks the suggested corrections. For example, specific advice such as "How to practice to rotate your hips faster" is displayed.

As a concrete example, consider the case where a user wants to improve their baseball batting form. The user first measures their physique using a 3D body scanning device (such as Kinect). Next, they record their batting form using a smartphone camera. This recorded data is input into a deep learning model using TensorFlow and compared with the movements of professional batters. Based on the comparison results, a dedicated app suggests improvements to the user's batting form.

An example prompt is, "Taking the user's physique data and a video of their batting form as input, compare it with the movements of a professional batter and suggest corrections."

In this way, users can compare their own movements with those of a professional and get specific corrections.

The flow of the identification process in Example 1 will be explained using Figure 11.

Step 1:

Users measure their physique using a 3D body scanning device.

Input: User's body location information

Specific action: The user stands in front of Kinect and rotates their body according to the instructions.

Data processing: The scanning device captures the user's physique data and generates a 3D model.

Output: The generated physique data is saved on the user's device.

Step 2:

The user performs a specific movement (e.g., a baseball batting stance) and records that movement.

Input: User action

Specific operation: The user sets up their smartphone on a tripod and takes a photo of their batting form.

Data processing: The camera records the user's movements as video data.

Output: Recorded video data is saved to the user's device.

Step 3:

The user's device sends recorded movement and physique data to the server.

Input: Movement data and physique data

Specific action: The user presses the "Send Data" button within the application.

Data processing: The device compresses the data and uploads it to a server via the Internet.

Output: Data is sent to the server.

Step 4:

The server inputs the received movement data and physique data into the AI model.

Input: Movement data and physique data

Specific operation: The server inputs the data into the AI model and begins the comparison process.

Data calculation: The AI model uses deep learning to compare the user's movements with those of professional athletes with a similar physique.

Output: The comparison results are generated.

Step 5:

The server generates corrections to the user's behavior based on the comparison results of the AI model.

Input: Comparison results

Specific actions: The server generates specific advice such as, "To increase your swing speed in your batting form, you need to rotate your hips faster."

Data processing: Analyze the comparison results and generate corrections in text format.

Output: Corrections are generated.

Step 6:

The server sends the generated modifications to the user's device.

Input: Corrections

Specific operation: The server sends the modifications to the user's device.

Data processing: Convert the corrections into the appropriate format and send.

Output: The changes are sent to the user's device.

Step 7:

The user's device will display the received corrections within the application.

Input: Corrections

What happens: The user opens the application and sees the suggested fixes.

Data processing: Display corrections in a format that is easy for users to understand.

Output: The user can see the changes.

(Application Example 1)

Next, we will explain Application Example 1 of Form Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

Traditional factory work lacks specific feedback to improve the efficiency and safety of workers' movements. This leads to problems such as reduced work efficiency and increased physical strain if workers continue to perform inappropriate movements. Furthermore, the lack of a system to optimize workers' movements makes it difficult to provide appropriate guidance to individual workers.

The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes a means for measuring the user's physique, a means for recording specific movements performed by the user, an AI means for analyzing the recorded movements and comparing them with the movements of workers with a similar physique, a means for suggesting to the user movement modifications based on the comparison results, and a means for displaying suggestions for optimizing the movements of factory workers. This makes it possible to provide specific feedback to improve the efficiency and safety of workers' movements.

"User" refers to an individual who uses the system to have their physique and movements measured and analyzed.

"Physique" refers to the user's physical characteristics and measurements.

"Movement" refers to a specific physical movement or task performed by the user.

"Recording means" refers to devices or methods for saving user actions as video or data.

"AI means" refers to technology that uses artificial intelligence to analyze recorded movements and compare them with other movements.

"Deep learning" is a type of artificial intelligence that uses multi-layered neural networks to analyze and learn from data.

"Means for suggesting modifications" refers to a method or device for suggesting improvements to the user's behavior based on the analysis results.

A "factory worker" is a worker who performs specific tasks within a factory.

"Means for displaying optimization suggestions" refers to a device or method for visually presenting suggestions to the user for improving operational efficiency and safety.

"3D body scanning" is a technology for measuring a user's physique in three dimensions.

A system for implementing this invention includes means for measuring a user's physique, means for recording specific movements performed by the user, AI means for analyzing the recorded movements and comparing them with the movements of workers with a similar physique, means for suggesting to the user corrections to their movements based on the comparison results, and means for displaying suggestions for optimizing the movements of factory workers.

Hardware and software used

3D body scanning device: A device for measuring the user's physique in three dimensions.

Camera: A device used to record user actions. An example is the Logitech C920.

Smart glasses: A device that visually presents the user with corrections to their movements. An example is Google® Glass®.

Tablet: A device used to display operational corrections and optimization suggestions. An example is the iPad (registered trademark).

Server: A computer system for processing data and running AI models.

Software: Uses programming libraries such as Python (registered trademark), OpenCV (registered trademark), and Keras.

Data processing and calculation

1. Acquisition of physique data: The server acquires the user's physique data using a 3D body scanning device. This data represents the user's physical characteristics and dimensions in three dimensions.

2. Recording of actions: The server uses a camera to record the user's actions. The recorded actions are saved as image data.

3. Movement analysis: The server inputs the recorded image data and physique data into the AI model and analyzes the movements. The AI model uses deep learning to compare the user's movements with those of workers with a similar physique.

4. Suggested modifications: The server will suggest modifications to the user's behavior based on the analysis results. These suggestions will be displayed on the smart glasses or tablet.

Specific examples

When a factory worker lifts a heavy object, their movements are recorded using a 3D body scan and camera, and then analyzed by AI. As a result of the analysis, suggestions are made for corrections such as the angle of the waist and the position of the hands when lifting. This allows workers to work efficiently and safely.

Example prompt

"Record factory workers lifting heavy objects, input this data into the AI along with 3D body scan data, compare it with efficient movements, and suggest corrections."

The above is a form for implementing this invention.

The flow of the specific processing in Application Example 1 will be explained using Figure 12.

Step 1:

The server uses a 3D body scanning device to obtain the user's physique data. The input includes the user's physical characteristics and dimensions. Data processing involves converting these characteristics and dimensions into a data format that represents them in three dimensions. The output is the user's physique data.

Step 2:

The server uses a camera to record the user's actions. The input includes the specific actions performed by the user. The data is processed by saving the actions as video data. The output is image data of the recorded actions.

Step 3:

The server inputs the recorded image data and physique data into the AI model and analyzes the movements. The input includes image data and physique data. To calculate the data, the AI model uses deep learning to compare the user's movements with those of workers with a similar physique. The output is the movement analysis results.

Step 4:

The server suggests behavioral modifications to the user based on the analysis results. The input includes the behavioral analysis results. Data processing generates specific suggestions for modifications. The output is the suggested modifications.

Step 5:

The server displays suggested corrections on the smart glasses or tablet. The input includes suggested corrections. Specific operations include generating data to visually display the suggestions and sending it to the smart glasses or tablet. The output displays suggested corrections that the user can visually confirm.

The above is the processing flow of this program.

(Example 2)

Next, we will explain Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

Conventional motion analysis systems have struggled to accurately measure and record a user's physique and movements and suggest appropriate corrections. Furthermore, there was a lack of means to provide specific corrections to users, which led to problems with effective motion improvement.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, artificial intelligence means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, and means for presenting the modifications to the user. This makes it possible to accurately measure and record the user's physique and movements, suggest appropriate modifications, and present specific modifications to the user.

"User" refers to an individual who uses the system to measure and record their physique and movements and receive suggested movement modifications.

"Means for measuring body size" refers to devices or technologies that measure the dimensions and shape of a user's body in three dimensions.

"Means for recording specific actions" refers to devices or technologies that use cameras or sensors to record actions performed by users.

"Artificial intelligence means" refers to AI technology used to analyze recorded movement data and compare the user's movements with those of professional athletes.

"Means for suggesting corrections" refers to technology that extracts and suggests areas for improvement in the user's behavior based on the analysis results of artificial intelligence means.

"Means for presenting corrections" refers to a device or application that visually or audibly communicates suggested corrections to the user.

"3D body scanning" refers to technology that measures a user's physique in three dimensions.

"Deep learning" refers to a machine learning technique using multiple layers of neural networks used in artificial intelligence methods.

Form for implementing the invention

This invention relates to a system that measures a user's physique, records and analyzes specific movements, and suggests modifications to those movements. A specific embodiment of this system is described below.

User body measurements

Users measure their physique using a 3D body scanning device. Examples of such devices include a 3D scanner or motion capture system. Users follow the device's instructions and rotate 360 degrees to scan their physique. The device processes the scan data in real time and generates physique data for the user. The generated data is then sent to a server via wireless communication.

Recording actions

Next, the user performs a specific movement, such as batting or pitching in baseball. The user's movement is recorded using a camera or sensor. The hardware used may include a high-resolution camera or a motion capture system. The device generates the recorded video data or motion capture data and sends it to a server.

Analysis of movement

The server inputs the received physique data and movement data into an artificial intelligence model. This artificial intelligence model uses deep learning to compare the user's movements with those of professional athletes. The software used is a "deep learning framework." The server extracts corrections for the user's movements from the output of the artificial intelligence model.

Suggested behavior fixes

The server sends the extracted correction points to the device. The device then displays the received correction points in a dedicated application. The user can check the correction points through the application and learn specific ways to improve. For example, specific instructions such as "increase the angle at the start of the bat swing by 10 degrees" are displayed.

Specific examples

Example 1: Correcting baseball batting form

The user measures their physique using a 3D body scanning device and sends the data to a server. Then, a high-resolution camera is used to capture their batting form, and the video data is uploaded to the server. The server then analyzes the data using a deep learning framework and identifies areas for improvement. These areas are presented to the user via a dedicated application. The user then works to improve their batting form while viewing the application.

Example 2: Correcting pitching form

The user measures their physique using a 3D scanner and sends the data to the server. They then use a motion capture system to record their pitching form and upload the data to the server. The server then analyzes the data using a deep learning framework and extracts corrections that are presented to the user through a dedicated application. The user works to improve their pitching form while looking at the application.

Example prompt

Prompt 1: Baseball batting form

"Using the user's 3D body scan data and video data of their batting form as input, please generate an AI model that compares it with data from professional baseball players and suggests corrections."

Prompt 2: Pitching form

"Using the user's 3D body scan data and motion capture data of their pitching form as input, please compare it with data from professional baseball players and generate an AI model that suggests corrections."

The flow of the identification process in Example 2 will be explained using Figure 13.

Program processing flow

Step 1: Measure the user's body size

The user activates the 3D body scanning device. Following the device's instructions, the user rotates 360 degrees to scan their physique. The device processes the scan data in real time and generates the user's physique data. The generated data is sent to the server via wireless communication.

Input: User's physique scan data

Data processing: Real-time processing of scanned data

Output: User's physique data

Step 2: Record the action

Using a high-resolution camera and motion capture system, the user performs a specific movement, such as batting or pitching in baseball. The device records the movement and generates video data or motion capture data, which is then sent to the server.

Input: User action

Data processing: Recording movements and generating data

Output: Video data or motion capture data

Step 3: Analyze the Movement

The server inputs the received physique data and movement data into an artificial intelligence model. This artificial intelligence model uses deep learning to compare the user's movements with those of professional athletes. The server extracts corrections for the user's movements from the output of the artificial intelligence model.

Input: physique data, movement data

Data processing: Deep learning behavior comparison

Output: Behavior corrections

Step 4: Propose behavioral modifications

The server sends the extracted correction points to the device. The device then displays the received correction points in a dedicated application. The user can check the correction points through the application and learn specific ways to improve. For example, specific instructions such as "increase the angle at the start of the bat swing by 10 degrees" are displayed.

Input: Behavior corrections

Data processing: Preparing for corrections

Output: Present the corrections to the user

Add specific actions

Step 1: Measure the user's body size

The user activates the 3D body scanning device and follows the device's instructions to scan their physique. The scan data is processed in real time to generate the user's physique data, which is then transmitted to the server via wireless communication.

Step 2: Record the action

The user installs a high-resolution camera and films their baseball batting form. The camera records high-resolution video and saves it on the device. The device then compresses the saved video data and uploads it to the server.

Step 3: Analyze the Movement

The server inputs the received physique data and video data into a deep learning framework. The AI model compares the user's batting form with that of professional baseball players. Features such as joint angles and movement speed are used for the comparison. The server extracts corrections for the user's movement from the output of the AI model.

Step 4: Propose behavioral modifications

The server sends the extracted corrections to the device in JSON format. The device then displays the received corrections in a dedicated application. The user can review the corrections through the application and learn specific ways to improve. For example, specific instructions such as "increase the angle at the start of your bat swing by 10 degrees" are displayed.

(Application Example 2)

Next, we will explain Application Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

Traditional optimization of factory robots relies on manual adjustments and experience, making it difficult to achieve efficient and accurate optimization. Furthermore, because individual optimization based on the user's physique and movements is not performed, general-purpose optimization methods often fail to produce sufficient results. This poses a risk of reduced factory productivity and quality.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, means for recording and analyzing the movements of robots used in factories, and means for suggesting modifications to optimize the robot's movement. This enables individual optimization based on the user's physique and movements, making it possible to efficiently and accurately optimize the movement of factory robots.

"User" means an individual or corporation that uses the system to measure and analyze their physique and movements.

"Physique" refers to the shape and dimensions of the user's body, as measured by means such as a 3D body scan.

"Specific actions" refer to specific movements or tasks performed by the user, which are recorded using cameras or sensors.

"Recording means" refers to devices or methods that use cameras, sensors, etc. to save user actions as data.

"AI means" refers to technology that uses artificial intelligence to analyze recorded movement data and compare the user's movements with those of professional athletes.

"Deep learning" is a type of artificial intelligence that uses multi-layered neural networks to analyze and learn from data.

A "professional athlete" is an athlete who specializes in a particular sport and is the subject to be compared with the user's movements.

"Behavior Modifications" are specific improvements suggested to optimize the user's behavior.

"Robots used in factories" are automated machinery and devices used on factory production lines and in workshops.

"Optimization" means adjusting something to the most efficient and effective state for a specific purpose.

The following hardware and software are used to implement this invention. Hardware requires a 3D scanner, camera, sensor, server, and user terminal (smartphone or tablet). Software uses Python, OpenCV, Keras, etc.

First, the user measures their physique using a 3D scanner. The 3D scanner captures the user's body shape and dimensions with high precision and saves them as digital data. This data is then sent to a server.

Next, the user performs a specific action. For example, if they are imitating the actions of a robot used in a factory, the action is recorded using a camera or sensor. The recorded action data is also sent to the server.

The server inputs the received 3D scan data and movement data into the AI means. The AI means analyzes this data using deep learning and compares the user's movements with those of professional athletes with a similar physique. Based on the comparison results, it suggests movement modifications to the user.

Furthermore, to optimize the operation of robots used in factories, robot operation data is also recorded and analyzed using AI methods. Based on the analysis results, corrections are suggested to optimize the robot's operation. These suggestions are displayed on the user's device.

As a concrete example, consider optimizing the operation of a welding robot used in a factory. A 3D scanner captures the robot's shape data, and cameras and sensors record its operation data. This data is input into an AI tool, and suggestions are made for adjusting the welding angle and speed based on the analysis results.

An example of a prompt to input into the generative AI model is, "Please input the welding robot's motion data along with the 3D scan data, and suggest optimal motion corrections."

As described above, this invention enables individual optimization based on the user's physique and movements, allowing the operation of factory robots to be optimized efficiently and with high precision.

The flow of the specific processing in Application Example 2 will be explained using Figure 14.

Step 1:

Users measure their body size using a 3D scanner.

Input: User's body

Data processing: A 3D scanner captures the user's body shape and dimensions with high precision and stores them as digital data.

Output: 3D scan data

Step 2:

The user performs a specific action, which is recorded using cameras and sensors.

Input: User action

Data processing: Cameras and sensors record user movements as video and sensor data.

Output: Operation data

Step 3:

The server receives the 3D scan data and movement data.

Input: 3D scan data, motion data

Data processing: The server receives this data and prepares it for input into the AI means.

Output: Prepared data

Step 4:

The server analyzes the 3D scan data and movement data using AI tools.

Input: Prepared data

Data processing: Using a deep learning model, we compare the user's movements with those of professional athletes with a similar physique.

Output: Analysis results

Step 5:

The server then suggests behavioral modifications to the user based on the analysis results.

Input: Analysis results

Data processing: Based on the analysis results, specific modifications are generated to optimize user behavior.

Output: Suggested fixes

Step 6:

Suggested fixes will be displayed on the user's device.

Enter: Suggested fix

Data processing: Convert the proposal content into a format for display on the user's device.

Output: Corrections displayed on the user's device

Step 7:

The movements of robots used in factories are recorded and sent to a server.

Input: Robot movement

Data processing: Cameras and sensors record the robot's movements as video and sensor data, which are then sent to a server.

Output: Robot movement data

Step 8:

The server analyzes the robot's operation data using AI means and suggests modifications for optimization.

Input: Robot movement data

Data processing: Using deep learning models, we analyze the robot's behavior and generate corrections for optimization.

Output: Suggested modifications to the robot's behavior

Step 9:

Suggestions for modifying the robot's behavior will be displayed on the user's device.

Input: Suggested modifications to the robot's behavior

Data processing: Convert the proposal content into a format for display on the user's device.

Output: Robot behavior corrections displayed on the user's device.

Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

"Example 1"

In one embodiment of the present invention, a system is provided that includes a means for measuring a user's physique, a means for recording specific movements performed by the user, an AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, a means for suggesting movement modifications to the user based on the comparison results, and an emotion engine that recognizes the user's emotions. The emotion engine recognizes emotions from the user's facial expressions and tone of voice and provides this information to the AI means. The AI means suggests movement modifications that take into account not only the comparison between the user's movements and the movements of the professional athletes, but also the user's emotions. For example, if the user is feeling frustrated, the AI means takes this information into consideration and suggests modifications in a manner that is easy for the user to accept. In this way, it is possible to suggest movement improvements that take the user's emotions into account.

"Example 2"

In one embodiment of the present invention, a system is provided that includes a means for measuring a user's physique, a means for recording specific movements performed by the user, an AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, a means for suggesting movement modifications to the user based on the comparison results, and an emotion engine that recognizes the user's emotions. The emotion engine recognizes emotions from the user's facial expressions and tone of voice and provides this information to the AI means. The AI means suggests movement modifications that take into account not only the comparison between the user's movements and the movements of the professional athletes, but also the user's emotions. For example, if the user is feeling frustrated, the AI means takes this information into consideration and suggests modifications in a manner that is easy for the user to accept. In this way, it is possible to suggest movement improvements that take the user's emotions into account.

The processing flow for each example is explained below.

"Example 1"

Step 1: Measure the user's physique using a 3D body scan.

Step 2: Record a specific action performed by the user (e.g., batting or pitching a baseball) using a video camera or motion sensor.

Step 3: The recorded movements are analyzed using AI tools and compared with the movements of professional athletes with similar physiques.

Step 4: Understand the user's emotions using an emotion engine that recognizes emotions from the user's facial expressions and tone of voice.

Step 5: The AI means suggests corrections to the user's movements, taking into account the results of comparing the user's movements with those of professional athletes and the user's emotions.

Step 6: The proposed behavioral modifications are presented to the user via an application on a device such as a smartphone or tablet.

"Example 2"

Step 1: Measure the user's physique using a 3D body scan.

Step 2: Record a specific action performed by the user (e.g., batting or pitching a baseball) using a video camera or motion sensor.

Step 3: The recorded movements are analyzed using AI tools and compared with the movements of professional athletes with similar physiques.

Step 4: Understand the user's emotions using an emotion engine that recognizes emotions from the user's facial expressions and tone of voice.

Step 5: The AI means suggests corrections to the user's movements, taking into account the results of comparing the user's movements with those of professional athletes and the user's emotions.

Step 6: The proposed behavioral modifications are presented to the user via an application on a device such as a smartphone or tablet.

(Example 1)

Next, we will explain Example 1 of Form Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

Traditional movement improvement systems make suggestions based on the user's physique and movement data, but because they don't take the user's emotions into account, users may find it difficult to accept the suggestions. Furthermore, since the corrections to movement are not specific, it is difficult for users to understand how to improve.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, artificial intelligence means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, means for recognizing the user's emotions, and means for suggesting movement modifications taking into account the emotion data. This makes it possible to suggest movement improvements that take into account the user's physique and emotions.

"Means for measuring the user's physique" refers to devices or methods that measure the user's physical characteristics and acquire them as digital data.

"Means for recording specific actions performed by a user" refers to devices or methods that use video or sensors to record specific actions performed by a user as digital data.

"Artificial intelligence means" refers to software or systems that analyze recorded data and use specific algorithms to compare the user's movements with other data (e.g., the movements of professional athletes).

"Means for suggesting movement corrections" refers to devices or methods that suggest movement improvements and correction methods to the user based on the analysis results.

"Means for recognizing user emotions" refers to devices or methods that analyze emotions from the user's facial expressions, tone of voice, etc., and acquire them as digital data.

"Means for suggesting movement modifications taking into account emotional data" refers to a device or method that suggests movement modifications in a more acceptable form based on the user's emotional data.

"3D body scanning" is a technology that scans the user's body in three dimensions and obtains detailed physique data.

"Deep learning" is an artificial intelligence technology that uses multi-layered neural networks to analyze data and learn patterns and features.

This invention is a system that analyzes a user's physique and movements, compares them with the movements of professional athletes, and suggests movement modifications to the user. Furthermore, by recognizing the user's emotions and making suggestions that take their emotional data into consideration, it is possible to present movement modifications in a way that is easy for the user to accept.

Hardware and software used

1. User body measurements

Users measure their physique using equipment that provides a 3D body scan. This equipment may include, for example, a 3D scanner or a dedicated body scan application. This obtains detailed physique data such as the user's height, weight, and body fat percentage.

2. Recording actions

Users use their smartphone camera or a dedicated motion capture device to record specific movements (e.g., batting or pitching in baseball). The recorded data includes detailed movement information such as swing speed and angle.

3. Sending operation data

The device transmits recorded movement and physique data to a server. This transmission occurs in real time over the Internet, and the data is encrypted using a secure communication protocol.

4. Movement Analysis

The server inputs the received data into an AI model, which uses deep learning algorithms to compare the user's movements with those of professional athletes. The software used is a deep learning framework such as TensorFlow or PyTorch. For example, it calculates how much the user's swing speed differs from the average speed of professional athletes.

5. Emotional Recognition

The device uses an emotion engine that analyzes the user's facial expressions and tone of voice in real time. This emotion engine determines whether the user is frustrated or satisfied and sends that information to a server. The software used is an emotion recognition API (e.g., Microsoft® Azure® Emotion API).

6. Suggestions for behavioral modifications

Based on the comparison of the movements and the emotional data, the server suggests to the user how to modify their movements. For example, if the user is feeling frustrated, the server might suggest, "Maybe if you just change the way you grip the bat a little, your swing will become smoother." The user is notified of these suggestions through an application on their device, such as a smartphone or tablet.

Specific examples

Example 1: Improving baseball batting form

The user measures their physique using a 3D scanner and records their batting form with their smartphone camera. The device then sends the recorded data to a server, which then inputs the data into an AI model and compares it with the form of professional baseball players. If the emotion engine detects the user's frustration, the server will make suggestions such as, "Maybe if you just change the way you grip the bat a little, your swing will become smoother." The user is notified of these suggestions via a smartphone application.

Example prompt for a generative AI model

"Using the user's 3D body scan data and video of their batting form as input, please compare it with the form of a professional baseball player and suggest improvements while taking into account the user's emotions."

In this way, it becomes possible to suggest movement improvements that take into account the user's physique and emotions.

The flow of the identification process in Example 1 will be explained using Figure 15.

Step 1:

Users measure their physique using a device that provides a 3D body scan. As input, a 3D scanner is used to scan the user's body. As output, detailed physique data such as the user's height, weight, and body fat percentage is generated. This data is stored on the device.

Step 2:

Users use their smartphone's camera or a dedicated motion capture device to record specific movements (e.g., batting or pitching in baseball). As input, the user's movements are captured by the camera or sensors. As output, detailed movement data such as swing speed and angle is generated. This data is stored on the device.

Step 3:

The device sends the recorded movement data and physique data to the server. The movement data and physique data stored on the device are used as input. As output, this data is sent to the server via the internet. The data is encrypted and uses a secure communication protocol.

Step 4:

The server inputs the received data into the AI model. The movement data and physique data sent to the server are used as input. The output is the results of the AI model comparing the user's movements with those of professional athletes. This AI model uses deep learning algorithms and utilizes deep learning frameworks such as TensorFlow and PyTorch.

Step 5:

The device uses an emotion engine that analyzes the user's facial expressions and tone of voice in real time. As input, the user's facial and voice data is captured through a camera and microphone. As output, emotional data is generated, such as whether the user is frustrated or satisfied. This data is sent to a server.

Step 6:

The server suggests movement corrections to the user based on the movement comparison results and emotional data. The AI model's comparison results and emotional data are used as input. The output is movement corrections generated for the user. For example, if the user is feeling frustrated, the server might suggest, "Maybe your swing will become smoother if you just change the way you grip the bat slightly." The suggestion is notified to the user through an application on their device, such as a smartphone or tablet.

(Application Example 1)

Next, we will explain Application Example 1 of Form Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

While conventional motion analysis systems can analyze a user's physique and movements, they lack the ability to suggest motion modifications that take the user's emotions into account. Furthermore, when optimizing the movements of robots working in factories, they lack the ability to monitor movement efficiency and error rates in real time and provide improvement suggestions. This leads to issues such as reduced movement efficiency for users and robots, preventing optimal performance.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, an emotion engine for recognizing the user's emotions, means for suggesting movement modifications taking into account information from the emotion engine, means for optimizing the robot's movements, means for monitoring the robot's movement efficiency and error rate, and means for making improvement suggestions in real time. This makes it possible to suggest movement modifications taking the user's emotions into account, and further enables the movement efficiency and error rate of robots working in factories to be monitored in real time, enabling optimal movement performance to be achieved.

"Means for measuring the user's physique" refers to devices or technologies for accurately measuring the dimensions and shape of the user's body.

"Means for recording specific actions performed by a user" refers to devices or technologies that use cameras, sensors, etc. to record specific actions performed by a user.

"AI means for analyzing recorded movements and comparing them with the movements of professional athletes with similar physiques" refers to artificial intelligence technology that analyzes recorded user movement data and compares it with the movement data of professional athletes with similar physiques.

"Means for suggesting to the user points for correction of movements based on the comparison results" refers to devices or technologies that suggest points for improvement or correction of movements to the user based on the results of movement comparison by AI.

The "emotion engine that recognizes user emotions" is a technology that identifies emotions from the user's facial expressions, tone of voice, etc.

"Means for suggesting behavioral modifications by taking into account information from an emotion engine" refers to devices or technologies that suggest behavioral modifications based on the user's emotional information recognized by an emotion engine.

"Means for optimizing robot operation" refers to devices and technologies that optimize the operation of robots working in factories to ensure that they are efficient and effective.

"Means for monitoring a robot's operational efficiency and error rate" refers to devices and technologies that monitor a robot's operational efficiency and error rate in real time.

"Means for making improvement suggestions in real time" refers to devices and technologies that analyze robot operation data in real time and immediately suggest areas for improvement.

A system for implementing this invention includes means for measuring a user's physique, means for recording specific movements performed by the user, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting to the user movements modifications based on the comparison results, an emotion engine for recognizing the user's emotions, means for suggesting movements modifications taking into account information from the emotion engine, means for optimizing the robot's movements, means for monitoring the robot's movement efficiency and error rate, and means for making improvement suggestions in real time.

Hardware and software configuration

Hardware:

3D body scanning device

Camera (e.g., Logitech C920)

Computer (e.g., Intel Core i7, 16GB RAM)

Software:

OpenCV: Motion recording and image processing

Keras: Loading and predicting deep learning models

EmotionRecognizer: Emotion Recognition Library

Processing flow

The server first acquires the user's physique data using a 3D body scanning device. Then, it uses a camera to record specific movements performed by the user. The recorded movement data is preprocessed using OpenCV and analyzed using a deep learning model using Keras. The analysis results are compared with the movement data of professional athletes with similar physiques.

Furthermore, EmotionRecognizer is used to recognize the user's emotions and suggests behavioral modifications taking into account information from the emotion engine. The suggested modifications are presented to the user.

For robots working in factories, measures are used to optimize the robot's operations. The robot's operating efficiency and error rate are monitored in real time, and suggestions for improvement are made. This makes it possible to optimize the robot's operating performance.

Specific examples

For example, if a robot assembling parts in a factory is moving slowly, the AI will analyze its movements and suggest correcting the angle and speed of its movements. Furthermore, if the robot is making frequent errors, it will determine through emotion recognition that it is feeling frustrated and suggest that it move more slowly.

Example prompt

"To optimize the movements of robots that assemble parts in factories, design a system that uses 3D body scans and cameras to record their movements, analyzes them with AI, and suggests optimal movement patterns. Additionally, add functionality to monitor the robot's movement efficiency and error rate and suggest improvements in real time."

The flow of the specific processing in Application Example 1 will be explained using Figure 16.

Step 1:

The server acquires the user's physique data using a 3D body scanning device. As input, it scans the user's body dimensions and shape, and generates 3D physique data as output. This data is used to accurately determine the user's physique.

Step 2:

The server uses a camera to record specific actions performed by the user. As input, it captures the actions performed by the user in real time, and as output, it generates video data of the actions. This video data is used for subsequent action analysis.

Step 3:

The server preprocesses the recorded motion data using OpenCV. It receives motion video data as input and generates preprocessed image data as output. This preprocessing includes image resizing and noise removal.

Step 4:

The server analyzes the preprocessed image data using a deep learning model using Keras. It receives the preprocessed image data as input and extracts movement features as output. These features are used to analyze the user's movements in detail.

Step 5:

The server compares the extracted features with the motion data of professional athletes with a similar physique. It receives the user's motion features and the motion data of the professional athletes as input, and generates the comparison results as output. These comparison results are used to identify areas in the user's motion that need to be corrected.

Step 6:

The server uses EmotionRecognizer to recognize the user's emotions. As input, it captures the user's facial expressions and tone of voice while in action, and generates emotional data as output. This emotional data is used to understand the user's emotional state.

Step 7:

The server takes into account information from the emotion engine to suggest behavioral modifications. It receives the comparison results and emotion data as input, and generates behavioral modifications for the user as output. These modifications are suggested taking into account the user's emotional state.

Step 8:

The server executes a process to optimize the operation of robots working in factories. It receives robot operation data as input and generates optimized operation patterns as output. This optimization is performed to improve the robot's operation efficiency.

Step 9:

The server monitors the robot's operating efficiency and error rate in real time. It receives the robot's operating data in real time as input and generates monitoring results of operating efficiency and error rate as output. These monitoring results are used to understand the robot's operating status.

Step 10:

The server makes improvement suggestions in real time. It receives monitoring results as input and generates improvement suggestions as output. These improvement suggestions are made to optimize the robot's operational performance.

(Example 2)

Next, we will explain Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

Previous movement improvement systems struggled to accurately measure a user's physique and movements and provide appropriate feedback. Furthermore, they didn't take the user's emotions into account when proposing movement improvements, resulting in a lack of feedback that was easy for the user to accept. This limited the effectiveness of the system's movement improvements.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, means for transmitting the recorded movements, artificial intelligence means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, means for recognizing the user's emotions, and means for suggesting movement modifications taking the recognized emotions into consideration. This makes it possible to accurately measure the user's physique and movements and provide appropriate feedback. Furthermore, because suggestions for movement improvement are made taking the user's emotions into consideration, feedback is provided in a form that makes the suggestions more receptive to the user, improving the effectiveness of movement improvement.

"Means for measuring the user's physique" refers to devices or technologies that measure the dimensions and shape of the user's body in three dimensions.

"Means for recording specific actions performed by a user" refers to devices or technologies that use video or sensors to record specific actions performed by a user.

"Means for transmitting recorded behavior" refers to communication means for transmitting recorded behavior data to a server or other device.

"Artificial intelligence means" refers to artificial intelligence technology used to analyze recorded movement data and compare the user's movements with those of professional athletes.

"Means for suggesting movement corrections" refers to devices or technologies that suggest movement improvements to the user based on the analysis results of artificial intelligence means.

"Means for recognizing user emotions" refers to devices or technologies for recognizing emotions from the user's facial expressions, tone of voice, etc.

"Means for suggesting behavioral modifications taking into account recognized emotions" refers to devices or technologies that suggest behavioral modifications in a way that is easy for the user to accept, based on the user's emotional information.

This invention is a system that measures a user's physique, records specific movements, analyzes those movements, and suggests corrections. The system can recognize the user's emotions and take them into consideration when suggesting corrections to movements.

First, the user measures their physique using a 3D body scanning device. Specifically, devices such as the "Structure Sensor" or "Kinect" are used. This allows the user's physique data to be acquired and stored on the device.

Next, the user performs a specific movement (e.g., batting or pitching a baseball). This movement is recorded using a camera or sensor (e.g., a GoPro camera or a Vicon motion capture system). The recorded movement data is saved on the device.

The device sends recorded movement and physique data to a server. The data is sent using a secure protocol (e.g., HTTPS).

The server inputs the received data into an AI model, which is built using a deep learning framework (e.g., TensorFlow or PyTorch) and compares the user's movements with those of professional athletes with a similar physique. The server generates comparison results and identifies movement modifications.

The server sends the identified corrections to the device, which then presents the corrections to the user through an application on the device, such as a smartphone or tablet. For example, specific advice such as "You should increase the angle of your bat swing a little more" is displayed.

In addition, the device inputs the user's facial expressions and tone of voice into an emotion engine. The emotion engine uses Affectiva and the Microsoft Azure Emotion API to recognize the user's emotions in real time. The recognition results are stored on the device.

The device sends the recognized emotional information to the server. The server reevaluates the AI model taking the emotional information into account and suggests modifications that the user can easily accept. For example, if the user feels frustrated, the server will generate a suggestion such as "First, relax, and then try to be aware of your release points," and send it to the device. The device will then display this suggestion to the user.

Specific examples

Example 1: Improving baseball batting form

The user uses the "Structure Sensor" to measure their physique and a GoPro camera to record their batting form. The device then sends this data to a server. The server uses TensorFlow to compare the batting form with that of professional baseball players and suggests, via a smartphone app, that the user "increase the angle of the bat's swing a little more." If the user is feeling frustrated, the emotion engine recognizes this and the server suggests, "First, relax, and then try to be more conscious of your swing angle."

Example 2: Improving pitching form

The user uses Kinect to measure their physique and a Vicon motion capture system to record their pitching form. The device then sends this data to a server. The server uses PyTorch to compare the form with that of professional pitchers and suggests, via a tablet app, that the release point should be moved a little further forward. If the user is feeling frustrated, the emotion engine recognizes this and the server suggests, "First, relax, and then try to focus on the release point."

Example prompt

"The user uses a 3D body scanning device to measure their physique and a camera to record their batting form. The AI uses TensorFlow to compare their form with that of professional baseball players and suggests corrections via a smartphone app. If the user is feeling frustrated, the emotion engine recognizes this and the AI suggests corrections in a way that is easy for the user to accept."

The flow of the identification process in Example 2 will be explained using Figure 17.

Step 1:

Users measure their own physique using a 3D body scanning device. Specifically, users acquire physique data using a "Structure Sensor" or "Kinect." The input is the dimensions and shape of the user's body, and the output is physique data as a 3D model. This data is stored on the device.

Step 2:

The user performs a specific movement (e.g., batting or pitching a baseball). The user records the movement using a GoPro camera or a Vicon motion capture system. The input is the user's movement, and the output is high-resolution video data. This data is stored on the device.

Step 3:

The device sends recorded movement data and physique data to the server. The input is movement data and physique data, and the output is data sent to the server. Data is sent using a secure protocol (e.g., HTTPS).

Step 4:

The server inputs the received data into an AI model. The AI model is built using TensorFlow and PyTorch and compares the user's movements with those of professional athletes with a similar physique. The input is movement data and physique data, and the output is the comparison results, including suggestions for movement correction.

Step 5:

The server sends the identified corrections to the terminal. The input is the comparison result, and the output is the corrections sent to the terminal. The terminal then presents the corrections to the user through an application on a device such as a smartphone or tablet. For example, specific advice such as "You should increase the angle of your bat swing a little more" is displayed.

Step 6:

The device inputs the user's facial expressions and tone of voice into the emotion engine. The emotion engine uses "Affectiva" and "Microsoft Azure Emotion API" to recognize the user's emotions in real time. The input is the user's facial expressions and tone of voice, and the output is recognized emotional information. This information is stored on the device.

Step 7:

The device sends the recognized emotional information to the server. The input is emotional information, and the output is the transmission of emotional information to the server. The server reevaluates the AI model taking the emotional information into account and suggests corrections that are easy for the user to accept. For example, if the user is feeling frustrated, the server will generate a suggestion such as "First, relax, and then try to be aware of your release point," and send it to the device. The device will display this suggestion to the user.

(Application Example 2)

Next, we will explain Application Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

Conventional motion analysis systems record a user's physique and movements, compare them with those of professional athletes, and suggest corrections, but they were not designed to optimize the movements of robots working in factories. They also lacked the ability to detect abnormalities in the robots and suggest corrections to their movements based on that information. This made it difficult to efficiently manage the movements of robots in factories.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, means for recording the robot's movements and proposing optimal movement patterns, and means for detecting abnormalities in the robot and proposing movement modifications based on that information. This not only improves the user's movements, but also enables efficient movement management and optimization of robots within factories.

"User" refers to an individual who uses the system to measure and analyze their physique and movements.

"Physique" refers to the user's physical characteristics and measurements.

"Specific movements" refers to specific actions or movements performed by the user (e.g., batting or pitching form in baseball).

"Recording means" refers to devices or methods that use cameras, sensors, etc. to save the actions of users or robots as digital data.

"AI means" refers to a system that uses artificial intelligence to analyze data and output results according to a specific purpose.

"Deep learning" refers to a machine learning technique that uses multi-layered neural networks to learn data and extract complex patterns and features.

A "professional athlete" refers to an athlete who has high skills in a particular sport and engages in the sport professionally.

"Behavior modifications" refers to specific changes or adjustments proposed to improve the user's or robot's behavior.

"Robot" refers to an automated mechanical device that performs work in a factory.

"Abnormal" refers to conditions such as vibrations or temperature rises that deviate from the robot's normal operation.

"Means of sensing" refers to methods or devices that use sensors or monitoring devices to detect abnormalities.

"Optimal movement pattern" refers to a series of steps or methods of robot movement proposed to perform a task efficiently and effectively.

We will now explain the mode for implementing this invention. First, a 3D body scanning device is used to measure the user's physique. Specifically, a 3D scanner such as Intel RealSense is used to acquire the user's physique data. This data is used to accurately record the user's physical characteristics and dimensions.

Next, cameras and sensors are used to record specific actions performed by the user. For example, an Intel RealSense camera is used to record user actions with high accuracy. This recorded data is then analyzed using AI tools, which will be described later.

The recorded movement data is analyzed using AI tools that use deep learning, such as TensorFlow and Keras. The AI tools compare the user's movements with those of professional athletes with a similar physique, and suggest movement corrections. These suggestions are presented to the user via an application on their smartphone or tablet.

Furthermore, in order to optimize the movements of robots working in factories, it includes a means for recording the movements of the robots and proposing optimal movement patterns. The movements of the robots are recorded using a 3D scanner or camera and analyzed by the AI means. The AI means proposes optimal movement patterns and makes the robots' movements more efficient.

In addition, vibration and temperature sensors are used to detect abnormalities in the robot. OpenCV and PyTorch are used to detect abnormalities and suggest corrections to behavior based on that information. This optimizes the robot's behavior and enables it to work more efficiently.

For example, if a robot assembles parts in a factory and its movements slow down or abnormal vibrations occur, the system records the movement and the AI suggests the optimal movement pattern. If the emotion engine detects an abnormality, it will reflect this in the suggestions for correcting the movement.

An example of a prompt to input to the generative AI model is as follows:

"To optimize the movements of robots assembling parts in a factory, AI should suggest optimal movement patterns using data recorded by 3D scans and cameras. It should also detect abnormal vibrations and temperature increases in the robot and suggest movement corrections based on that information."

The flow of the specific processing in Application Example 2 is explained using Figure 18.

Step 1:

The user uses a 3D body scanning device to obtain physique data.

Input: User's body

Output: 3D body scan data

How it works: The user stands in front of the 3D scanner, which scans their entire body and generates digital data.

Step 2:

The user's specific actions are recorded using cameras and sensors.

Input: User action

Output: Operation record data

Specific actions: The user performs a specific action (e.g., batting or pitching) in front of the camera, and the camera records that action with high accuracy.

Step 3:

The server analyzes the recorded movement data using AI methods.

Input: Motion record data, 3D body scan data

Output: Motion analysis results

Specific operation: The server uses TensorFlow and Keras to input motion recording data and 3D body scan data into an AI model to analyze the user's movements.

Step 4:

Based on the results of the behavior analysis, the server suggests behavioral modifications to the user.

Input: Motion analysis results

Output: Proposed behavior changes

Specific actions: Based on the analysis results, the server generates specific corrections to improve the user's behavior and presents them to the user via a smartphone or tablet application.

Step 5:

The robot's movements are recorded using a 3D scanner and camera.

Input: Robot movement

Output: Robot operation record data

Specific movements: As the robot performs its tasks, 3D scanners and cameras record its movements.

Step 6:

The server analyzes the robot's recorded operation data using AI methods.

Input: Robot movement record data

Output: Robot movement analysis results

Specific operation: The server uses TensorFlow and Keras to input recorded robot movement data into an AI model and analyze the robot's movement.

Step 7:

The server detects an abnormality in the robot.

Input: Robot vibration data, temperature data

Output: Anomaly detection results

Specific operation: The server analyzes data from vibration and temperature sensors using OpenCV and PyTorch to detect abnormalities.

Step 8:

The server proposes optimal movement patterns based on the results of the robot's movement analysis and anomaly detection.

Input: Robot behavior analysis results, anomaly detection results

Output: Optimal motion pattern proposal

Specific behavior: The server integrates the analysis results and anomaly detection results, generates specific behavior patterns to optimize the robot's behavior, and applies them to the robot.

The specific processing unit 290 transmits the results of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the results of the specific processing. The microphone 38B captures audio indicating the user's input regarding the results of the specific processing.

The control unit 46A transmits voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

Data generation model 58 is a so-called generative AI (Artificial Intelligence) model. An example of data generation model 58 is generative AI such as ChatGPT (registered trademark) (Internet search <URL: https://openai.com/blog/chatgpt>). Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. Data generation model 58 performs inference on the input inference data in accordance with the instructions indicated by the prompt, and outputs the inference results in the form of data such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization.

Another example of generative AI is Gemini (registered trademark) (Internet search <URL: https://gemini.google.com/?hl=ja>).

In the above embodiment, an example was given in which the specific processing was performed by the data processing device 12, but the technology disclosed herein is not limited to this, and the specific processing may also be performed by the smart device 14.

[Second embodiment]

Figure 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

As shown in FIG. 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and/or a LAN (Local Area Network).

The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I/F 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user 20's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

The communication I/F 44 is connected to the network 54. The communication I/Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I/Fs 44 and 26 is carried out in a secure manner.

Figure 4 shows an example of the main functions of the data processing device 12 and smart glasses 214. As shown in Figure 4, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32.

The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

Storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

Next, we will explain the identification process performed by the identification processing unit 290 of the data processing device 12.

"Example 1"

In one embodiment of the present invention, a user first measures their own physique using a 3D body scan. This measurement is performed, for example, using a device that provides a 3D body scan. Next, the user performs a specific movement, such as batting or pitching in baseball. This movement is recorded using a camera or sensor. The recorded movement is analyzed by AI. The AI uses deep learning to compare the user's movement with that of a professional athlete with a similar physique. Then, based on the comparison results, suggestions are made to the user regarding movement modifications. These suggestions are presented to the user, for example, through an application on a device such as a smartphone or tablet.

"Example 2"

In one embodiment of the present invention, a user first measures their own physique using a 3D body scan. This measurement is performed, for example, using a device that provides a 3D body scan. Next, the user performs a specific movement, such as batting or pitching in baseball. This movement is recorded using a camera or sensor. The recorded movement is analyzed by AI. The AI uses deep learning to compare the user's movement with that of a professional athlete with a similar physique. Then, based on the comparison results, suggestions are made to the user regarding movement modifications. These suggestions are presented to the user, for example, through an application on a device such as a smartphone or tablet.

The processing flow for each example form is explained below.

"Example 1"

Step 1: The user measures their physique using a 3D body scan. This measurement is performed, for example, using a device that provides a 3D body scan.

Step 2: Next, the user performs a specific action, such as batting or pitching a baseball. This action is recorded using cameras and sensors.

Step 3: The recorded movements are analyzed by AI, which uses deep learning to compare the user's movements with those of professional athletes with a similar physique.

Step 4: Based on the comparison results, the user is then given suggestions for modifying their behavior. These suggestions are presented to the user, for example, through an application on a device such as a smartphone or tablet.

"Example 2"

Step 1: The user measures their physique using a 3D body scan. This measurement is performed, for example, using a device that provides a 3D body scan.

Step 2: Next, the user performs a specific action, such as batting or pitching a baseball. This action is recorded using cameras and sensors.

Step 3: The recorded movements are analyzed by AI, which uses deep learning to compare the user's movements with those of professional athletes with a similar physique.

Step 4: Based on the comparison results, the user is then given suggestions for modifying their behavior. These suggestions are presented to the user, for example, through an application on a device such as a smartphone or tablet.

(Example 1)

Next, Example 1 of Form Example 1 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

Conventional motion analysis systems have struggled to accurately measure and record a user's physique and movements and provide appropriate feedback. Furthermore, they lacked the means to perform highly accurate analysis when comparing a user's movements with those of professional athletes. This made it difficult for users to effectively improve their own movements.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, means for transmitting recorded movement data and physique data to the server, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement corrections to the user based on the comparison results, and means for displaying the suggested corrections on the user's device. This allows users to accurately measure and record their own physique and movements, and compare them with the movements of professional athletes to obtain specific corrections.

"User" refers to an individual who uses the system to measure and record their physique and movements and receive feedback.

"Physique" refers to a user's physical characteristics and measurements, including data measured by means such as a 3D body scan.

"Movement" refers to specific physical movements or performances performed by the user, which are recorded using cameras and sensors.

"Server" refers to a computer system that receives data sent by users, analyzes it using AI means, and provides the results to users.

"3D body scanning" refers to technology and equipment that measures a user's physique in three dimensions and is used to obtain detailed data about the user's body.

"AI means" refers to means that use artificial intelligence technology to analyze a user's movements and compare them with those of professional athletes.

"Deep learning" is a field of artificial intelligence that uses multi-layer neural networks to learn the characteristics of data and perform advanced analysis and predictions.

"Fixes" refer to specific changes or suggestions suggested to improve user experience.

"Terminal" refers to the device used by the user to access the system, including smartphones and tablets.

"Comparison results" refers to the results of comparing the user's movements with those of a professional athlete using AI means, and includes data that serves as the basis for feedback to the user.

This invention is a system that measures and records a user's physique and movements, analyzes them using AI, and then suggests specific movement modifications to the user. A specific embodiment of this system is described below.

First, the user measures their physique using a device that provides a 3D body scan. Examples of devices used include Kinect and Structure Sensor. This acquires the user's physique data and stores it on the user's device (smartphone or tablet).

Next, the user performs a specific movement, such as a baseball batting or pitching form. This movement is recorded using a smartphone camera or a GoPro. The recorded video data is saved on the user's device.

The user's device sends recorded movement and physique data to the server. An internet connection is required for transmission. The user presses the "Send Data" button in the application, and the device uploads the data to the server.

The server inputs the received movement data and physique data into an AI model. This AI model uses TensorFlow and PyTorch, and uses deep learning to compare the user's movements with those of professional athletes with a similar physique. The server inputs the data into the AI model and begins the comparison process. Once the process is complete, the comparison results are generated.

The server generates corrections for the user's movements based on the comparison results of the AI model. These corrections are compiled into specific advice and improvement methods. For example, specific advice such as "To increase the swing speed of your batting form, you need to rotate your hips more quickly" is generated.

The server sends the generated corrections to the user's device, which then displays the received corrections within the application. The user opens the application and checks the suggested corrections. For example, specific advice such as "How to practice to rotate your hips faster" is displayed.

As a concrete example, consider the case where a user wants to improve their baseball batting form. The user first measures their physique using a 3D body scanning device (such as Kinect). Next, they record their batting form using a smartphone camera. This recorded data is input into a deep learning model using TensorFlow and compared with the movements of professional batters. Based on the comparison results, a dedicated app suggests improvements to the user's batting form.

An example prompt is, "Taking the user's physique data and a video of their batting form as input, compare it with the movements of a professional batter and suggest corrections."

In this way, users can compare their own movements with those of a professional and get specific corrections.

The flow of the identification process in Example 1 will be explained using Figure 11.

Step 1:

Users measure their physique using a 3D body scanning device.

Input: User's body location information

Specific action: The user stands in front of Kinect and rotates their body according to the instructions.

Data processing: The scanning device captures the user's physique data and generates a 3D model.

Output: The generated physique data is saved on the user's device.

Step 2:

The user performs a specific movement (e.g., a baseball batting stance) and records that movement.

Input: User action

Specific operation: The user sets up their smartphone on a tripod and takes a photo of their batting form.

Data processing: The camera records the user's movements as video data.

Output: Recorded video data is saved to the user's device.

Step 3:

The user's device sends recorded movement and physique data to the server.

Input: Movement data and physique data

Specific action: The user presses the "Send Data" button within the application.

Data processing: The device compresses the data and uploads it to a server via the Internet.

Output: Data is sent to the server.

Step 4:

The server inputs the received movement data and physique data into the AI model.

Input: Movement data and physique data

Specific operation: The server inputs the data into the AI model and begins the comparison process.

Data calculation: The AI model uses deep learning to compare the user's movements with those of professional athletes with a similar physique.

Output: The comparison results are generated.

Step 5:

The server generates corrections to the user's behavior based on the comparison results of the AI model.

Input: Comparison results

Specific actions: The server generates specific advice such as, "To increase your swing speed in your batting form, you need to rotate your hips faster."

Data processing: Analyze the comparison results and generate corrections in text format.

Output: Corrections are generated.

Step 6:

The server sends the generated modifications to the user's device.

Input: Corrections

Specific operation: The server sends the modifications to the user's device.

Data processing: Convert the corrections into the appropriate format and send.

Output: The changes are sent to the user's device.

Step 7:

The user's device will display the received corrections within the application.

Input: Corrections

What happens: The user opens the application and sees the suggested fixes.

Data processing: Display corrections in a format that is easy for users to understand.

Output: The user can see the changes.

(Application Example 1)

Next, we will explain Application Example 1 of Form Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

Traditional factory work lacks specific feedback to improve the efficiency and safety of workers' movements. This leads to problems such as reduced work efficiency and increased physical strain if workers continue to perform inappropriate movements. Furthermore, the lack of a system to optimize workers' movements makes it difficult to provide appropriate guidance to individual workers.

The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes a means for measuring the user's physique, a means for recording specific movements performed by the user, an AI means for analyzing the recorded movements and comparing them with the movements of workers with a similar physique, a means for suggesting to the user movement modifications based on the comparison results, and a means for displaying suggestions for optimizing the movements of factory workers. This makes it possible to provide specific feedback to improve the efficiency and safety of workers' movements.

"User" refers to an individual who uses the system to have their physique and movements measured and analyzed.

"Physique" refers to the user's physical characteristics and measurements.

"Movement" refers to a specific physical movement or task performed by the user.

"Recording means" refers to devices or methods for saving user actions as video or data.

"AI means" refers to technology that uses artificial intelligence to analyze recorded movements and compare them with other movements.

"Deep learning" is a type of artificial intelligence that uses multi-layered neural networks to analyze and learn from data.

"Means for suggesting modifications" refers to a method or device for suggesting improvements to the user's behavior based on the analysis results.

A "factory worker" is a worker who performs specific tasks within a factory.

"Means for displaying optimization suggestions" refers to a device or method for visually presenting suggestions to the user for improving operational efficiency and safety.

"3D body scanning" is a technology for measuring a user's physique in three dimensions.

A system for implementing this invention includes means for measuring a user's physique, means for recording specific movements performed by the user, AI means for analyzing the recorded movements and comparing them with the movements of workers with a similar physique, means for suggesting to the user corrections to their movements based on the comparison results, and means for displaying suggestions for optimizing the movements of factory workers.

Hardware and software used

3D body scanning device: A device for measuring the user's physique in three dimensions.

Camera: A device used to record user actions. An example is the Logitech C920.

Smart glasses: A device that visually shows users how to correct their movements. An example is Google Glass.

Tablet: A device used to display operational corrections and optimization suggestions. An example is the iPad.

Server: A computer system for processing data and running AI models.

Software: Use programming libraries such as Python, OpenCV, and Keras.

Data processing and calculation

1. Acquisition of physique data: The server acquires the user's physique data using a 3D body scanning device. This data represents the user's physical characteristics and dimensions in three dimensions.

2. Recording of actions: The server uses a camera to record the user's actions. The recorded actions are saved as image data.

3. Movement analysis: The server inputs the recorded image data and physique data into the AI model and analyzes the movements. The AI model uses deep learning to compare the user's movements with those of workers with a similar physique.

4. Suggested modifications: The server will suggest modifications to the user's behavior based on the analysis results. These suggestions will be displayed on the smart glasses or tablet.

Specific examples

When a factory worker lifts a heavy object, their movements are recorded using a 3D body scan and camera, and then analyzed by AI. As a result of the analysis, suggestions are made for corrections such as the angle of the waist and the position of the hands when lifting. This allows workers to work efficiently and safely.

Example prompt

"Record factory workers lifting heavy objects, input this data into the AI along with 3D body scan data, compare it with efficient movements, and suggest corrections."

The above is a form for implementing this invention.

The flow of the specific processing in Application Example 1 will be explained using Figure 12.

Step 1:

The server uses a 3D body scanning device to obtain the user's physique data. The input includes the user's physical characteristics and dimensions. Data processing involves converting these characteristics and dimensions into a data format that represents them in three dimensions. The output is the user's physique data.

Step 2:

The server uses a camera to record the user's actions. The input includes the specific actions performed by the user. The data is processed by saving the actions as video data. The output is image data of the recorded actions.

Step 3:

The server inputs the recorded image data and physique data into the AI model and analyzes the movements. The input includes image data and physique data. To calculate the data, the AI model uses deep learning to compare the user's movements with those of workers with a similar physique. The output is the movement analysis results.

Step 4:

The server suggests behavioral modifications to the user based on the analysis results. The input includes the behavioral analysis results. Data processing generates specific suggestions for modifications. The output is the suggested modifications.

Step 5:

The server displays suggested corrections on the smart glasses or tablet. The input includes suggested corrections. Specific operations include generating data to visually display the suggestions and sending it to the smart glasses or tablet. The output displays suggested corrections that the user can visually confirm.

The above is the processing flow of this program.

(Example 2)

Next, we will explain Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

Conventional motion analysis systems have struggled to accurately measure and record a user's physique and movements and suggest appropriate corrections. Furthermore, there was a lack of means to provide specific corrections to users, which led to problems with effective motion improvement.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, artificial intelligence means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, and means for presenting the modifications to the user. This makes it possible to accurately measure and record the user's physique and movements, suggest appropriate modifications, and present specific modifications to the user.

"User" refers to an individual who uses the system to measure and record their physique and movements and receive suggested movement modifications.

"Means for measuring body size" refers to devices or technologies that measure the dimensions and shape of a user's body in three dimensions.

"Means for recording specific actions" refers to devices or technologies that use cameras or sensors to record actions performed by users.

"Artificial intelligence means" refers to AI technology used to analyze recorded movement data and compare the user's movements with those of professional athletes.

"Means for suggesting corrections" refers to technology that extracts and suggests areas for improvement in the user's behavior based on the analysis results of artificial intelligence means.

"Means for presenting corrections" refers to a device or application that visually or audibly communicates suggested corrections to the user.

"3D body scanning" refers to technology that measures a user's physique in three dimensions.

"Deep learning" refers to a machine learning technique using multiple layers of neural networks used in artificial intelligence methods.

Form for implementing the invention

This invention relates to a system that measures a user's physique, records and analyzes specific movements, and suggests modifications to those movements. A specific embodiment of this system is described below.

User body measurements

Users measure their physique using a 3D body scanning device. Examples of such devices include a 3D scanner or motion capture system. Users follow the device's instructions and rotate 360 degrees to scan their physique. The device processes the scan data in real time and generates physique data for the user. The generated data is then sent to a server via wireless communication.

Recording actions

Next, the user performs a specific movement, such as batting or pitching in baseball. The user's movement is recorded using a camera or sensor. The hardware used may include a high-resolution camera or a motion capture system. The device generates the recorded video data or motion capture data and sends it to a server.

Analysis of movement

The server inputs the received physique data and movement data into an artificial intelligence model. This artificial intelligence model uses deep learning to compare the user's movements with those of professional athletes. The software used is a "deep learning framework." The server extracts corrections for the user's movements from the output of the artificial intelligence model.

Suggested behavior fixes

The server sends the extracted correction points to the device. The device then displays the received correction points in a dedicated application. The user can check the correction points through the application and learn specific ways to improve. For example, specific instructions such as "increase the angle at the start of the bat swing by 10 degrees" are displayed.

Specific examples

Example 1: Correcting baseball batting form

The user measures their physique using a 3D body scanning device and sends the data to a server. Then, a high-resolution camera is used to capture their batting form, and the video data is uploaded to the server. The server then analyzes the data using a deep learning framework and identifies areas for improvement. These areas are presented to the user via a dedicated application. The user then works to improve their batting form while viewing the application.

Example 2: Correcting pitching form

The user measures their physique using a 3D scanner and sends the data to the server. They then use a motion capture system to record their pitching form and upload the data to the server. The server then analyzes the data using a deep learning framework and extracts corrections that are presented to the user through a dedicated application. The user works to improve their pitching form while looking at the application.

Example prompt

Prompt 1: Baseball batting form

"Using the user's 3D body scan data and video data of their batting form as input, please generate an AI model that compares it with data from professional baseball players and suggests corrections."

Prompt 2: Pitching form

"Using the user's 3D body scan data and motion capture data of their pitching form as input, please compare it with data from professional baseball players and generate an AI model that suggests corrections."

The flow of the identification process in Example 2 will be explained using Figure 13.

Program processing flow

Step 1: Measure the user's body size

The user activates the 3D body scanning device. Following the device's instructions, the user rotates 360 degrees to scan their physique. The device processes the scan data in real time and generates the user's physique data. The generated data is sent to the server via wireless communication.

Input: User's physique scan data

Data processing: Real-time processing of scanned data

Output: User's physique data

Step 2: Record the action

Using a high-resolution camera and motion capture system, the user performs a specific movement, such as batting or pitching in baseball. The device records the movement and generates video data or motion capture data, which is then sent to the server.

Input: User action

Data processing: Recording movements and generating data

Output: Video data or motion capture data

Step 3: Analyze the behavior

The server inputs the received physique data and movement data into an artificial intelligence model. This artificial intelligence model uses deep learning to compare the user's movements with those of professional athletes. The server extracts corrections for the user's movements from the output of the artificial intelligence model.

Input: physique data, movement data

Data processing: Deep learning behavior comparison

Output: Behavior corrections

Step 4: Propose behavioral modifications

The server sends the extracted correction points to the device. The device then displays the received correction points in a dedicated application. The user can check the correction points through the application and learn specific ways to improve. For example, specific instructions such as "increase the angle at the start of the bat swing by 10 degrees" are displayed.

Input: Behavior corrections

Data processing: Preparing for corrections

Output: Present the corrections to the user

Add specific actions

Step 1: Measure the user's body size

The user activates the 3D body scanning device and follows the device's instructions to scan their physique. The scan data is processed in real time to generate the user's physique data, which is then transmitted to the server via wireless communication.

Step 2: Record the action

The user installs a high-resolution camera and films their baseball batting form. The camera records high-resolution video and saves it on the device. The device then compresses the saved video data and uploads it to the server.

Step 3: Analyze the Movement

The server inputs the received physique data and video data into a deep learning framework. The AI model compares the user's batting form with that of professional baseball players. Features such as joint angles and movement speed are used for the comparison. The server extracts corrections for the user's movement from the output of the AI model.

Step 4: Propose behavioral modifications

The server sends the extracted corrections to the device in JSON format. The device then displays the received corrections in a dedicated application. The user can review the corrections through the application and learn specific ways to improve. For example, specific instructions such as "increase the angle at the start of your bat swing by 10 degrees" are displayed.

(Application Example 2)

Next, we will explain Application Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

Traditional optimization of factory robots relies on manual adjustments and experience, making it difficult to achieve efficient and accurate optimization. Furthermore, because individual optimization based on the user's physique and movements is not performed, general-purpose optimization methods often fail to produce sufficient results. This poses a risk of reduced factory productivity and quality.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, means for recording and analyzing the movements of robots used in factories, and means for suggesting modifications to optimize the robot's movement. This enables individual optimization based on the user's physique and movements, making it possible to efficiently and accurately optimize the movement of factory robots.

"User" means an individual or corporation that uses the system to measure and analyze their physique and movements.

"Physique" refers to the shape and dimensions of the user's body, as measured by means such as a 3D body scan.

"Specific actions" refer to specific movements or tasks performed by the user, which are recorded using cameras or sensors.

"Recording means" refers to devices or methods that use cameras, sensors, etc. to save user actions as data.

"AI means" refers to technology that uses artificial intelligence to analyze recorded movement data and compare the user's movements with those of professional athletes.

"Deep learning" is a type of artificial intelligence that uses multi-layered neural networks to analyze and learn from data.

A "professional athlete" is an athlete who specializes in a particular sport and is the subject to be compared with the user's movements.

"Behavior Modifications" are specific improvements suggested to optimize the user's behavior.

"Robots used in factories" are automated machinery and devices used on factory production lines and in workshops.

"Optimization" means adjusting something to the most efficient and effective state for a specific purpose.

The following hardware and software are used to implement this invention. Hardware requires a 3D scanner, camera, sensor, server, and user terminal (smartphone or tablet). Software uses Python, OpenCV, Keras, etc.

First, the user measures their physique using a 3D scanner. The 3D scanner captures the user's body shape and dimensions with high precision and saves them as digital data. This data is then sent to a server.

Next, the user performs a specific action. For example, if they are imitating the actions of a robot used in a factory, the action is recorded using a camera or sensor. The recorded action data is also sent to the server.

The server inputs the received 3D scan data and movement data into the AI means. The AI means analyzes this data using deep learning and compares the user's movements with those of professional athletes with a similar physique. Based on the comparison results, it suggests movement modifications to the user.

Furthermore, to optimize the operation of robots used in factories, robot operation data is also recorded and analyzed using AI methods. Based on the analysis results, corrections are suggested to optimize the robot's operation. These suggestions are displayed on the user's device.

As a concrete example, consider optimizing the operation of a welding robot used in a factory. A 3D scanner captures the robot's shape data, and cameras and sensors record its operation data. This data is input into an AI tool, and suggestions for adjustments to the welding angle and speed are made based on the analysis results.

An example of a prompt to input into the generative AI model is, "Please input the welding robot's motion data along with the 3D scan data, and suggest optimal motion corrections."

As described above, this invention enables individual optimization based on the user's physique and movements, allowing the operation of factory robots to be optimized efficiently and with high precision.

The flow of the specific processing in Application Example 2 will be explained using Figure 14.

Step 1:

Users measure their body size using a 3D scanner.

Input: User's body

Data processing: A 3D scanner captures the user's body shape and dimensions with high precision and stores them as digital data.

Output: 3D scan data

Step 2:

The user performs a specific action, which is recorded using cameras and sensors.

Input: User action

Data processing: Cameras and sensors record user movements as video and sensor data.

Output: Operation data

Step 3:

The server receives the 3D scan data and movement data.

Input: 3D scan data, motion data

Data processing: The server receives this data and prepares it for input into the AI means.

Output: Prepared data

Step 4:

The server analyzes the 3D scan data and movement data using AI tools.

Input: Prepared data

Data processing: Using a deep learning model, we compare the user's movements with those of professional athletes with a similar physique.

Output: Analysis results

Step 5:

The server then suggests behavioral modifications to the user based on the analysis results.

Input: Analysis results

Data processing: Based on the analysis results, specific modifications are generated to optimize user behavior.

Output: Suggested fixes

Step 6:

Suggested fixes will be displayed on the user's device.

Enter: Suggested fix

Data processing: Convert the proposal content into a format for display on the user's device.

Output: Corrections displayed on the user's device

Step 7:

The movements of robots used in factories are recorded and sent to a server.

Input: Robot movement

Data processing: Cameras and sensors record the robot's movements as video and sensor data, which are then sent to a server.

Output: Robot movement data

Step 8:

The server analyzes the robot's operation data using AI means and suggests modifications for optimization.

Input: Robot movement data

Data processing: Using deep learning models, we analyze the robot's behavior and generate corrections for optimization.

Output: Suggested modifications to the robot's behavior

Step 9:

Suggestions for modifying the robot's behavior will be displayed on the user's device.

Input: Suggested modifications to the robot's behavior

Data processing: Convert the proposal content into a format for display on the user's device.

Output: Robot behavior corrections displayed on the user's device.

It is also possible to further combine an emotion engine that estimates the user's emotion. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

"Example 1"

In one embodiment of the present invention, a system is provided that includes a means for measuring a user's physique, a means for recording specific movements performed by the user, an AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, a means for suggesting movement modifications to the user based on the comparison results, and an emotion engine that recognizes the user's emotions. The emotion engine recognizes emotions from the user's facial expressions and tone of voice and provides this information to the AI means. The AI means suggests movement modifications that take into account not only the comparison between the user's movements and the movements of the professional athletes, but also the user's emotions. For example, if the user is feeling frustrated, the AI means takes this information into consideration and suggests modifications in a manner that is easy for the user to accept. In this way, it is possible to suggest movement improvements that take the user's emotions into account.

"Example 2"

In one embodiment of the present invention, a system is provided that includes a means for measuring a user's physique, a means for recording specific movements performed by the user, an AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, a means for suggesting movement modifications to the user based on the comparison results, and an emotion engine that recognizes the user's emotions. The emotion engine recognizes emotions from the user's facial expressions and tone of voice and provides this information to the AI means. The AI means suggests movement modifications that take into account not only the comparison between the user's movements and the movements of the professional athletes, but also the user's emotions. For example, if the user is feeling frustrated, the AI means takes this information into consideration and suggests modifications in a manner that is easy for the user to accept. In this way, it is possible to suggest movement improvements that take the user's emotions into account.

The processing flow for each example form is explained below.

"Example 1"

Step 1: Measure the user's physique using a 3D body scan.

Step 2: Record a specific action performed by the user (e.g., batting or pitching a baseball) using a video camera or motion sensor.

Step 3: The recorded movements are analyzed using AI tools and compared with the movements of professional athletes with similar physiques.

Step 4: Understand the user's emotions using an emotion engine that recognizes emotions from the user's facial expressions and tone of voice.

Step 5: The AI means suggests corrections to the user's movements, taking into account the results of comparing the user's movements with those of professional athletes and the user's emotions.

Step 6: The proposed behavioral modifications are presented to the user via an application on a device such as a smartphone or tablet.

"Example 2"

Step 1: Measure the user's physique using a 3D body scan.

Step 2: Record a specific action performed by the user (e.g., batting or pitching a baseball) using a video camera or motion sensor.

Step 3: The recorded movements are analyzed using AI tools and compared with the movements of professional athletes with similar physiques.

Step 4: Understand the user's emotions using an emotion engine that recognizes emotions from the user's facial expressions and tone of voice.

Step 5: The AI means suggests corrections to the user's movements, taking into account the results of comparing the user's movements with those of professional athletes and the user's emotions.

Step 6: The proposed behavioral modifications are presented to the user via an application on a device such as a smartphone or tablet.

(Example 1)

Next, Example 1 of Form Example 1 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

Traditional movement improvement systems make suggestions based on the user's physique and movement data, but because they don't take the user's emotions into account, users may find it difficult to accept the suggestions. Furthermore, since the corrections to movement are not specific, it is difficult for users to understand how to improve.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, artificial intelligence means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, means for recognizing the user's emotions, and means for suggesting movement modifications taking into account the emotion data. This makes it possible to suggest movement improvements that take into account the user's physique and emotions.

"Means for measuring the user's physique" refers to devices or methods that measure the user's physical characteristics and acquire them as digital data.

"Means for recording specific actions performed by a user" refers to a device or method that uses video or sensors to record specific actions performed by a user as digital data.

"Artificial intelligence means" refers to software or systems that analyze recorded data and use specific algorithms to compare the user's movements with other data (e.g., the movements of professional athletes).

"Means for suggesting movement corrections" refers to devices or methods that suggest movement improvements and correction methods to the user based on the analysis results.

"Means for recognizing user emotions" refers to devices or methods that analyze the user's emotions from facial expressions, tone of voice, etc., and acquire them as digital data.

"Means for suggesting movement modifications taking into account emotional data" refers to a device or method that suggests movement modifications in a more acceptable form based on the user's emotional data.

"3D body scanning" is a technology that scans the user's body in three dimensions and obtains detailed physique data.

"Deep learning" is an artificial intelligence technology that uses multi-layered neural networks to analyze data and learn patterns and features.

This invention is a system that analyzes a user's physique and movements, compares them with the movements of professional athletes, and suggests movement modifications to the user. Furthermore, by recognizing the user's emotions and making suggestions that take their emotional data into consideration, it is possible to present movement modifications in a way that is easy for the user to accept.

Hardware and software used

1. User body measurements

Users measure their physique using equipment that provides a 3D body scan. This equipment may include, for example, a 3D scanner or a dedicated body scan application. This obtains detailed physique data such as the user's height, weight, and body fat percentage.

2. Recording actions

Users use their smartphone camera or a dedicated motion capture device to record specific movements (e.g., batting or pitching in baseball). The recorded data includes detailed movement information such as swing speed and angle.

3. Sending operation data

The device transmits recorded movement and physique data to a server. This transmission occurs in real time over the Internet, and the data is encrypted using a secure communication protocol.

4. Movement Analysis

The server inputs the received data into an AI model, which uses deep learning algorithms to compare the user's movements with those of professional athletes. The software used is a deep learning framework such as TensorFlow or PyTorch. For example, it calculates how much the user's swing speed differs from the average speed of professional athletes.

5. Emotional Recognition

The device uses an emotion engine that analyzes the user's facial expressions and tone of voice in real time. This emotion engine determines whether the user is frustrated or satisfied and sends that information to a server. The software used is an emotion recognition API (e.g., Microsoft Azure's Emotion API).

6. Suggestions for behavioral modifications

Based on the comparison of the movements and the emotional data, the server suggests to the user how to modify their movements. For example, if the user is feeling frustrated, the server might suggest, "Maybe if you just change the way you grip the bat a little, your swing will become smoother." The user is notified of these suggestions through an application on their device, such as a smartphone or tablet.

Specific examples

Example 1: Improving baseball batting form

The user measures their physique using a 3D scanner and records their batting form with their smartphone camera. The device then sends the recorded data to a server, which then inputs the data into an AI model and compares it with the form of professional baseball players. If the emotion engine detects the user's frustration, the server will make suggestions such as, "Maybe if you just change the way you grip the bat a little, your swing will become smoother." The user is notified of these suggestions via a smartphone application.

Example prompt for a generative AI model

"Using the user's 3D body scan data and video of their batting form as input, please compare it with the form of a professional baseball player and suggest improvements while taking into account the user's emotions."

In this way, it becomes possible to suggest movement improvements that take into account the user's physique and emotions.

The flow of the identification process in Example 1 will be explained using Figure 15.

Step 1:

Users measure their physique using a device that provides a 3D body scan. As input, a 3D scanner is used to scan the user's body. As output, detailed physique data such as the user's height, weight, and body fat percentage is generated. This data is stored on the device.

Step 2:

Users use their smartphone's camera or a dedicated motion capture device to record specific movements (e.g., batting or pitching in baseball). As input, the user's movements are captured by the camera or sensors. As output, detailed movement data such as swing speed and angle is generated. This data is stored on the device.

Step 3:

The device sends the recorded movement data and physique data to the server. The movement data and physique data stored on the device are used as input. As output, this data is sent to the server via the internet. The data is encrypted and uses a secure communication protocol.

Step 4:

The server inputs the received data into the AI model. The movement data and physique data sent to the server are used as input. The output is the results of the AI model comparing the user's movements with those of professional athletes. This AI model uses deep learning algorithms and utilizes deep learning frameworks such as TensorFlow and PyTorch.

Step 5:

The device uses an emotion engine that analyzes the user's facial expressions and tone of voice in real time. As input, the user's facial and voice data is captured through a camera and microphone. As output, emotional data is generated, such as whether the user is frustrated or satisfied. This data is sent to a server.

Step 6:

The server suggests movement corrections to the user based on the movement comparison results and emotional data. The AI model's comparison results and emotional data are used as input. The output is movement corrections generated for the user. For example, if the user is feeling frustrated, the server might suggest, "Maybe your swing will become smoother if you just change the way you grip the bat slightly." The suggestion is notified to the user through an application on their device, such as a smartphone or tablet.

(Application Example 1)

Next, we will explain Application Example 1 of Form Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

While conventional motion analysis systems can analyze a user's physique and movements, they lack the ability to suggest motion modifications that take the user's emotions into account. Furthermore, when optimizing the movements of robots working in factories, they lack the ability to monitor movement efficiency and error rates in real time and provide improvement suggestions. This leads to issues such as reduced movement efficiency for users and robots, preventing optimal performance.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, an emotion engine for recognizing the user's emotions, means for suggesting movement modifications taking into account information from the emotion engine, means for optimizing the robot's movements, means for monitoring the robot's movement efficiency and error rate, and means for making improvement suggestions in real time. This makes it possible to suggest movement modifications taking the user's emotions into account, and further enables the movement efficiency and error rate of robots working in factories to be monitored in real time, enabling optimal movement performance to be achieved.

"Means for measuring the user's physique" refers to devices or technologies for accurately measuring the dimensions and shape of the user's body.

"Means for recording specific actions performed by a user" refers to devices or technologies that use cameras, sensors, etc. to record specific actions performed by a user.

"AI means for analyzing recorded movements and comparing them with the movements of professional athletes with similar physiques" refers to artificial intelligence technology that analyzes recorded user movement data and compares it with the movement data of professional athletes with similar physiques.

"Means for suggesting to the user points for correction of movements based on the comparison results" refers to devices or technologies that suggest points for improvement or correction of movements to the user based on the results of movement comparison by AI.

The "emotion engine that recognizes user emotions" is a technology that identifies emotions from the user's facial expressions, tone of voice, etc.

"Means for suggesting behavioral modifications by taking into account information from an emotion engine" refers to devices or technologies that suggest behavioral modifications based on the user's emotional information recognized by an emotion engine.

"Means for optimizing robot operation" refers to devices and technologies that optimize the operation of robots working in factories to ensure that they are efficient and effective.

"Means for monitoring a robot's operational efficiency and error rate" refers to devices and technologies that monitor a robot's operational efficiency and error rate in real time.

"Means for making improvement suggestions in real time" refers to devices and technologies that analyze robot operation data in real time and immediately suggest areas for improvement.

A system for implementing this invention includes means for measuring a user's physique, means for recording specific movements performed by the user, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting to the user movements modifications based on the comparison results, an emotion engine for recognizing the user's emotions, means for suggesting movements modifications taking into account information from the emotion engine, means for optimizing the robot's movements, means for monitoring the robot's movement efficiency and error rate, and means for making improvement suggestions in real time.

Hardware and software configuration

Hardware:

3D body scanning device

Camera (e.g., Logitech C920)

Computer (e.g., Intel Core i7, 16GB RAM)

Software:

OpenCV: Motion recording and image processing

Keras: Loading and predicting deep learning models

EmotionRecognizer: Emotion Recognition Library

Processing flow

The server first acquires the user's physique data using a 3D body scanning device. Then, it uses a camera to record specific movements performed by the user. The recorded movement data is preprocessed using OpenCV and analyzed using a deep learning model using Keras. The analysis results are compared with the movement data of professional athletes with similar physiques.

Furthermore, EmotionRecognizer is used to recognize the user's emotions and suggests behavioral modifications taking into account information from the emotion engine. The suggested modifications are presented to the user.

For robots working in factories, measures are used to optimize the robot's operations. The robot's operating efficiency and error rate are monitored in real time, and suggestions for improvement are made. This makes it possible to optimize the robot's operating performance.

Specific examples

For example, if a robot assembling parts in a factory is moving slowly, the AI will analyze its movements and suggest correcting the angle and speed of its movements. Furthermore, if the robot is making frequent errors, it will determine through emotion recognition that it is feeling frustrated and suggest that it move more slowly.

Example prompt

"To optimize the movements of robots that assemble parts in factories, design a system that uses 3D body scans and cameras to record their movements, analyzes them with AI, and suggests optimal movement patterns. Additionally, add functionality to monitor the robot's movement efficiency and error rate and suggest improvements in real time."

The flow of the specific processing in Application Example 1 will be explained using Figure 16.

Step 1:

The server acquires the user's physique data using a 3D body scanning device. As input, it scans the user's body dimensions and shape, and generates 3D physique data as output. This data is used to accurately determine the user's physique.

Step 2:

The server uses a camera to record specific actions performed by the user. As input, it captures the actions performed by the user in real time, and as output, it generates video data of the actions. This video data is used for subsequent action analysis.

Step 3:

The server preprocesses the recorded motion data using OpenCV. It receives motion video data as input and generates preprocessed image data as output. This preprocessing includes image resizing and noise removal.

Step 4:

The server analyzes the preprocessed image data using a deep learning model using Keras. It receives the preprocessed image data as input and extracts movement features as output. These features are used to analyze the user's movements in detail.

Step 5:

The server compares the extracted features with the motion data of professional athletes with a similar physique. It receives the user's motion features and the motion data of the professional athletes as input, and generates the comparison results as output. These comparison results are used to identify areas in the user's motion that need to be corrected.

Step 6:

The server uses EmotionRecognizer to recognize the user's emotions. As input, it captures the user's facial expressions and tone of voice while in action, and generates emotional data as output. This emotional data is used to understand the user's emotional state.

Step 7:

The server takes into account information from the emotion engine to suggest behavioral modifications. It receives the comparison results and emotion data as input, and generates behavioral modifications for the user as output. These modifications are suggested taking into account the user's emotional state.

Step 8:

The server executes a process to optimize the operation of robots working in factories. It receives robot operation data as input and generates optimized operation patterns as output. This optimization is performed to improve the robot's operation efficiency.

Step 9:

The server monitors the robot's operating efficiency and error rate in real time. It receives the robot's operating data in real time as input and generates monitoring results of operating efficiency and error rate as output. These monitoring results are used to understand the robot's operating status.

Step 10:

The server makes improvement suggestions in real time. It receives monitoring results as input and generates improvement suggestions as output. These improvement suggestions are made to optimize the robot's operational performance.

(Example 2)

Next, we will explain Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

Previous movement improvement systems struggled to accurately measure a user's physique and movements and provide appropriate feedback. Furthermore, they didn't take the user's emotions into account when proposing movement improvements, resulting in a lack of feedback that was easy for the user to accept. This limited the effectiveness of the system's movement improvements.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, means for transmitting the recorded movements, artificial intelligence means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, means for recognizing the user's emotions, and means for suggesting movement modifications taking the recognized emotions into consideration. This makes it possible to accurately measure the user's physique and movements and provide appropriate feedback. Furthermore, because suggestions for movement improvement are made taking the user's emotions into consideration, feedback is provided in a form that makes the suggestions more receptive to the user, improving the effectiveness of movement improvement.

"Means for measuring the user's physique" refers to devices or technologies that measure the dimensions and shape of the user's body in three dimensions.

"Means for recording specific actions performed by a user" refers to devices or technologies that use video or sensors to record specific actions performed by a user.

"Means for transmitting recorded behavior" refers to communication means for transmitting recorded behavior data to a server or other device.

"Artificial intelligence means" refers to artificial intelligence technology used to analyze recorded movement data and compare the user's movements with those of professional athletes.

"Means for suggesting movement corrections" refers to devices or technologies that suggest movement improvements to the user based on the analysis results of artificial intelligence means.

"Means for recognizing user emotions" refers to devices or technologies for recognizing emotions from the user's facial expressions, tone of voice, etc.

"Means for suggesting behavioral modifications taking into account recognized emotions" refers to devices or technologies that suggest behavioral modifications in a way that is easy for the user to accept, based on the user's emotional information.

This invention is a system that measures a user's physique, records specific movements, analyzes those movements, and suggests corrections. The system can recognize the user's emotions and take them into consideration when suggesting corrections to movements.

First, the user measures their physique using a 3D body scanning device. Specifically, devices such as the "Structure Sensor" or "Kinect" are used. This allows the user's physique data to be acquired and stored on the device.

Next, the user performs a specific movement (e.g., batting or pitching a baseball). This movement is recorded using a camera or sensor (e.g., a GoPro camera or a Vicon motion capture system). The recorded movement data is saved on the device.

The device sends recorded movement and physique data to a server. The data is sent using a secure protocol (e.g., HTTPS).

The server inputs the received data into an AI model, which is built using a deep learning framework (e.g., TensorFlow or PyTorch) and compares the user's movements with those of professional athletes with a similar physique. The server generates comparison results and identifies movement modifications.

The server sends the identified corrections to the device, which then presents the corrections to the user through an application on the device, such as a smartphone or tablet. For example, specific advice such as "You should increase the angle of your bat swing a little more" is displayed.

In addition, the device inputs the user's facial expressions and tone of voice into an emotion engine. The emotion engine uses Affectiva and the Microsoft Azure Emotion API to recognize the user's emotions in real time. The recognition results are stored on the device.

The device sends the recognized emotional information to the server. The server reevaluates the AI model taking the emotional information into account and suggests modifications that the user can easily accept. For example, if the user feels frustrated, the server will generate a suggestion such as "First, relax, and then try to be aware of your release points," and send it to the device. The device will then display this suggestion to the user.

Specific examples

Example 1: Improving baseball batting form

The user uses the "Structure Sensor" to measure their physique and a GoPro camera to record their batting form. The device then sends this data to a server. The server uses TensorFlow to compare the batting form with that of professional baseball players and suggests, via a smartphone app, that the user "increase the angle of the bat's swing a little more." If the user is feeling frustrated, the emotion engine recognizes this and the server suggests, "First, relax, and then try to be more conscious of your swing angle."

Example 2: Improving pitching form

The user uses Kinect to measure their physique and a Vicon motion capture system to record their pitching form. The device then sends this data to a server. The server uses PyTorch to compare the form with that of professional pitchers and suggests, via a tablet app, that the release point should be moved a little further forward. If the user is feeling frustrated, the emotion engine recognizes this and the server suggests, "First, relax, and then try to focus on the release point."

Example prompt

"The user uses a 3D body scanning device to measure their physique and a camera to record their batting form. The AI uses TensorFlow to compare their form with that of professional baseball players and suggests corrections via a smartphone app. If the user is feeling frustrated, the emotion engine recognizes this and the AI suggests corrections in a way that is easy for the user to accept."

The flow of the identification process in Example 2 will be explained using Figure 17.

Step 1:

Users measure their own physique using a 3D body scanning device. Specifically, users acquire physique data using a "Structure Sensor" or "Kinect." The input is the dimensions and shape of the user's body, and the output is physique data as a 3D model. This data is stored on the device.

Step 2:

The user performs a specific movement (e.g., batting or pitching a baseball). The user records the movement using a GoPro camera or a Vicon motion capture system. The input is the user's movement, and the output is high-resolution video data. This data is stored on the device.

Step 3:

The device sends recorded movement data and physique data to the server. The input is movement data and physique data, and the output is data sent to the server. Data is sent using a secure protocol (e.g., HTTPS).

Step 4:

The server inputs the received data into an AI model. The AI model is built using TensorFlow and PyTorch and compares the user's movements with those of professional athletes with a similar physique. The input is movement data and physique data, and the output is the comparison results, including suggestions for movement correction.

Step 5:

The server sends the identified corrections to the terminal. The input is the comparison result, and the output is the corrections sent to the terminal. The terminal then presents the corrections to the user through an application on a device such as a smartphone or tablet. For example, specific advice such as "You should increase the angle of your bat swing a little more" is displayed.

Step 6:

The device inputs the user's facial expressions and tone of voice into the emotion engine. The emotion engine uses "Affectiva" and "Microsoft Azure Emotion API" to recognize the user's emotions in real time. The input is the user's facial expressions and tone of voice, and the output is recognized emotional information. This information is stored on the device.

Step 7:

The device sends the recognized emotional information to the server. The input is emotional information, and the output is the transmission of emotional information to the server. The server reevaluates the AI model taking the emotional information into account and suggests corrections that are easy for the user to accept. For example, if the user is feeling frustrated, the server will generate a suggestion such as "First, relax, and then try to be aware of your release point," and send it to the device. The device will display this suggestion to the user.

(Application Example 2)

Next, we will explain Application Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

Conventional motion analysis systems record a user's physique and movements, compare them with those of professional athletes, and suggest corrections, but they were not designed to optimize the movements of robots working in factories. They also lacked the ability to detect abnormalities in the robots and suggest corrections to their movements based on that information. This made it difficult to efficiently manage the movements of robots in factories.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, means for recording the robot's movements and proposing optimal movement patterns, and means for detecting abnormalities in the robot and proposing movement modifications based on that information. This not only improves the user's movements, but also enables efficient movement management and optimization of robots within factories.

"User" refers to an individual who uses the system to measure and analyze their physique and movements.

"Physique" refers to the user's physical characteristics and measurements.

"Specific movements" refers to specific actions or movements performed by the user (e.g., batting or pitching form in baseball).

"Recording means" refers to devices or methods that use cameras, sensors, etc. to save the actions of users or robots as digital data.

"AI means" refers to a system that uses artificial intelligence to analyze data and output results according to a specific purpose.

"Deep learning" refers to a machine learning technique that uses multi-layered neural networks to learn data and extract complex patterns and features.

A "professional athlete" refers to an athlete who has high skills in a particular sport and engages in the sport professionally.

"Behavior modifications" refers to specific changes or adjustments proposed to improve the user's or robot's behavior.

"Robot" refers to an automated mechanical device that performs work in a factory.

"Abnormal" refers to conditions such as vibrations or temperature rises that deviate from the robot's normal operation.

"Means of sensing" refers to methods or devices that use sensors or monitoring devices to detect abnormalities.

"Optimal movement pattern" refers to a series of steps or methods of robot movement proposed to perform a task efficiently and effectively.

We will now explain the mode for implementing this invention. First, a 3D body scanning device is used to measure the user's physique. Specifically, a 3D scanner such as Intel RealSense is used to acquire the user's physique data. This data is used to accurately record the user's physical characteristics and dimensions.

Next, cameras and sensors are used to record specific actions performed by the user. For example, an Intel RealSense camera is used to record user actions with high accuracy. This recorded data is then analyzed using AI tools, which will be described later.

The recorded movement data is analyzed using AI tools that use deep learning, such as TensorFlow and Keras. The AI tools compare the user's movements with those of professional athletes with a similar physique, and suggest movement corrections. These suggestions are presented to the user via an application on their smartphone or tablet.

Furthermore, in order to optimize the movements of robots working in factories, it includes a means for recording the movements of the robots and proposing optimal movement patterns. The movements of the robots are recorded using a 3D scanner or camera and analyzed by the AI means. The AI means proposes optimal movement patterns and makes the robots' movements more efficient.

In addition, vibration and temperature sensors are used to detect abnormalities in the robot. OpenCV and PyTorch are used to detect abnormalities and suggest corrections to behavior based on that information. This optimizes the robot's behavior and enables it to work more efficiently.

For example, if a robot assembles parts in a factory and its movements slow down or abnormal vibrations occur, the system records the movement and the AI suggests the optimal movement pattern. If the emotion engine detects an abnormality, it will reflect this in the suggestions for correcting the movement.

An example of a prompt to input to the generative AI model is as follows:

"To optimize the movements of robots assembling parts in a factory, AI should suggest optimal movement patterns using data recorded by 3D scans and cameras. It should also detect abnormal vibrations and temperature increases in the robot and suggest movement corrections based on that information."

The flow of the specific processing in Application Example 2 is explained using Figure 18.

Step 1:

The user uses a 3D body scanning device to obtain physique data.

Input: User's body

Output: 3D body scan data

How it works: The user stands in front of the 3D scanner, which scans their entire body and generates digital data.

Step 2:

The user's specific actions are recorded using cameras and sensors.

Input: User action

Output: Operation record data

Specific actions: The user performs a specific action (e.g., batting or pitching) in front of the camera, and the camera records that action with high accuracy.

Step 3:

The server analyzes the recorded movement data using AI methods.

Input: Motion record data, 3D body scan data

Output: Motion analysis results

Specific operation: The server uses TensorFlow and Keras to input motion recording data and 3D body scan data into an AI model to analyze the user's movements.

Step 4:

Based on the results of the behavior analysis, the server suggests behavioral modifications to the user.

Input: Motion analysis results

Output: Proposed behavior changes

Specific actions: Based on the analysis results, the server generates specific corrections to improve the user's behavior and presents them to the user via a smartphone or tablet application.

Step 5:

The robot's movements are recorded using a 3D scanner and camera.

Input: Robot movement

Output: Robot operation record data

Specific movements: As the robot performs its tasks, 3D scanners and cameras record its movements.

Step 6:

The server analyzes the robot's recorded operation data using AI methods.

Input: Robot movement record data

Output: Robot movement analysis results

Specific operation: The server uses TensorFlow and Keras to input recorded robot movement data into an AI model and analyze the robot's movement.

Step 7:

The server detects an abnormality in the robot.

Input: Robot vibration data, temperature data

Output: Anomaly detection results

Specific operation: The server analyzes data from vibration and temperature sensors using OpenCV and PyTorch to detect abnormalities.

Step 8:

The server proposes optimal movement patterns based on the results of the robot's movement analysis and anomaly detection.

Input: Robot behavior analysis results, anomaly detection results

Output: Optimal motion pattern proposal

Specific behavior: The server integrates the analysis results and anomaly detection results, generates specific behavior patterns to optimize the robot's behavior, and applies them to the robot.

The specific processing unit 290 transmits the results of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the results of the specific processing. The microphone 238 acquires audio indicating the user input regarding the results of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

Data generation model 58 is a so-called generative AI (Artificial Intelligence) model. An example of data generation model 58 is generative AI such as ChatGPT (Internet search <URL: https://openai.com/blog/chatgpt>). Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. Data generation model 58 performs inference on the input inference data in accordance with the instructions indicated by the prompt, and outputs the inference results in the form of data such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization.

Another example of generative AI is Gemini (registered trademark) (Internet search <URL: https://gemini.google.com/?hl=ja>).

In the above embodiment, an example was given in which the specific processing was performed by the data processing device 12, but the technology disclosed herein is not limited to this, and the specific processing may also be performed by the smart glasses 214.

[Third embodiment]

Figure 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

As shown in FIG. 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and/or a LAN (Local Area Network).

The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I/F 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user 20's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

The communication I/F 44 is connected to the network 54. The communication I/Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I/Fs 44 and 26 is carried out in a secure manner.

Figure 6 shows an example of the main functions of the data processing device 12 and headset terminal 314. As shown in Figure 6, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32.

The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

Storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

In the headset terminal 314, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

Next, we will explain the identification process performed by the identification processing unit 290 of the data processing device 12.

"Example 1"

In one embodiment of the present invention, a user first measures their own physique using a 3D body scan. This measurement is performed, for example, using a device that provides a 3D body scan. Next, the user performs a specific movement, such as batting or pitching in baseball. This movement is recorded using a camera or sensor. The recorded movement is analyzed by AI. The AI uses deep learning to compare the user's movement with that of a professional athlete with a similar physique. Then, based on the comparison results, suggestions are made to the user regarding movement modifications. These suggestions are presented to the user, for example, through an application on a device such as a smartphone or tablet.

"Example 2"

In one embodiment of the present invention, a user first measures their own physique using a 3D body scan. This measurement is performed, for example, using a device that provides a 3D body scan. Next, the user performs a specific movement, such as batting or pitching in baseball. This movement is recorded using a camera or sensor. The recorded movement is analyzed by AI. The AI uses deep learning to compare the user's movement with that of a professional athlete with a similar physique. Then, based on the comparison results, suggestions are made to the user regarding movement modifications. These suggestions are presented to the user, for example, through an application on a device such as a smartphone or tablet.

The processing flow for each example form is explained below.

"Example 1"

Step 1: The user measures their physique using a 3D body scan. This measurement is performed, for example, using a device that provides a 3D body scan.

Step 2: Next, the user performs a specific action, such as batting or pitching a baseball. This action is recorded using cameras and sensors.

Step 3: The recorded movements are analyzed by AI, which uses deep learning to compare the user's movements with those of professional athletes with a similar physique.

Step 4: Based on the comparison results, the user is then given suggestions for modifying their behavior. These suggestions are presented to the user, for example, through an application on a device such as a smartphone or tablet.

"Example 2"

Step 1: The user measures their physique using a 3D body scan. This measurement is performed, for example, using a device that provides a 3D body scan.

Step 2: Next, the user performs a specific action, such as batting or pitching a baseball. This action is recorded using cameras and sensors.

Step 3: The recorded movements are analyzed by AI, which uses deep learning to compare the user's movements with those of professional athletes with a similar physique.

Step 4: Based on the comparison results, the user is then given suggestions for modifying their behavior. These suggestions are presented to the user, for example, through an application on a device such as a smartphone or tablet.

(Example 1)

Next, Example 1 of Form Example 1 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Conventional motion analysis systems have struggled to accurately measure and record a user's physique and movements and provide appropriate feedback. Furthermore, they lacked the means to perform highly accurate analysis when comparing a user's movements with those of professional athletes. This made it difficult for users to effectively improve their own movements.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, means for transmitting recorded movement data and physique data to the server, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement corrections to the user based on the comparison results, and means for displaying the suggested corrections on the user's device. This allows users to accurately measure and record their own physique and movements, and compare them with the movements of professional athletes to obtain specific corrections.

"User" refers to an individual who uses the system to measure and record their physique and movements and receive feedback.

"Physique" refers to a user's physical characteristics and measurements, including data measured by means such as a 3D body scan.

"Movement" refers to specific physical movements or performances performed by the user, which are recorded using cameras and sensors.

"Server" refers to a computer system that receives data sent by users, analyzes it using AI means, and provides the results to users.

"3D body scanning" refers to technology and equipment that measures a user's physique in three dimensions and is used to obtain detailed data about the user's body.

"AI means" refers to means that use artificial intelligence technology to analyze a user's movements and compare them with those of professional athletes.

"Deep learning" is a field of artificial intelligence that uses multi-layer neural networks to learn the characteristics of data and perform advanced analysis and predictions.

"Fixes" refer to specific changes or suggestions suggested to improve user experience.

"Terminal" refers to the device used by the user to access the system, including smartphones and tablets.

"Comparison results" refers to the results of comparing the user's movements with those of a professional athlete using AI means, and includes data that serves as the basis for feedback to the user.

This invention is a system that measures and records a user's physique and movements, analyzes them using AI, and then suggests specific movement modifications to the user. A specific embodiment of this system is described below.

First, the user measures their physique using a device that provides a 3D body scan. Examples of devices used include Kinect and Structure Sensor. This acquires the user's physique data and stores it on the user's device (smartphone or tablet).

Next, the user performs a specific movement, such as a baseball batting or pitching form. This movement is recorded using a smartphone camera or a GoPro. The recorded video data is saved on the user's device.

The user's device sends recorded movement and physique data to the server. An internet connection is required for transmission. The user presses the "Send Data" button in the application, and the device uploads the data to the server.

The server inputs the received movement data and physique data into an AI model. This AI model uses TensorFlow and PyTorch, and uses deep learning to compare the user's movements with those of professional athletes with a similar physique. The server inputs the data into the AI model and begins the comparison process. Once the process is complete, the comparison results are generated.

The server generates corrections for the user's movements based on the comparison results of the AI model. These corrections are compiled into specific advice and improvement methods. For example, specific advice such as "To increase the swing speed of your batting form, you need to rotate your hips more quickly" is generated.

The server sends the generated corrections to the user's device, which then displays the received corrections within the application. The user opens the application and checks the suggested corrections. For example, specific advice such as "How to practice to rotate your hips faster" is displayed.

As a concrete example, consider the case where a user wants to improve their baseball batting form. The user first measures their physique using a 3D body scanning device (such as Kinect). Next, they record their batting form using a smartphone camera. This recorded data is input into a deep learning model using TensorFlow and compared with the movements of professional batters. Based on the comparison results, a dedicated app suggests improvements to the user's batting form.

An example prompt is, "Taking the user's physique data and a video of their batting form as input, compare it with the movements of a professional batter and suggest corrections."

In this way, users can compare their own movements with those of a professional and get specific corrections.

The flow of the identification process in Example 1 will be explained using Figure 11.

Step 1:

Users measure their physique using a 3D body scanning device.

Input: User's body location information

Specific action: The user stands in front of Kinect and rotates their body according to the instructions.

Data processing: The scanning device captures the user's physique data and generates a 3D model.

Output: The generated physique data is saved on the user's device.

Step 2:

The user performs a specific movement (e.g., a baseball batting stance) and records that movement.

Input: User action

Specific operation: The user sets up their smartphone on a tripod and takes a photo of their batting form.

Data processing: The camera records the user's movements as video data.

Output: Recorded video data is saved to the user's device.

Step 3:

The user's device sends recorded movement and physique data to the server.

Input: Movement data and physique data

Specific action: The user presses the "Send Data" button within the application.

Data processing: The device compresses the data and uploads it to a server via the Internet.

Output: Data is sent to the server.

Step 4:

The server inputs the received movement data and physique data into the AI model.

Input: Movement data and physique data

Specific operation: The server inputs the data into the AI model and begins the comparison process.

Data calculation: The AI model uses deep learning to compare the user's movements with those of professional athletes with a similar physique.

Output: The comparison results are generated.

Step 5:

The server generates corrections to the user's behavior based on the comparison results of the AI model.

Input: Comparison results

Specific actions: The server generates specific advice such as, "To increase your swing speed in your batting form, you need to rotate your hips faster."

Data processing: Analyze the comparison results and generate corrections in text format.

Output: Corrections are generated.

Step 6:

The server sends the generated modifications to the user's device.

Input: Corrections

Specific operation: The server sends the modifications to the user's device.

Data processing: Convert the corrections into the appropriate format and send.

Output: The changes are sent to the user's device.

Step 7:

The user's device will display the received corrections within the application.

Input: Corrections

What happens: The user opens the application and sees the suggested fixes.

Data processing: Display corrections in a format that is easy for users to understand.

Output: The user can see the changes.

(Application Example 1)

Next, we will explain Application Example 1 of Form Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Traditional factory work lacks specific feedback to improve the efficiency and safety of workers' movements. This leads to problems such as reduced work efficiency and increased physical strain if workers continue to perform inappropriate movements. Furthermore, the lack of a system to optimize workers' movements makes it difficult to provide appropriate guidance to individual workers.

The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes a means for measuring the user's physique, a means for recording specific movements performed by the user, an AI means for analyzing the recorded movements and comparing them with the movements of workers with a similar physique, a means for suggesting to the user movement modifications based on the comparison results, and a means for displaying suggestions for optimizing the movements of factory workers. This makes it possible to provide specific feedback to improve the efficiency and safety of workers' movements.

"User" refers to an individual who uses the system to have their physique and movements measured and analyzed.

"Physique" refers to the user's physical characteristics and measurements.

"Movement" refers to a specific physical movement or task performed by the user.

"Recording means" refers to devices or methods for saving user actions as video or data.

"AI means" refers to technology that uses artificial intelligence to analyze recorded movements and compare them with other movements.

"Deep learning" is a type of artificial intelligence that uses multi-layered neural networks to analyze and learn from data.

"Means for suggesting modifications" refers to a method or device for suggesting improvements to the user's behavior based on the analysis results.

A "factory worker" is a worker who performs specific tasks within a factory.

"Means for displaying optimization suggestions" refers to a device or method for visually presenting suggestions to the user for improving operational efficiency and safety.

"3D body scanning" is a technology for measuring a user's physique in three dimensions.

A system for implementing this invention includes means for measuring a user's physique, means for recording specific movements performed by the user, AI means for analyzing the recorded movements and comparing them with the movements of workers with a similar physique, means for suggesting to the user corrections to their movements based on the comparison results, and means for displaying suggestions for optimizing the movements of factory workers.

Hardware and software used

3D body scanning device: A device for measuring the user's physique in three dimensions.

Camera: A device used to record user actions. An example is the Logitech C920.

Smart glasses: A device that visually shows users how to correct their movements. An example is Google Glass.

Tablet: A device used to display operational corrections and optimization suggestions. An example is the iPad.

Server: A computer system for processing data and running AI models.

Software: Use programming libraries such as Python, OpenCV, and Keras.

Data processing and calculation

1. Acquisition of physique data: The server acquires the user's physique data using a 3D body scanning device. This data represents the user's physical characteristics and dimensions in three dimensions.

2. Recording of actions: The server uses a camera to record the user's actions. The recorded actions are saved as image data.

3. Movement analysis: The server inputs the recorded image data and physique data into the AI model and analyzes the movements. The AI model uses deep learning to compare the user's movements with those of workers with a similar physique.

4. Suggested modifications: The server will suggest modifications to the user's behavior based on the analysis results. These suggestions will be displayed on the smart glasses or tablet.

Specific examples

When a factory worker lifts a heavy object, their movements are recorded using a 3D body scan and camera, and then analyzed by AI. As a result of the analysis, suggestions are made for corrections such as the angle of the waist and the position of the hands when lifting. This allows workers to work efficiently and safely.

Example prompt

"Record factory workers lifting heavy objects, input this data into the AI along with 3D body scan data, compare it with efficient movements, and suggest corrections."

The above is a form for implementing this invention.

The flow of the specific processing in Application Example 1 will be explained using Figure 12.

Step 1:

The server uses a 3D body scanning device to obtain the user's physique data. The input includes the user's physical characteristics and dimensions. Data processing involves converting these characteristics and dimensions into a data format that represents them in three dimensions. The output is the user's physique data.

Step 2:

The server uses a camera to record the user's actions. The input includes the specific actions performed by the user. The data is processed by saving the actions as video data. The output is image data of the recorded actions.

Step 3:

The server inputs the recorded image data and physique data into the AI model and analyzes the movements. The input includes image data and physique data. To calculate the data, the AI model uses deep learning to compare the user's movements with those of workers with a similar physique. The output is the movement analysis results.

Step 4:

The server suggests behavioral modifications to the user based on the analysis results. The input includes the behavioral analysis results. Data processing generates specific suggestions for modifications. The output is the suggested modifications.

Step 5:

The server displays suggested corrections on the smart glasses or tablet. The input includes suggested corrections. Specific operations include generating data to visually display the suggestions and sending it to the smart glasses or tablet. The output displays suggested corrections that the user can visually confirm.

The above is the processing flow of this program.

(Example 2)

Next, Example 2 of Form Example 2 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Conventional motion analysis systems have struggled to accurately measure and record a user's physique and movements and suggest appropriate corrections. Furthermore, there was a lack of means to provide specific corrections to users, which led to problems with effective motion improvement.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, artificial intelligence means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, and means for presenting the modifications to the user. This makes it possible to accurately measure and record the user's physique and movements, suggest appropriate modifications, and present specific modifications to the user.

"User" refers to an individual who uses the system to measure and record their physique and movements and receive suggested movement modifications.

"Means for measuring body size" refers to devices or technologies that measure the dimensions and shape of a user's body in three dimensions.

"Means for recording specific actions" refers to devices or technologies that use cameras or sensors to record actions performed by users.

"Artificial intelligence means" refers to AI technology used to analyze recorded movement data and compare the user's movements with those of professional athletes.

"Means for suggesting corrections" refers to technology that extracts and suggests areas for improvement in the user's behavior based on the analysis results of artificial intelligence means.

"Means for presenting corrections" refers to a device or application that visually or audibly communicates suggested corrections to the user.

"3D body scanning" refers to technology that measures a user's physique in three dimensions.

"Deep learning" refers to a machine learning technique using multiple layers of neural networks used in artificial intelligence methods.

Form for implementing the invention

This invention relates to a system that measures a user's physique, records and analyzes specific movements, and suggests modifications to those movements. A specific embodiment of this system is described below.

User body measurements

Users measure their physique using a 3D body scanning device. Examples of such devices include a 3D scanner or motion capture system. Users follow the device's instructions and rotate 360 degrees to scan their physique. The device processes the scan data in real time and generates physique data for the user. The generated data is then sent to a server via wireless communication.

Recording actions

Next, the user performs a specific movement, such as batting or pitching in baseball. The user's movement is recorded using a camera or sensor. The hardware used may include a high-resolution camera or a motion capture system. The device generates the recorded video data or motion capture data and sends it to a server.

Analysis of movement

The server inputs the received physique data and movement data into an artificial intelligence model. This artificial intelligence model uses deep learning to compare the user's movements with those of professional athletes. The software used is a "deep learning framework." The server extracts corrections for the user's movements from the output of the artificial intelligence model.

Suggested behavior fixes

The server sends the extracted correction points to the device. The device then displays the received correction points in a dedicated application. The user can check the correction points through the application and learn specific ways to improve. For example, specific instructions such as "increase the angle at the start of the bat swing by 10 degrees" are displayed.

Specific examples

Example 1: Correcting baseball batting form

The user measures their physique using a 3D body scanning device and sends the data to a server. Then, a high-resolution camera is used to capture their batting form, and the video data is uploaded to the server. The server then analyzes the data using a deep learning framework and identifies areas for improvement. These areas are presented to the user via a dedicated application. The user then works to improve their batting form while viewing the application.

Example 2: Correcting pitching form

The user measures their physique using a 3D scanner and sends the data to the server. They then use a motion capture system to record their pitching form and upload the data to the server. The server then analyzes the data using a deep learning framework and extracts corrections that are presented to the user through a dedicated application. The user works to improve their pitching form while looking at the application.

Example prompt

Prompt 1: Baseball batting form

"Using the user's 3D body scan data and video data of their batting form as input, please generate an AI model that compares it with data from professional baseball players and suggests corrections."

Prompt 2: Pitching form

"Using the user's 3D body scan data and motion capture data of their pitching form as input, please compare it with data from professional baseball players and generate an AI model that suggests corrections."

The flow of the identification process in Example 2 will be explained using Figure 13.

Program processing flow

Step 1: Measure the user's body size

The user activates the 3D body scanning device. Following the device's instructions, the user rotates 360 degrees to scan their physique. The device processes the scan data in real time and generates the user's physique data. The generated data is sent to the server via wireless communication.

Input: User's physique scan data

Data processing: Real-time processing of scanned data

Output: User's physique data

Step 2: Record the action

Using a high-resolution camera and motion capture system, the user performs a specific movement, such as batting or pitching in baseball. The device records the movement and generates video data or motion capture data, which is then sent to the server.

Input: User action

Data processing: Recording movements and generating data

Output: Video data or motion capture data

Step 3: Analyze the Movement

The server inputs the received physique data and movement data into an artificial intelligence model. This artificial intelligence model uses deep learning to compare the user's movements with those of professional athletes. The server extracts corrections for the user's movements from the output of the artificial intelligence model.

Input: physique data, movement data

Data processing: Deep learning behavior comparison

Output: Behavior corrections

Step 4: Propose behavioral modifications

The server sends the extracted correction points to the device. The device then displays the received correction points in a dedicated application. The user can check the correction points through the application and learn specific ways to improve. For example, specific instructions such as "increase the angle at the start of the bat swing by 10 degrees" are displayed.

Input: Behavior corrections

Data processing: Preparing for corrections

Output: Present the corrections to the user

Add specific actions

Step 1: Measure the user's body size

The user activates the 3D body scanning device and follows the device's instructions to scan their physique. The scan data is processed in real time to generate the user's physique data, which is then transmitted to the server via wireless communication.

Step 2: Record the action

The user installs a high-resolution camera and films their baseball batting form. The camera records high-resolution video and saves it on the device. The device then compresses the saved video data and uploads it to the server.

Step 3: Analyze the Movement

The server inputs the received physique data and video data into a deep learning framework. The AI model compares the user's batting form with that of professional baseball players. Features such as joint angles and movement speed are used for the comparison. The server extracts corrections for the user's movement from the output of the AI model.

Step 4: Propose behavioral modifications

The server sends the extracted corrections to the device in JSON format. The device then displays the received corrections in a dedicated application. The user can review the corrections through the application and learn specific ways to improve. For example, specific instructions such as "increase the angle at the start of your bat swing by 10 degrees" are displayed.

(Application Example 2)

Next, we will explain Application Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Traditional optimization of factory robots relies on manual adjustments and experience, making it difficult to achieve efficient and accurate optimization. Furthermore, because individual optimization based on the user's physique and movements is not performed, general-purpose optimization methods often fail to produce sufficient results. This poses a risk of reduced factory productivity and quality.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, means for recording and analyzing the movements of robots used in factories, and means for suggesting modifications to optimize the robot's movement. This enables individual optimization based on the user's physique and movements, making it possible to efficiently and accurately optimize the movement of factory robots.

"User" means an individual or corporation that uses the system to measure and analyze their physique and movements.

"Physique" refers to the shape and dimensions of the user's body, as measured by means such as a 3D body scan.

"Specific actions" refer to specific movements or tasks performed by the user, which are recorded using cameras or sensors.

"Recording means" refers to devices or methods that use cameras, sensors, etc. to save user actions as data.

"AI means" refers to technology that uses artificial intelligence to analyze recorded movement data and compare the user's movements with those of professional athletes.

"Deep learning" is a type of artificial intelligence that uses multi-layered neural networks to analyze and learn from data.

A "professional athlete" is an athlete who specializes in a particular sport and is the subject to be compared with the user's movements.

"Behavior Modifications" are specific improvements suggested to optimize the user's behavior.

"Robots used in factories" are automated machinery and devices used on factory production lines and in workshops.

"Optimization" means adjusting something to the most efficient and effective state for a specific purpose.

The following hardware and software are used to implement this invention. Hardware requires a 3D scanner, camera, sensor, server, and user terminal (smartphone or tablet). Software uses Python, OpenCV, Keras, etc.

First, the user measures their physique using a 3D scanner. The 3D scanner captures the user's body shape and dimensions with high precision and saves them as digital data. This data is then sent to a server.

Next, the user performs a specific action. For example, if they are imitating the actions of a robot used in a factory, the action is recorded using a camera or sensor. The recorded action data is also sent to the server.

The server inputs the received 3D scan data and movement data into the AI means. The AI means analyzes this data using deep learning and compares the user's movements with those of professional athletes with a similar physique. Based on the comparison results, it suggests movement modifications to the user.

Furthermore, to optimize the operation of robots used in factories, robot operation data is also recorded and analyzed using AI methods. Based on the analysis results, corrections are suggested to optimize the robot's operation. These suggestions are displayed on the user's device.

As a concrete example, consider optimizing the operation of a welding robot used in a factory. A 3D scanner captures the robot's shape data, and cameras and sensors record its operation data. This data is input into an AI tool, and suggestions are made for adjusting the welding angle and speed based on the analysis results.

An example of a prompt to input into the generative AI model is, "Please input the welding robot's motion data along with the 3D scan data, and suggest optimal motion corrections."

As described above, this invention enables individual optimization based on the user's physique and movements, allowing the operation of factory robots to be optimized efficiently and with high precision.

The flow of the specific processing in Application Example 2 will be explained using Figure 14.

Step 1:

Users measure their body size using a 3D scanner.

Input: User's body

Data processing: A 3D scanner captures the user's body shape and dimensions with high precision and stores them as digital data.

Output: 3D scan data

Step 2:

The user performs a specific action, which is recorded using cameras and sensors.

Input: User action

Data processing: Cameras and sensors record user movements as video and sensor data.

Output: Operation data

Step 3:

The server receives the 3D scan data and movement data.

Input: 3D scan data, motion data

Data processing: The server receives this data and prepares it for input into the AI means.

Output: Prepared data

Step 4:

The server analyzes the 3D scan data and movement data using AI tools.

Input: Prepared data

Data processing: Using a deep learning model, we compare the user's movements with those of professional athletes with a similar physique.

Output: Analysis results

Step 5:

The server then suggests behavioral modifications to the user based on the analysis results.

Input: Analysis results

Data processing: Based on the analysis results, specific modifications are generated to optimize user behavior.

Output: Suggested fixes

Step 6:

Suggested fixes will be displayed on the user's device.

Enter: Suggested fix

Data processing: Convert the proposal content into a format for display on the user's device.

Output: Corrections displayed on the user's device

Step 7:

The movements of robots used in factories are recorded and sent to a server.

Input: Robot movement

Data processing: Cameras and sensors record the robot's movements as video and sensor data, which are then sent to a server.

Output: Robot movement data

Step 8:

The server analyzes the robot's operation data using AI means and suggests modifications for optimization.

Input: Robot movement data

Data processing: Using deep learning models, we analyze the robot's behavior and generate corrections for optimization.

Output: Suggested modifications to the robot's behavior

Step 9:

Suggestions for modifying the robot's behavior will be displayed on the user's device.

Input: Suggested modifications to the robot's behavior

Data processing: Convert the proposal content into a format for display on the user's device.

Output: Robot behavior corrections displayed on the user's device.

It is also possible to further combine an emotion engine that estimates the user's emotion. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

"Example 1"

In one embodiment of the present invention, a system is provided that includes a means for measuring a user's physique, a means for recording specific movements performed by the user, an AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, a means for suggesting movement modifications to the user based on the comparison results, and an emotion engine for recognizing the user's emotions. The emotion engine recognizes emotions from the user's facial expressions and tone of voice and provides this information to the AI means. The AI means not only compares the user's movements with those of the professional athletes, but also takes the user's emotions into consideration when suggesting movement modifications.

For example, if a user is feeling frustrated, the AI tool will take that information into account and suggest modifications that are acceptable to the user. In this way, it becomes possible to suggest operational improvements that take the user's emotions into account.

"Example 2"

In one embodiment of the present invention, a system is provided that includes a means for measuring a user's physique, a means for recording specific movements performed by the user, an AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, a means for suggesting movement modifications to the user based on the comparison results, and an emotion engine that recognizes the user's emotions. The emotion engine recognizes emotions from the user's facial expressions and tone of voice and provides this information to the AI means. The AI means suggests movement modifications that take into account not only the comparison between the user's movements and the movements of the professional athletes, but also the user's emotions. For example, if the user is feeling frustrated, the AI means takes this information into consideration and suggests modifications in a manner that is easy for the user to accept. In this way, it is possible to suggest movement improvements that take the user's emotions into account.

The processing flow for each example form is explained below.

"Example 1"

Step 1: Measure the user's physique using a 3D body scan.

Step 2: Record a specific action performed by the user (e.g., batting or pitching a baseball) using a video camera or motion sensor.

Step 3: The recorded movements are analyzed using AI tools and compared with the movements of professional athletes with similar physiques.

Step 4: Understand the user's emotions using an emotion engine that recognizes emotions from the user's facial expressions and tone of voice.

Step 5: The AI means suggests corrections to the user's movements, taking into account the results of comparing the user's movements with those of professional athletes and the user's emotions.

Step 6: The proposed behavioral modifications are presented to the user via an application on a device such as a smartphone or tablet.

"Example 2"

Step 1: Measure the user's physique using a 3D body scan.

Step 2: Record a specific action performed by the user (e.g., batting or pitching a baseball) using a video camera or motion sensor.

Step 3: The recorded movements are analyzed using AI tools and compared with the movements of professional athletes with similar physiques.

Step 4: Understand the user's emotions using an emotion engine that recognizes emotions from the user's facial expressions and tone of voice.

Step 5: The AI means suggests corrections to the user's movements, taking into account the results of comparing the user's movements with those of professional athletes and the user's emotions.

Step 6: The proposed behavioral modifications are presented to the user via an application on a device such as a smartphone or tablet.

(Example 1)

Next, Example 1 of Form Example 1 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Traditional movement improvement systems make suggestions based on the user's physique and movement data, but because they don't take the user's emotions into account, users may find it difficult to accept the suggestions. Furthermore, since the corrections to movement are not specific, it is difficult for users to understand how to improve.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, artificial intelligence means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, means for recognizing the user's emotions, and means for suggesting movement modifications taking into account the emotion data. This makes it possible to suggest movement improvements that take into account the user's physique and emotions.

"Means for measuring the user's physique" refers to devices or methods that measure the user's physical characteristics and acquire them as digital data.

"Means for recording specific actions performed by a user" refers to devices or methods that use video or sensors to record specific actions performed by a user as digital data.

"Artificial intelligence means" refers to software or systems that analyze recorded data and use specific algorithms to compare the user's movements with other data (e.g., the movements of professional athletes).

"Means for suggesting movement corrections" refers to devices or methods that suggest movement improvements and correction methods to the user based on the analysis results.

"Means for recognizing user emotions" refers to devices or methods that analyze emotions from the user's facial expressions, tone of voice, etc., and acquire them as digital data.

"Means for suggesting movement modifications taking into account emotional data" refers to a device or method that suggests movement modifications in a more acceptable form based on the user's emotional data.

"3D body scanning" is a technology that scans the user's body in three dimensions and obtains detailed physique data.

"Deep learning" is an artificial intelligence technology that uses multi-layered neural networks to analyze data and learn patterns and features.

This invention is a system that analyzes a user's physique and movements, compares them with the movements of professional athletes, and suggests movement modifications to the user. Furthermore, by recognizing the user's emotions and making suggestions that take their emotional data into consideration, it is possible to present movement modifications in a way that is easy for the user to accept.

Hardware and software used

1. User body measurements

Users measure their physique using equipment that provides a 3D body scan. This equipment may include, for example, a 3D scanner or a dedicated body scan application. This obtains detailed physique data such as the user's height, weight, and body fat percentage.

2. Recording actions

Users use their smartphone camera or a dedicated motion capture device to record specific movements (e.g., batting or pitching in baseball). The recorded data includes detailed movement information such as swing speed and angle.

3. Sending operation data

The device transmits recorded movement and physique data to a server. This transmission occurs in real time over the Internet, and the data is encrypted using a secure communication protocol.

4. Movement Analysis

The server inputs the received data into an AI model, which uses deep learning algorithms to compare the user's movements with those of professional athletes. The software used is a deep learning framework such as TensorFlow or PyTorch. For example, it calculates how much the user's swing speed differs from the average speed of professional athletes.

5. Emotional Recognition

The device uses an emotion engine that analyzes the user's facial expressions and tone of voice in real time. This emotion engine determines whether the user is frustrated or satisfied and sends that information to a server. The software used is an emotion recognition API (e.g., Microsoft Azure's Emotion API).

6. Suggestions for behavioral modifications

Based on the comparison of the movements and the emotional data, the server suggests to the user how to modify their movements. For example, if the user is feeling frustrated, the server might suggest, "Maybe if you just change the way you grip the bat a little, your swing will become smoother." The user is notified of these suggestions through an application on their device, such as a smartphone or tablet.

Specific examples

Example 1: Improving baseball batting form

The user measures their physique using a 3D scanner and records their batting form with their smartphone camera. The device then sends the recorded data to a server, which then inputs the data into an AI model and compares it with the form of professional baseball players. If the emotion engine detects the user's frustration, the server will make suggestions such as, "Maybe if you just change the way you grip the bat a little, your swing will become smoother." The user is notified of these suggestions via a smartphone application.

Example prompt for a generative AI model

"Using the user's 3D body scan data and video of their batting form as input, please compare it with the form of a professional baseball player and suggest improvements while taking into account the user's emotions."

In this way, it becomes possible to suggest movement improvements that take into account the user's physique and emotions.

The flow of the identification process in Example 1 will be explained using Figure 15.

Step 1:

Users measure their physique using a device that provides a 3D body scan. As input, a 3D scanner is used to scan the user's body. As output, detailed physique data such as the user's height, weight, and body fat percentage is generated. This data is stored on the device.

Step 2:

Users use their smartphone's camera or a dedicated motion capture device to record specific movements (e.g., batting or pitching in baseball). As input, the user's movements are captured by the camera or sensors. As output, detailed movement data such as swing speed and angle is generated. This data is stored on the device.

Step 3:

The device sends the recorded movement data and physique data to the server. The movement data and physique data stored on the device are used as input. As output, this data is sent to the server via the internet. The data is encrypted and uses a secure communication protocol.

Step 4:

The server inputs the received data into the AI model. The movement data and physique data sent to the server are used as input. The output is the results of the AI model comparing the user's movements with those of professional athletes. This AI model uses deep learning algorithms and utilizes deep learning frameworks such as TensorFlow and PyTorch.

Step 5:

The device uses an emotion engine that analyzes the user's facial expressions and tone of voice in real time. As input, the user's facial and voice data is captured through a camera and microphone. As output, emotional data is generated, such as whether the user is frustrated or satisfied. This data is sent to a server.

Step 6:

The server suggests movement corrections to the user based on the movement comparison results and emotional data. The AI model's comparison results and emotional data are used as input. The output is movement corrections generated for the user. For example, if the user is feeling frustrated, the server might suggest, "Maybe your swing will become smoother if you just change the way you grip the bat slightly." The suggestion is notified to the user through an application on their device, such as a smartphone or tablet.

(Application Example 1)

Next, we will explain Application Example 1 of Form Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

While conventional motion analysis systems can analyze a user's physique and movements, they lack the ability to suggest motion modifications that take the user's emotions into account. Furthermore, when optimizing the movements of robots working in factories, they lack the ability to monitor movement efficiency and error rates in real time and provide improvement suggestions. This leads to issues such as reduced movement efficiency for users and robots, preventing optimal performance.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, an emotion engine for recognizing the user's emotions, means for suggesting movement modifications taking into account information from the emotion engine, means for optimizing the robot's movements, means for monitoring the robot's movement efficiency and error rate, and means for making improvement suggestions in real time. This makes it possible to suggest movement modifications taking the user's emotions into account, and further enables the movement efficiency and error rate of robots working in factories to be monitored in real time, enabling optimal movement performance to be achieved.

"Means for measuring the user's physique" refers to devices or technologies for accurately measuring the dimensions and shape of the user's body.

"Means for recording specific actions performed by a user" refers to devices or technologies that use cameras, sensors, etc. to record specific actions performed by a user.

"AI means for analyzing recorded movements and comparing them with the movements of professional athletes with similar physiques" refers to artificial intelligence technology that analyzes recorded user movement data and compares it with the movement data of professional athletes with similar physiques.

"Means for suggesting to the user points for correction of movements based on the comparison results" refers to devices or technologies that suggest points for improvement or correction of movements to the user based on the results of movement comparison by AI.

The "emotion engine that recognizes user emotions" is a technology that identifies emotions from the user's facial expressions, tone of voice, etc.

"Means for suggesting behavioral modifications by taking into account information from an emotion engine" refers to devices or technologies that suggest behavioral modifications based on the user's emotional information recognized by an emotion engine.

"Means for optimizing robot operation" refers to devices and technologies that optimize the operation of robots working in factories to ensure that they are efficient and effective.

"Means for monitoring a robot's operational efficiency and error rate" refers to devices and technologies that monitor a robot's operational efficiency and error rate in real time.

"Means for making improvement suggestions in real time" refers to devices and technologies that analyze robot operation data in real time and immediately suggest areas for improvement.

A system for implementing this invention includes means for measuring a user's physique, means for recording specific movements performed by the user, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting to the user movements modifications based on the comparison results, an emotion engine for recognizing the user's emotions, means for suggesting movements modifications taking into account information from the emotion engine, means for optimizing the robot's movements, means for monitoring the robot's movement efficiency and error rate, and means for making improvement suggestions in real time.

Hardware and software configuration

Hardware:

3D body scanning device

Camera (e.g., Logitech C920)

Computer (e.g., Intel Core i7, 16GB RAM)

Software:

OpenCV: Motion recording and image processing

Keras: Loading and predicting deep learning models

EmotionRecognizer: Emotion Recognition Library

Processing flow

The server first acquires the user's physique data using a 3D body scanning device. Then, it uses a camera to record specific movements performed by the user. The recorded movement data is preprocessed using OpenCV and analyzed using a deep learning model using Keras. The analysis results are compared with the movement data of professional athletes with similar physiques.

Furthermore, EmotionRecognizer is used to recognize the user's emotions and suggests behavioral modifications taking into account information from the emotion engine. The suggested modifications are presented to the user.

For robots working in factories, measures are used to optimize the robot's operations. The robot's operating efficiency and error rate are monitored in real time, and suggestions for improvement are made. This makes it possible to optimize the robot's operating performance.

Specific examples

For example, if a robot assembling parts in a factory is moving slowly, the AI will analyze its movements and suggest correcting the angle and speed of its movements. Furthermore, if the robot is making frequent errors, it will determine through emotion recognition that it is feeling frustrated and suggest that it move more slowly.

Example prompt

"To optimize the movements of robots that assemble parts in factories, design a system that uses 3D body scans and cameras to record their movements, analyzes them with AI, and suggests optimal movement patterns. Additionally, add functionality to monitor the robot's movement efficiency and error rate and suggest improvements in real time."

The flow of the specific processing in Application Example 1 will be explained using Figure 16.

Step 1:

The server acquires the user's physique data using a 3D body scanning device. As input, it scans the user's body dimensions and shape, and generates 3D physique data as output. This data is used to accurately determine the user's physique.

Step 2:

The server uses a camera to record specific actions performed by the user. As input, it captures the actions performed by the user in real time, and as output, it generates video data of the actions. This video data is used for subsequent action analysis.

Step 3:

The server preprocesses the recorded motion data using OpenCV. It receives motion video data as input and generates preprocessed image data as output. This preprocessing includes image resizing and noise removal.

Step 4:

The server analyzes the preprocessed image data using a deep learning model using Keras. It receives the preprocessed image data as input and extracts movement features as output. These features are used to analyze the user's movements in detail.

Step 5:

The server compares the extracted features with the motion data of professional athletes with a similar physique. It receives the user's motion features and the motion data of the professional athletes as input, and generates the comparison results as output. These comparison results are used to identify areas in the user's motion that need to be corrected.

Step 6:

The server uses EmotionRecognizer to recognize the user's emotions. As input, it captures the user's facial expressions and tone of voice while in action, and generates emotional data as output. This emotional data is used to understand the user's emotional state.

Step 7:

The server takes into account information from the emotion engine to suggest behavioral modifications. It receives the comparison results and emotion data as input, and generates behavioral modifications for the user as output. These modifications are suggested taking into account the user's emotional state.

Step 8:

The server executes a process to optimize the operation of robots working in factories. It receives robot operation data as input and generates optimized operation patterns as output. This optimization is performed to improve the robot's operation efficiency.

Step 9:

The server monitors the robot's operating efficiency and error rate in real time. It receives the robot's operating data in real time as input and generates monitoring results of operating efficiency and error rate as output. These monitoring results are used to understand the robot's operating status.

Step 10:

The server makes improvement suggestions in real time. It receives monitoring results as input and generates improvement suggestions as output. These improvement suggestions are made to optimize the robot's operational performance.

(Example 2)

Next, Example 2 of Form Example 2 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Previous movement improvement systems struggled to accurately measure a user's physique and movements and provide appropriate feedback. Furthermore, they didn't take the user's emotions into account when proposing movement improvements, resulting in a lack of feedback that was easy for the user to accept. This limited the effectiveness of the system's movement improvements.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, means for transmitting the recorded movements, artificial intelligence means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, means for recognizing the user's emotions, and means for suggesting movement modifications taking the recognized emotions into consideration. This makes it possible to accurately measure the user's physique and movements and provide appropriate feedback. Furthermore, because suggestions for movement improvement are made taking the user's emotions into consideration, feedback is provided in a form that makes the suggestions more receptive to the user, improving the effectiveness of movement improvement.

"Means for measuring the user's physique" refers to devices or technologies that measure the dimensions and shape of the user's body in three dimensions.

"Means for recording specific actions performed by a user" refers to devices or technologies that use video or sensors to record specific actions performed by a user.

"Means for transmitting recorded behavior" refers to communication means for transmitting recorded behavior data to a server or other device.

"Artificial intelligence means" refers to artificial intelligence technology used to analyze recorded movement data and compare the user's movements with those of professional athletes.

"Means for suggesting movement corrections" refers to devices or technologies that suggest movement improvements to the user based on the analysis results of artificial intelligence means.

"Means for recognizing user emotions" refers to devices or technologies for recognizing emotions from the user's facial expressions, tone of voice, etc.

"Means for suggesting behavioral modifications taking into account recognized emotions" refers to devices or technologies that suggest behavioral modifications in a way that is easy for the user to accept, based on the user's emotional information.

This invention is a system that measures a user's physique, records specific movements, analyzes those movements, and suggests corrections. The system can recognize the user's emotions and take them into consideration when suggesting corrections to movements.

First, the user measures their physique using a 3D body scanning device. Specifically, devices such as the "Structure Sensor" or "Kinect" are used. This allows the user's physique data to be acquired and stored on the device.

Next, the user performs a specific movement (e.g., batting or pitching a baseball). This movement is recorded using a camera or sensor (e.g., a GoPro camera or a Vicon motion capture system). The recorded movement data is stored on the device.

The device sends recorded movement and physique data to a server. The data is sent using a secure protocol (e.g., HTTPS).

The server inputs the received data into an AI model, which is built using a deep learning framework (e.g., TensorFlow or PyTorch) and compares the user's movements with those of professional athletes with a similar physique. The server generates comparison results and identifies movement modifications.

The server sends the identified corrections to the device, which then presents the corrections to the user through an application on the device, such as a smartphone or tablet. For example, specific advice such as "You should increase the angle of your bat swing a little more" is displayed.

In addition, the device inputs the user's facial expressions and tone of voice into an emotion engine. The emotion engine uses Affectiva and the Microsoft Azure Emotion API to recognize the user's emotions in real time. The recognition results are stored on the device.

The device sends the recognized emotional information to the server. The server reevaluates the AI model taking the emotional information into account and suggests modifications that the user can easily accept. For example, if the user feels frustrated, the server will generate a suggestion such as "First, relax, and then try to be aware of your release points," and send it to the device. The device will then display this suggestion to the user.

Specific examples

Example 1: Improving baseball batting form

The user uses the "Structure Sensor" to measure their physique and a GoPro camera to record their batting form. The device then sends this data to a server. The server uses TensorFlow to compare the batting form with that of professional baseball players and suggests, via a smartphone app, that the user "increase the angle of the bat's swing a little more." If the user is feeling frustrated, the emotion engine recognizes this and the server suggests, "First, relax, and then try to be more conscious of your swing angle."

Example 2: Improving pitching form

The user uses Kinect to measure their physique and a Vicon motion capture system to record their pitching form. The device then sends this data to a server. The server uses PyTorch to compare the form with that of professional pitchers and suggests, via a tablet app, that the release point should be moved a little further forward. If the user is feeling frustrated, the emotion engine recognizes this and the server suggests, "First, relax, and then try to focus on the release point."

Example prompt

"The user uses a 3D body scanning device to measure their physique and a camera to record their batting form. The AI uses TensorFlow to compare their form with that of professional baseball players and suggests corrections via a smartphone app. If the user is feeling frustrated, the emotion engine recognizes this and the AI suggests corrections in a way that is easy for the user to accept."

The flow of the identification process in Example 2 will be explained using Figure 17.

Step 1:

Users measure their own physique using a 3D body scanning device. Specifically, users acquire physique data using a "Structure Sensor" or "Kinect." The input is the dimensions and shape of the user's body, and the output is physique data as a 3D model. This data is stored on the device.

Step 2:

The user performs a specific movement (e.g., batting or pitching a baseball). The user records the movement using a GoPro camera or a Vicon motion capture system. The input is the user's movement, and the output is high-resolution video data. This data is stored on the device.

Step 3:

The device sends recorded movement data and physique data to the server. The input is movement data and physique data, and the output is data sent to the server. Data is sent using a secure protocol (e.g., HTTPS).

Step 4:

The server inputs the received data into an AI model. The AI model is built using TensorFlow and PyTorch and compares the user's movements with those of professional athletes with a similar physique. The input is movement data and physique data, and the output is the comparison results, including suggestions for movement correction.

Step 5:

The server sends the identified corrections to the terminal. The input is the comparison result, and the output is the corrections sent to the terminal. The terminal then presents the corrections to the user through an application on a device such as a smartphone or tablet. For example, specific advice such as "You should increase the angle of your bat swing a little more" is displayed.

Step 6:

The device inputs the user's facial expressions and tone of voice into the emotion engine. The emotion engine uses "Affectiva" and "Microsoft Azure Emotion API" to recognize the user's emotions in real time. The input is the user's facial expressions and tone of voice, and the output is recognized emotional information. This information is stored on the device.

Step 7:

The device sends the recognized emotional information to the server. The input is emotional information, and the output is the transmission of emotional information to the server. The server reevaluates the AI model taking the emotional information into account and suggests corrections that are easy for the user to accept. For example, if the user is feeling frustrated, the server will generate a suggestion such as "First, relax, and then try to be aware of your release point," and send it to the device. The device will display this suggestion to the user.

(Application Example 2)

Next, we will explain Application Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Conventional motion analysis systems record a user's physique and movements, compare them with those of professional athletes, and suggest corrections, but they were not designed to optimize the movements of robots working in factories. They also lacked the ability to detect abnormalities in the robots and suggest corrections to their movements based on that information. This made it difficult to efficiently manage the movements of robots in factories.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, means for recording the robot's movements and proposing optimal movement patterns, and means for detecting abnormalities in the robot and proposing movement modifications based on that information. This not only improves the user's movements, but also enables efficient movement management and optimization of robots within factories.

"User" refers to an individual who uses the system to measure and analyze their physique and movements.

"Physique" refers to the user's physical characteristics and measurements.

"Specific movements" refers to specific actions or movements performed by the user (e.g., batting or pitching form in baseball).

"Recording means" refers to devices or methods that use cameras, sensors, etc. to save the actions of users or robots as digital data.

"AI means" refers to a system that uses artificial intelligence to analyze data and output results according to a specific purpose.

"Deep learning" refers to a machine learning technique that uses multi-layered neural networks to learn data and extract complex patterns and features.

A "professional athlete" refers to an athlete who has high skills in a particular sport and engages in the sport professionally.

"Behavior modifications" refers to specific changes or adjustments proposed to improve the user's or robot's behavior.

"Robot" refers to an automated mechanical device that performs work in a factory.

"Abnormal" refers to conditions such as vibrations or temperature rises that deviate from the robot's normal operation.

"Means of sensing" refers to methods or devices that use sensors or monitoring devices to detect abnormalities.

"Optimal movement pattern" refers to a series of steps or methods of robot movement proposed to perform a task efficiently and effectively.

We will now explain the mode for implementing this invention. First, a 3D body scanning device is used to measure the user's physique. Specifically, a 3D scanner such as Intel RealSense is used to acquire the user's physique data. This data is used to accurately record the user's physical characteristics and dimensions.

Next, cameras and sensors are used to record specific actions performed by the user. For example, an Intel RealSense camera is used to record user actions with high accuracy. This recorded data is then analyzed using AI tools, which will be described later.

The recorded movement data is analyzed using AI tools that use deep learning, such as TensorFlow and Keras. The AI tools compare the user's movements with those of professional athletes with a similar physique, and suggest movement corrections. These suggestions are presented to the user via an application on their smartphone or tablet.

Furthermore, in order to optimize the movements of robots working in factories, it includes a means for recording the movements of the robots and proposing optimal movement patterns. The movements of the robots are recorded using a 3D scanner or camera and analyzed by the AI means. The AI means proposes optimal movement patterns and makes the robots' movements more efficient.

In addition, vibration and temperature sensors are used to detect abnormalities in the robot. OpenCV and PyTorch are used to detect abnormalities and suggest corrections to behavior based on that information. This optimizes the robot's behavior and enables it to work more efficiently.

For example, if a robot assembles parts in a factory and its movements slow down or abnormal vibrations occur, the system records the movement and the AI suggests the optimal movement pattern. If the emotion engine detects an abnormality, it will reflect this in the suggestions for correcting the movement.

An example of a prompt to input to the generative AI model is as follows:

"To optimize the movements of robots assembling parts in a factory, AI should suggest optimal movement patterns using data recorded by 3D scans and cameras. It should also detect abnormal vibrations and temperature increases in the robot and suggest movement corrections based on that information."

The flow of the specific processing in Application Example 2 is explained using Figure 18.

Step 1:

The user uses a 3D body scanning device to obtain physique data.

Input: User's body

Output: 3D body scan data

How it works: The user stands in front of the 3D scanner, which scans their entire body and generates digital data.

Step 2:

The user's specific actions are recorded using cameras and sensors.

Input: User action

Output: Operation record data

Specific actions: The user performs a specific action (e.g., batting or pitching) in front of the camera, and the camera records that action with high accuracy.

Step 3:

The server analyzes the recorded movement data using AI methods.

Input: Motion record data, 3D body scan data

Output: Motion analysis results

Specific operation: The server uses TensorFlow and Keras to input motion recording data and 3D body scan data into an AI model to analyze the user's movements.

Step 4:

Based on the results of the behavior analysis, the server suggests behavioral modifications to the user.

Input: Motion analysis results

Output: Proposed behavior changes

Specific actions: Based on the analysis results, the server generates specific corrections to improve the user's behavior and presents them to the user via a smartphone or tablet application.

Step 5:

The robot's movements are recorded using a 3D scanner and camera.

Input: Robot movement

Output: Robot operation record data

Specific movements: As the robot performs its tasks, 3D scanners and cameras record its movements.

Step 6:

The server analyzes the robot's recorded operation data using AI methods.

Input: Robot movement record data

Output: Robot movement analysis results

Specific operation: The server uses TensorFlow and Keras to input recorded robot movement data into an AI model and analyze the robot's movement.

Step 7:

The server detects an abnormality in the robot.

Input: Robot vibration data, temperature data

Output: Anomaly detection results

Specific operation: The server analyzes data from vibration and temperature sensors using OpenCV and PyTorch to detect abnormalities.

Step 8:

The server proposes optimal movement patterns based on the results of the robot's movement analysis and anomaly detection.

Input: Robot behavior analysis results, anomaly detection results

Output: Optimal motion pattern proposal

Specific behavior: The server integrates the analysis results and anomaly detection results, generates specific behavior patterns to optimize the robot's behavior, and applies them to the robot.

The specific processing unit 290 transmits the results of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the results of the specific processing. The microphone 238 acquires audio indicating the user input regarding the results of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

Data generation model 58 is a so-called generative AI (Artificial Intelligence) model. An example of data generation model 58 is generative AI such as ChatGPT (Internet search <URL: https://openai.com/blog/chatgpt>). Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. Data generation model 58 performs inference on the input inference data in accordance with the instructions indicated by the prompt, and outputs the inference results in the form of data such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization.

Another example of generative AI is Gemini (Internet search <URL: https://gemini.google.com/?hl=ja>).

In the above embodiment, an example was given in which the specific processing was performed by the data processing device 12, but the technology disclosed herein is not limited to this, and the specific processing may also be performed by the headset-type terminal 314.

[Fourth embodiment]

Figure 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

As shown in FIG. 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34.

The database 24 and communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and/or a LAN (Local Area Network).

The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I/F 44, and a control target 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and control target 443 are also connected to the bus 52.

The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user 20's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

The communication I/F 44 is connected to the network 54. The communication I/Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I/Fs 44 and 26 is carried out in a secure manner.

The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32.

The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

Storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

In the robot 414, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

Next, we will explain the identification process performed by the identification processing unit 290 of the data processing device 12.

"Example 1"

In one embodiment of the present invention, a user first measures their own physique using a 3D body scan. This measurement is performed, for example, using a device that provides a 3D body scan. Next, the user performs a specific movement, such as batting or pitching in baseball. This movement is recorded using a camera or sensor. The recorded movement is analyzed by AI. The AI uses deep learning to compare the user's movement with that of a professional athlete with a similar physique. Then, based on the comparison results, suggestions are made to the user regarding movement modifications. These suggestions are presented to the user, for example, through an application on a device such as a smartphone or tablet.

"Example 2"

In one embodiment of the present invention, a user first measures their own physique using a 3D body scan. This measurement is performed, for example, using a device that provides a 3D body scan. Next, the user performs a specific movement, such as batting or pitching in baseball. This movement is recorded using a camera or sensor. The recorded movement is analyzed by AI. The AI uses deep learning to compare the user's movement with that of a professional athlete with a similar physique. Then, based on the comparison results, suggestions are made to the user regarding movement modifications. These suggestions are presented to the user, for example, through an application on a device such as a smartphone or tablet.

The processing flow for each example form is explained below.

"Example 1"

Step 1: The user measures their physique using a 3D body scan. This measurement is performed, for example, using a device that provides a 3D body scan.

Step 2: Next, the user performs a specific action, such as batting or pitching a baseball. This action is recorded using cameras and sensors.

Step 3: The recorded movements are analyzed by AI, which uses deep learning to compare the user's movements with those of professional athletes with a similar physique.

Step 4: Based on the comparison results, the user is then given suggestions for modifying their behavior. These suggestions are presented to the user, for example, through an application on a device such as a smartphone or tablet.

"Example 2"

Step 1: The user measures their physique using a 3D body scan. This measurement is performed, for example, using a device that provides a 3D body scan.

Step 2: Next, the user performs a specific action, such as batting or pitching a baseball. This action is recorded using cameras and sensors.

Step 3: The recorded movements are analyzed by AI, which uses deep learning to compare the user's movements with those of professional athletes with a similar physique.

Step 4: Based on the comparison results, the user is then given suggestions for modifying their behavior. These suggestions are presented to the user, for example, through an application on a device such as a smartphone or tablet.

(Example 1)

Next, Example 1 of Form Example 1 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Conventional motion analysis systems have struggled to accurately measure and record a user's physique and movements and provide appropriate feedback. Furthermore, they lacked the means to perform highly accurate analysis when comparing a user's movements with those of professional athletes. This made it difficult for users to effectively improve their own movements.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, means for transmitting recorded movement data and physique data to the server, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement corrections to the user based on the comparison results, and means for displaying the suggested corrections on the user's device. This allows users to accurately measure and record their own physique and movements, and compare them with the movements of professional athletes to obtain specific corrections.

"User" refers to an individual who uses the system to measure and record their physique and movements and receive feedback.

"Physique" refers to a user's physical characteristics and measurements, including data measured by means such as a 3D body scan.

"Movement" refers to specific physical movements or performances performed by the user, which are recorded using cameras and sensors.

"Server" refers to a computer system that receives data sent by users, analyzes it using AI means, and provides the results to users.

"3D body scanning" refers to technology and equipment that measures a user's physique in three dimensions and is used to obtain detailed data about the user's body.

"AI means" refers to means that use artificial intelligence technology to analyze a user's movements and compare them with those of professional athletes.

"Deep learning" is a field of artificial intelligence that uses multi-layer neural networks to learn the characteristics of data and perform advanced analysis and predictions.

"Fixes" refer to specific changes or suggestions suggested to improve user experience.

"Terminal" refers to the device used by the user to access the system, including smartphones and tablets.

"Comparison results" refers to the results of comparing the user's movements with those of a professional athlete using AI means, and includes data that serves as the basis for feedback to the user.

This invention is a system that measures and records a user's physique and movements, analyzes them using AI, and then suggests specific movement modifications to the user. A specific embodiment of this system is described below.

First, the user measures their physique using a device that provides a 3D body scan. Examples of devices used include Kinect and Structure Sensor. This acquires the user's physique data and stores it on the user's device (smartphone or tablet).

Next, the user performs a specific movement, such as a baseball batting or pitching form. This movement is recorded using a smartphone camera or a GoPro. The recorded video data is saved on the user's device.

The user's device sends recorded movement and physique data to the server. An internet connection is required for transmission. The user presses the "Send Data" button in the application, and the device uploads the data to the server.

The server inputs the received movement data and physique data into an AI model. This AI model uses TensorFlow and PyTorch, and uses deep learning to compare the user's movements with those of professional athletes with a similar physique. The server inputs the data into the AI model and begins the comparison process. Once the process is complete, the comparison results are generated.

The server generates corrections for the user's movements based on the comparison results of the AI model. These corrections are compiled into specific advice and improvement methods. For example, specific advice such as "To increase the swing speed of your batting form, you need to rotate your hips more quickly" is generated.

The server sends the generated corrections to the user's device, which then displays the received corrections within the application. The user opens the application and checks the suggested corrections. For example, specific advice such as "How to practice to rotate your hips faster" is displayed.

As a concrete example, consider the case where a user wants to improve their baseball batting form. The user first measures their physique using a 3D body scanning device (such as Kinect). Next, they record their batting form using a smartphone camera. This recorded data is input into a deep learning model using TensorFlow and compared with the movements of professional batters. Based on the comparison results, a dedicated app suggests improvements to the user's batting form.

An example prompt is, "Taking the user's physique data and a video of their batting form as input, compare it with the movements of a professional batter and suggest corrections."

In this way, users can compare their own movements with those of a professional and get specific corrections.

The flow of the identification process in Example 1 will be explained using Figure 11.

Step 1:

Users measure their physique using a 3D body scanning device.

Input: User's body location information

Specific action: The user stands in front of Kinect and rotates their body according to the instructions.

Data processing: The scanning device captures the user's physique data and generates a 3D model.

Output: The generated physique data is saved on the user's device.

Step 2:

The user performs a specific movement (e.g., a baseball batting stance) and records that movement.

Input: User action

Specific operation: The user sets up their smartphone on a tripod and takes a photo of their batting form.

Data processing: The camera records the user's movements as video data.

Output: Recorded video data is saved to the user's device.

Step 3:

The user's device sends recorded movement and physique data to the server.

Input: Movement data and physique data

Specific action: The user presses the "Send Data" button within the application.

Data processing: The device compresses the data and uploads it to a server via the Internet.

Output: Data is sent to the server.

Step 4:

The server inputs the received movement data and physique data into the AI model.

Input: Movement data and physique data

Specific operation: The server inputs the data into the AI model and begins the comparison process.

Data calculation: The AI model uses deep learning to compare the user's movements with those of professional athletes with a similar physique.

Output: The comparison results are generated.

Step 5:

The server generates corrections to the user's behavior based on the comparison results of the AI model.

Input: Comparison results

Specific actions: The server generates specific advice such as, "To increase your swing speed in your batting form, you need to rotate your hips faster."

Data processing: Analyze the comparison results and generate corrections in text format.

Output: Corrections are generated.

Step 6:

The server sends the generated modifications to the user's device.

Input: Corrections

Specific operation: The server sends the modifications to the user's device.

Data processing: Convert the corrections into the appropriate format and send.

Output: The changes are sent to the user's device.

Step 7:

The user's device will display the received corrections within the application.

Input: Corrections

What happens: The user opens the application and sees the suggested fixes.

Data processing: Display corrections in a format that is easy for users to understand.

Output: The user can see the changes.

(Application Example 1)

Next, we will explain Application Example 1 of Form Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Traditional factory work lacks specific feedback to improve the efficiency and safety of workers' movements. This leads to problems such as reduced work efficiency and increased physical strain if workers continue to perform inappropriate movements. Furthermore, the lack of a system to optimize workers' movements makes it difficult to provide appropriate guidance to individual workers.

The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes a means for measuring the user's physique, a means for recording specific movements performed by the user, an AI means for analyzing the recorded movements and comparing them with the movements of workers with a similar physique, a means for suggesting to the user movement modifications based on the comparison results, and a means for displaying suggestions for optimizing the movements of factory workers. This makes it possible to provide specific feedback to improve the efficiency and safety of workers' movements.

"User" refers to an individual who uses the system to have their physique and movements measured and analyzed.

"Physique" refers to the user's physical characteristics and measurements.

"Movement" refers to a specific physical movement or task performed by the user.

"Recording means" refers to devices or methods for saving user actions as video or data.

"AI means" refers to technology that uses artificial intelligence to analyze recorded movements and compare them with other movements.

"Deep learning" is a type of artificial intelligence that uses multi-layered neural networks to analyze and learn from data.

"Means for suggesting modifications" refers to a method or device for suggesting improvements to the user's behavior based on the analysis results.

A "factory worker" is a worker who performs specific tasks within a factory.

"Means for displaying optimization suggestions" refers to a device or method for visually presenting suggestions to the user for improving operational efficiency and safety.

"3D body scanning" is a technology for measuring a user's physique in three dimensions.

A system for implementing this invention includes means for measuring a user's physique, means for recording specific movements performed by the user, AI means for analyzing the recorded movements and comparing them with the movements of workers with a similar physique, means for suggesting to the user corrections to their movements based on the comparison results, and means for displaying suggestions for optimizing the movements of factory workers.

Hardware and software used

3D body scanning device: A device for measuring the user's physique in three dimensions.

Camera: A device used to record user actions. An example is the Logitech C920.

Smart glasses: A device that visually shows users how to correct their movements. An example is Google Glass.

Tablet: A device used to display operational corrections and optimization suggestions. An example is the iPad.

Server: A computer system for processing data and running AI models.

Software: Use programming libraries such as Python, OpenCV, and Keras.

Data processing and calculation

1. Acquisition of physique data: The server acquires the user's physique data using a 3D body scanning device. This data represents the user's physical characteristics and dimensions in three dimensions.

2. Recording of actions: The server uses a camera to record the user's actions. The recorded actions are saved as image data.

3. Movement analysis: The server inputs the recorded image data and physique data into the AI model and analyzes the movements. The AI model uses deep learning to compare the user's movements with those of workers with a similar physique.

4. Suggested modifications: The server will suggest modifications to the user's behavior based on the analysis results. These suggestions will be displayed on the smart glasses or tablet.

Specific examples

When a factory worker lifts a heavy object, their movements are recorded using a 3D body scan and camera, and then analyzed by AI. As a result of the analysis, suggestions are made for corrections such as the angle of the waist and the position of the hands when lifting. This allows workers to work efficiently and safely.

Example prompt

"Record factory workers lifting heavy objects, input this data into the AI along with 3D body scan data, compare it with efficient movements, and suggest corrections."

The above is an embodiment of the present invention.

The flow of the identification process in Application Example 1 will be explained using Figure 12.

Step 1:

The server uses a 3D body scanning device to obtain the user's physique data. The input includes the user's physical characteristics and dimensions. Data processing involves converting these characteristics and dimensions into a data format that represents them in three dimensions. The output is the user's physique data.

Step 2:

The server uses a camera to record the user's actions. The input includes the specific actions performed by the user. The data is processed by saving the actions as video data. The output is image data of the recorded actions.

Step 3:

The server inputs the recorded image data and physique data into the AI model and analyzes the movements. The input includes image data and physique data. To calculate the data, the AI model uses deep learning to compare the user's movements with those of workers with a similar physique. The output is the movement analysis results.

Step 4:

The server suggests behavioral modifications to the user based on the analysis results. The input includes the behavioral analysis results. Data processing generates specific suggestions for modifications. The output is the suggested modifications.

Step 5:

The server displays suggested corrections on the smart glasses or tablet. The input includes suggested corrections. Specific operations include generating data to visually display the suggestions and sending it to the smart glasses or tablet. The output displays suggested corrections that the user can visually confirm.

The above is the processing flow of this program.

(Example 2)

Next, Example 2 of Form Example 2 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Conventional motion analysis systems have struggled to accurately measure and record a user's physique and movements and suggest appropriate corrections. Furthermore, there was a lack of means to provide specific corrections to users, which led to problems with effective motion improvement.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, artificial intelligence means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, and means for presenting the modifications to the user. This makes it possible to accurately measure and record the user's physique and movements, suggest appropriate modifications, and present specific modifications to the user.

"User" refers to an individual who uses the system to measure and record their physique and movements and receive suggested movement modifications.

"Means for measuring body size" refers to devices or technologies that measure the dimensions and shape of a user's body in three dimensions.

"Means for recording specific actions" refers to devices or technologies that use cameras or sensors to record actions performed by users.

"Artificial intelligence means" refers to AI technology used to analyze recorded movement data and compare the user's movements with those of professional athletes.

"Means for suggesting corrections" refers to technology that extracts and suggests areas for improvement in the user's behavior based on the analysis results of artificial intelligence means.

"Means for presenting corrections" refers to a device or application that visually or audibly communicates suggested corrections to the user.

"3D body scanning" refers to technology that measures a user's physique in three dimensions.

"Deep learning" refers to a machine learning technique using multiple layers of neural networks used in artificial intelligence methods.

Form for implementing the invention

This invention relates to a system that measures a user's physique, records and analyzes specific movements, and suggests modifications to those movements. A specific embodiment of this system is described below.

User body measurements

Users measure their physique using a 3D body scanning device. Examples of such devices include a 3D scanner or motion capture system. Users follow the device's instructions and rotate 360 degrees to scan their physique. The device processes the scan data in real time and generates physique data for the user. The generated data is then sent to a server via wireless communication.

Recording actions

Next, the user performs a specific movement, such as batting or pitching in baseball. The user's movement is recorded using a camera or sensor. The hardware used may include a high-resolution camera or a motion capture system. The device generates the recorded video data or motion capture data and sends it to a server.

Analysis of movement

The server inputs the received physique data and movement data into an artificial intelligence model. This artificial intelligence model uses deep learning to compare the user's movements with those of professional athletes. The software used is a "deep learning framework." The server extracts corrections for the user's movements from the output of the artificial intelligence model.

Suggested behavior fixes

The server sends the extracted correction points to the device. The device then displays the received correction points in a dedicated application. The user can check the correction points through the application and learn specific ways to improve. For example, specific instructions such as "increase the angle at the start of the bat swing by 10 degrees" are displayed.

Specific examples

Example 1: Correcting baseball batting form

The user measures their physique using a 3D body scanning device and sends the data to a server. Then, a high-resolution camera is used to capture their batting form, and the video data is uploaded to the server. The server then analyzes the data using a deep learning framework and identifies areas for improvement. These areas are presented to the user via a dedicated application. The user then works to improve their batting form while viewing the application.

Example 2: Correcting pitching form

The user measures their physique using a 3D scanner and sends the data to the server. They then use a motion capture system to record their pitching form and upload the data to the server. The server then analyzes the data using a deep learning framework and extracts corrections that are presented to the user through a dedicated application. The user works to improve their pitching form while looking at the application.

Example prompt

Prompt 1: Baseball batting form

"Using the user's 3D body scan data and video data of their batting form as input, please generate an AI model that compares it with data from professional baseball players and suggests corrections."

Prompt 2: Pitching form

"Using the user's 3D body scan data and motion capture data of their pitching form as input, please compare it with data from professional baseball players and generate an AI model that suggests corrections."

The flow of the identification process in Example 2 will be explained using Figure 13.

Program processing flow

Step 1: Measure the user's body size

The user activates the 3D body scanning device. Following the device's instructions, the user rotates 360 degrees to scan their physique. The device processes the scan data in real time and generates the user's physique data. The generated data is sent to the server via wireless communication.

Input: User's physique scan data

Data processing: Real-time processing of scanned data

Output: User's physique data

Step 2: Record the action

Using a high-resolution camera and motion capture system, the user performs a specific movement, such as batting or pitching in baseball. The device records the movement and generates video data or motion capture data, which is then sent to the server.

Input: User action

Data processing: Recording movements and generating data

Output: Video data or motion capture data

Step 3: Analyze the Movement

The server inputs the received physique data and movement data into an artificial intelligence model. This artificial intelligence model uses deep learning to compare the user's movements with those of professional athletes. The server extracts corrections for the user's movements from the output of the artificial intelligence model.

Input: physique data, movement data

Data processing: Deep learning behavior comparison

Output: Behavior corrections

Step 4: Propose behavioral modifications

The server sends the extracted correction points to the device. The device then displays the received correction points in a dedicated application. The user can check the correction points through the application and learn specific ways to improve. For example, specific instructions such as "increase the angle at the start of the bat swing by 10 degrees" are displayed.

Input: Behavior corrections

Data processing: Preparing for corrections

Output: Present the corrections to the user

Add specific actions

Step 1: Measure the user's body size

The user activates the 3D body scanning device and follows the device's instructions to scan their physique. The scan data is processed in real time to generate the user's physique data, which is then transmitted to the server via wireless communication.

Step 2: Record the action

The user installs a high-resolution camera and films their baseball batting form. The camera records high-resolution video and saves it on the device. The device then compresses the saved video data and uploads it to the server.

Step 3: Analyze the Movement

The server inputs the received physique data and video data into a deep learning framework. The AI model compares the user's batting form with that of professional baseball players. Features such as joint angles and movement speed are used for the comparison. The server extracts corrections for the user's movement from the output of the AI model.

Step 4: Propose behavioral modifications

The server sends the extracted corrections to the device in JSON format. The device then displays the received corrections in a dedicated application. The user can review the corrections through the application and learn specific ways to improve. For example, specific instructions such as "increase the angle at the start of your bat swing by 10 degrees" are displayed.

(Application Example 2)

Next, we will explain Application Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Traditional optimization of factory robots relies on manual adjustments and experience, making it difficult to achieve efficient and accurate optimization. Furthermore, because individual optimization based on the user's physique and movements is not performed, general-purpose optimization methods often fail to produce sufficient results. This poses a risk of reduced factory productivity and quality.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, means for recording and analyzing the movements of robots used in factories, and means for suggesting modifications to optimize the robot's movement. This enables individual optimization based on the user's physique and movements, making it possible to efficiently and accurately optimize the movement of factory robots.

"User" means an individual or corporation that uses the system to measure and analyze their physique and movements.

"Physique" refers to the shape and dimensions of the user's body, as measured by means such as a 3D body scan.

"Specific actions" refer to specific movements or tasks performed by the user, which are recorded using cameras or sensors.

"Recording means" refers to devices or methods that use cameras, sensors, etc. to save user actions as data.

"AI means" refers to technology that uses artificial intelligence to analyze recorded movement data and compare the user's movements with those of professional athletes.

"Deep learning" is a type of artificial intelligence that uses multi-layered neural networks to analyze and learn from data.

A "professional athlete" is an athlete who specializes in a particular sport and is the subject to be compared with the user's movements.

"Behavior Modifications" are specific improvements suggested to optimize the user's behavior.

"Robots used in factories" are automated machinery and devices used on factory production lines and in workshops.

"Optimization" means adjusting something to the most efficient and effective state for a specific purpose.

The following hardware and software are used to implement this invention. Hardware requires a 3D scanner, camera, sensor, server, and user terminal (smartphone or tablet). Software uses Python, OpenCV, Keras, etc.

First, the user measures their physique using a 3D scanner. The 3D scanner captures the user's body shape and dimensions with high precision and saves them as digital data. This data is then sent to a server.

Next, the user performs a specific action. For example, if they are imitating the actions of a robot used in a factory, the action is recorded using a camera or sensor. The recorded action data is also sent to the server.

The server inputs the received 3D scan data and movement data into the AI means. The AI means analyzes this data using deep learning and compares the user's movements with those of professional athletes with a similar physique. Based on the comparison results, it suggests movement modifications to the user.

Furthermore, to optimize the operation of robots used in factories, robot operation data is also recorded and analyzed using AI methods. Based on the analysis results, corrections are suggested to optimize the robot's operation. These suggestions are displayed on the user's device.

As a concrete example, consider optimizing the operation of a welding robot used in a factory. A 3D scanner captures the robot's shape data, and cameras and sensors record its operation data. This data is input into an AI tool, and suggestions are made for adjusting the welding angle and speed based on the analysis results.

An example of a prompt to input into the generative AI model is, "Please input the welding robot's motion data along with the 3D scan data, and suggest optimal motion corrections."

As described above, this invention enables individual optimization based on the user's physique and movements, allowing the operation of factory robots to be optimized efficiently and with high precision.

The flow of the specific processing in Application Example 2 will be explained using Figure 14.

Step 1:

Users measure their body size using a 3D scanner.

Input: User's body

Data processing: A 3D scanner captures the user's body shape and dimensions with high precision and stores them as digital data.

Output: 3D scan data

Step 2:

The user performs a specific action, which is recorded using cameras and sensors.

Input: User action

Data processing: Cameras and sensors record user movements as video and sensor data.

Output: Operation data

Step 3:

The server receives the 3D scan data and movement data.

Input: 3D scan data, motion data

Data processing: The server receives this data and prepares it for input into the AI means.

Output: Prepared data

Step 4:

The server analyzes the 3D scan data and movement data using AI tools.

Input: Prepared data

Data processing: Using a deep learning model, we compare the user's movements with those of professional athletes with a similar physique.

Output: Analysis results

Step 5:

The server then suggests behavioral modifications to the user based on the analysis results.

Input: Analysis results

Data processing: Based on the analysis results, specific modifications are generated to optimize user behavior.

Output: Suggested fixes

Step 6:

Suggested fixes will be displayed on the user's device.

Enter: Suggested fix

Data processing: Convert the proposal content into a format for display on the user's device.

Output: Corrections displayed on the user's device

Step 7:

The movements of robots used in factories are recorded and sent to a server.

Input: Robot movement

Data processing: Cameras and sensors record the robot's movements as video and sensor data, which are then sent to a server.

Output: Robot movement data

Step 8:

The server analyzes the robot's operation data using AI means and suggests modifications for optimization.

Input: Robot movement data

Data processing: Using deep learning models, we analyze the robot's behavior and generate corrections for optimization.

Output: Suggested modifications to the robot's behavior

Step 9:

Suggestions for modifying the robot's behavior will be displayed on the user's device.

Input: Suggested modifications to the robot's behavior

Data processing: Convert the proposal content into a format for display on the user's device.

Output: Robot behavior corrections displayed on the user's device.

It is also possible to further combine an emotion engine that estimates the user's emotion. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

"Example 1"

In one embodiment of the present invention, a system is provided that includes a means for measuring a user's physique, a means for recording specific movements performed by the user, an AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, a means for suggesting movement modifications to the user based on the comparison results, and an emotion engine that recognizes the user's emotions. The emotion engine recognizes emotions from the user's facial expressions and tone of voice and provides this information to the AI means. The AI means suggests movement modifications that take into account not only the comparison between the user's movements and the movements of the professional athletes, but also the user's emotions. For example, if the user is feeling frustrated, the AI means takes this information into consideration and suggests modifications in a manner that is easy for the user to accept. In this way, it is possible to suggest movement improvements that take the user's emotions into account.

"Example 2"

In one embodiment of the present invention, a system is provided that includes a means for measuring a user's physique, a means for recording specific movements performed by the user, an AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, a means for suggesting movement modifications to the user based on the comparison results, and an emotion engine that recognizes the user's emotions. The emotion engine recognizes emotions from the user's facial expressions and tone of voice and provides this information to the AI means. The AI means suggests movement modifications that take into account not only the comparison between the user's movements and the movements of the professional athletes, but also the user's emotions. For example, if the user is feeling frustrated, the AI means takes this information into consideration and suggests modifications in a manner that is easy for the user to accept. In this way, it is possible to suggest movement improvements that take the user's emotions into account.

The processing flow for each example form is explained below.

"Example 1"

Step 1: Measure the user's physique using a 3D body scan.

Step 2: Record a specific action performed by the user (e.g., batting or pitching a baseball) using a video camera or motion sensor.

Step 3: The recorded movements are analyzed using AI tools and compared with the movements of professional athletes with similar physiques.

Step 4: Understand the user's emotions using an emotion engine that recognizes emotions from the user's facial expressions and tone of voice.

Step 5: The AI means suggests corrections to the user's movements, taking into account the results of comparing the user's movements with those of professional athletes and the user's emotions.

Step 6: The proposed behavioral modifications are presented to the user via an application on a device such as a smartphone or tablet.

"Example 2"

Step 1: Measure the user's physique using a 3D body scan.

Step 2: Record a specific action performed by the user (e.g., batting or pitching a baseball) using a video camera or motion sensor.

Step 3: The recorded movements are analyzed using AI tools and compared with the movements of professional athletes with similar physiques.

Step 4: Understand the user's emotions using an emotion engine that recognizes emotions from the user's facial expressions and tone of voice.

Step 5: The AI means suggests corrections to the user's movements, taking into account the results of comparing the user's movements with those of professional athletes and the user's emotions.

Step 6: The proposed behavioral modifications are presented to the user via an application on a device such as a smartphone or tablet.

(Example 1)

Next, Example 1 of Form Example 1 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Traditional movement improvement systems make suggestions based on the user's physique and movement data, but because they don't take the user's emotions into account, users may find it difficult to accept the suggestions. Furthermore, since the corrections to movement are not specific, it is difficult for users to understand how to improve.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, artificial intelligence means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, means for recognizing the user's emotions, and means for suggesting movement modifications taking into account the emotion data. This makes it possible to suggest movement improvements that take into account the user's physique and emotions.

"Means for measuring the user's physique" refers to devices or methods that measure the user's physical characteristics and acquire them as digital data.

"Means for recording specific actions performed by a user" refers to devices or methods that use video or sensors to record specific actions performed by a user as digital data.

"Artificial intelligence means" refers to software or systems that analyze recorded data and use specific algorithms to compare the user's movements with other data (e.g., the movements of professional athletes).

"Means for suggesting movement corrections" refers to devices or methods that suggest movement improvements and correction methods to the user based on the analysis results.

"Means for recognizing user emotions" refers to devices or methods that analyze emotions from the user's facial expressions, tone of voice, etc., and acquire them as digital data.

"Means for suggesting movement modifications taking into account emotional data" refers to a device or method that suggests movement modifications in a more acceptable form based on the user's emotional data.

"3D body scanning" is a technology that scans the user's body in three dimensions and obtains detailed physique data.

"Deep learning" is an artificial intelligence technology that uses multi-layered neural networks to analyze data and learn patterns and features.

This invention is a system that analyzes a user's physique and movements, compares them with the movements of professional athletes, and suggests movement modifications to the user. Furthermore, by recognizing the user's emotions and making suggestions that take their emotional data into consideration, it is possible to present movement modifications in a way that is easy for the user to accept.

Hardware and software used

1. User body measurements

Users measure their physique using equipment that provides a 3D body scan. This equipment may include, for example, a 3D scanner or a dedicated body scan application. This obtains detailed physique data such as the user's height, weight, and body fat percentage.

2. Recording actions

Users use their smartphone camera or a dedicated motion capture device to record specific movements (e.g., batting or pitching in baseball). The recorded data includes detailed movement information such as swing speed and angle.

3. Sending operation data

The device transmits recorded movement and physique data to a server. This transmission occurs in real time over the Internet, and the data is encrypted using a secure communication protocol.

4. Movement Analysis

The server inputs the received data into an AI model, which uses deep learning algorithms to compare the user's movements with those of professional athletes. The software used is a deep learning framework such as TensorFlow or PyTorch. For example, it calculates how much the user's swing speed differs from the average speed of professional athletes.

5. Emotional Recognition

The device uses an emotion engine that analyzes the user's facial expressions and tone of voice in real time. This emotion engine determines whether the user is frustrated or satisfied and sends that information to a server. The software used is an emotion recognition API (e.g., Microsoft Azure's Emotion API).

6. Suggestions for behavioral modifications

Based on the comparison of the movements and the emotional data, the server suggests to the user how to modify their movements. For example, if the user is feeling frustrated, the server might suggest, "Maybe if you just change the way you grip the bat a little, your swing will become smoother." The user is notified of these suggestions through an application on their device, such as a smartphone or tablet.

Specific examples

Example 1: Improving baseball batting form

The user measures their physique using a 3D scanner and records their batting form with their smartphone camera. The device then sends the recorded data to a server, which then inputs the data into an AI model and compares it with the form of professional baseball players. If the emotion engine detects the user's frustration, the server will make suggestions such as, "Maybe if you just change the way you grip the bat a little, your swing will become smoother." The user is notified of these suggestions via a smartphone application.

Example prompt for a generative AI model

"Using the user's 3D body scan data and video of their batting form as input, please compare it with the form of a professional baseball player and suggest improvements while taking into account the user's emotions."

In this way, it becomes possible to suggest movement improvements that take into account the user's physique and emotions.

The flow of the identification process in Example 1 will be explained using Figure 15.

Step 1:

Users measure their physique using a device that provides a 3D body scan. As input, a 3D scanner is used to scan the user's body. As output, detailed physique data such as the user's height, weight, and body fat percentage is generated. This data is stored on the device.

Step 2:

Users use their smartphone's camera or a dedicated motion capture device to record specific movements (e.g., batting or pitching in baseball). As input, the user's movements are captured by the camera or sensors. As output, detailed movement data such as swing speed and angle is generated. This data is stored on the device.

Step 3:

The device sends the recorded movement data and physique data to the server. The movement data and physique data stored on the device are used as input. As output, this data is sent to the server via the internet. The data is encrypted and uses a secure communication protocol.

Step 4:

The server inputs the received data into the AI model. The movement data and physique data sent to the server are used as input. The output is the results of the AI model comparing the user's movements with those of professional athletes. This AI model uses deep learning algorithms and utilizes deep learning frameworks such as TensorFlow and PyTorch.

Step 5:

The device uses an emotion engine that analyzes the user's facial expressions and tone of voice in real time. As input, the user's facial and voice data is captured through a camera and microphone. As output, emotional data is generated, such as whether the user is frustrated or satisfied. This data is sent to a server.

Step 6:

The server suggests movement corrections to the user based on the movement comparison results and emotional data. The AI model's comparison results and emotional data are used as input. The output is movement corrections generated for the user. For example, if the user is feeling frustrated, the server might suggest, "Maybe your swing will become smoother if you just change the way you grip the bat slightly." The suggestion is notified to the user through an application on their device, such as a smartphone or tablet.

(Application Example 1)

Next, we will explain Application Example 1 of Form Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

While conventional motion analysis systems can analyze a user's physique and movements, they lack the ability to suggest motion modifications that take the user's emotions into account. Furthermore, when optimizing the movements of robots working in factories, they lack the ability to monitor movement efficiency and error rates in real time and provide improvement suggestions. This leads to issues such as reduced movement efficiency for users and robots, preventing optimal performance.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, an emotion engine for recognizing the user's emotions, means for suggesting movement modifications taking into account information from the emotion engine, means for optimizing the robot's movements, means for monitoring the robot's movement efficiency and error rate, and means for making improvement suggestions in real time. This makes it possible to suggest movement modifications taking the user's emotions into account, and further enables the movement efficiency and error rate of robots working in factories to be monitored in real time, enabling optimal movement performance to be achieved.

"Means for measuring the user's physique" refers to devices or technologies for accurately measuring the dimensions and shape of the user's body.

"Means for recording specific actions performed by a user" refers to devices or technologies that use cameras, sensors, etc. to record specific actions performed by a user.

"AI means for analyzing recorded movements and comparing them with the movements of professional athletes with similar physiques" refers to artificial intelligence technology that analyzes recorded user movement data and compares it with the movement data of professional athletes with similar physiques.

"Means for suggesting to the user points for correction of movements based on the comparison results" refers to devices or technologies that suggest points for improvement or correction of movements to the user based on the results of movement comparison by AI.

The "emotion engine that recognizes user emotions" is a technology that identifies emotions from the user's facial expressions, tone of voice, etc.

"Means for suggesting behavioral modifications by taking into account information from an emotion engine" refers to devices or technologies that suggest behavioral modifications based on the user's emotional information recognized by an emotion engine.

"Means for optimizing robot operation" refers to devices and technologies that optimize the operation of robots working in factories to ensure that they are efficient and effective.

"Means for monitoring a robot's operational efficiency and error rate" refers to devices and technologies that monitor a robot's operational efficiency and error rate in real time.

"Means for making improvement suggestions in real time" refers to devices and technologies that analyze robot operation data in real time and immediately suggest areas for improvement.

A system for implementing this invention includes means for measuring a user's physique, means for recording specific movements performed by the user, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting to the user movements modifications based on the comparison results, an emotion engine for recognizing the user's emotions, means for suggesting movements modifications taking into account information from the emotion engine, means for optimizing the robot's movements, means for monitoring the robot's movement efficiency and error rate, and means for making improvement suggestions in real time.

Hardware and software configuration

Hardware:

3D body scanning device

Camera (e.g., Logitech C920)

Computer (e.g., Intel Core i7, 16GB RAM)

Software:

OpenCV: Motion recording and image processing

Keras: Loading and predicting deep learning models

EmotionRecognizer: Emotion Recognition Library

Processing flow

The server first acquires the user's physique data using a 3D body scanning device. Then, it uses a camera to record specific movements performed by the user. The recorded movement data is preprocessed using OpenCV and analyzed using a deep learning model using Keras. The analysis results are compared with the movement data of professional athletes with similar physiques.

Furthermore, EmotionRecognizer is used to recognize the user's emotions and suggests behavioral modifications taking into account information from the emotion engine. The suggested modifications are presented to the user.

For robots working in factories, measures are used to optimize the robot's operations. The robot's operating efficiency and error rate are monitored in real time, and suggestions for improvement are made. This makes it possible to optimize the robot's operating performance.

Specific examples

For example, if a robot assembling parts in a factory is moving slowly, the AI will analyze its movements and suggest correcting the angle and speed of its movements. Furthermore, if the robot is making frequent errors, it will determine through emotion recognition that it is feeling frustrated and suggest that it move more slowly.

Example prompt

"To optimize the movements of robots that assemble parts in factories, design a system that uses 3D body scans and cameras to record their movements, analyzes them with AI, and suggests optimal movement patterns. Additionally, add functionality to monitor the robot's movement efficiency and error rate and suggest improvements in real time."

The flow of the specific processing in Application Example 1 will be explained using Figure 16.

Step 1:

The server acquires the user's physique data using a 3D body scanning device. As input, it scans the user's body dimensions and shape, and generates 3D physique data as output. This data is used to accurately determine the user's physique.

Step 2:

The server uses a camera to record specific actions performed by the user. As input, it captures the actions performed by the user in real time, and as output, it generates video data of the actions. This video data is used for subsequent action analysis.

Step 3:

The server preprocesses the recorded motion data using OpenCV. It receives motion video data as input and generates preprocessed image data as output. This preprocessing includes image resizing and noise removal.

Step 4:

The server analyzes the preprocessed image data using a deep learning model using Keras. It receives the preprocessed image data as input and extracts movement features as output. These features are used to analyze the user's movements in detail.

Step 5:

The server compares the extracted features with the motion data of professional athletes with a similar physique. It receives the user's motion features and the motion data of the professional athletes as input, and generates the comparison results as output. These comparison results are used to identify areas in the user's motion that need to be corrected.

Step 6:

The server uses EmotionRecognizer to recognize the user's emotions. As input, it captures the user's facial expressions and tone of voice while in action, and generates emotional data as output. This emotional data is used to understand the user's emotional state.

Step 7:

The server takes into account information from the emotion engine to suggest behavioral modifications. It receives the comparison results and emotion data as input, and generates behavioral modifications for the user as output. These modifications are suggested taking into account the user's emotional state.

Step 8:

The server executes a process to optimize the operation of robots working in factories. It receives robot operation data as input and generates optimized operation patterns as output. This optimization is performed to improve the robot's operation efficiency.

Step 9:

The server monitors the robot's operating efficiency and error rate in real time. It receives the robot's operating data in real time as input and generates monitoring results of operating efficiency and error rate as output. These monitoring results are used to understand the robot's operating status.

Step 10:

The server makes improvement suggestions in real time. It receives monitoring results as input and generates improvement suggestions as output. These improvement suggestions are made to optimize the robot's operational performance.

(Example 2)

Next, Example 2 of Form Example 2 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Previous movement improvement systems struggled to accurately measure a user's physique and movements and provide appropriate feedback. Furthermore, they didn't take the user's emotions into account when proposing movement improvements, resulting in a lack of feedback that was easy for the user to accept. This limited the effectiveness of the system's movement improvements.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, means for transmitting the recorded movements, artificial intelligence means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, means for recognizing the user's emotions, and means for suggesting movement modifications taking the recognized emotions into consideration. This makes it possible to accurately measure the user's physique and movements and provide appropriate feedback. Furthermore, because suggestions for movement improvement are made taking the user's emotions into consideration, feedback is provided in a form that makes the suggestions more receptive to the user, improving the effectiveness of movement improvement.

"Means for measuring the user's physique" refers to devices or technologies that measure the dimensions and shape of the user's body in three dimensions.

"Means for recording specific actions performed by a user" refers to devices or technologies that use video or sensors to record specific actions performed by a user.

"Means for transmitting recorded behavior" refers to communication means for transmitting recorded behavior data to a server or other device.

"Artificial intelligence means" refers to artificial intelligence technology used to analyze recorded movement data and compare the user's movements with those of professional athletes.

"Means for suggesting movement corrections" refers to devices or technologies that suggest movement improvements to the user based on the analysis results of artificial intelligence means.

"Means for recognizing user emotions" refers to devices or technologies for recognizing emotions from the user's facial expressions, tone of voice, etc.

"Means for suggesting behavioral modifications taking into account recognized emotions" refers to devices or technologies that suggest behavioral modifications in a way that is easy for the user to accept, based on the user's emotional information.

This invention is a system that measures a user's physique, records specific movements, analyzes those movements, and suggests corrections. The system can recognize the user's emotions and take them into consideration when suggesting corrections to movements.

First, the user measures their physique using a 3D body scanning device. Specifically, devices such as the "Structure Sensor" or "Kinect" are used. This allows the user's physique data to be acquired and stored on the device.

Next, the user performs a specific movement (e.g., batting or pitching a baseball). This movement is recorded using a camera or sensor (e.g., a GoPro camera or a Vicon motion capture system). The recorded movement data is saved on the device.

The device sends recorded movement and physique data to a server. The data is sent using a secure protocol (e.g., HTTPS).

The server inputs the received data into an AI model, which is built using a deep learning framework (e.g., TensorFlow or PyTorch) and compares the user's movements with those of professional athletes with a similar physique. The server generates comparison results and identifies movement modifications.

The server sends the identified corrections to the device, which then presents the corrections to the user through an application on the device, such as a smartphone or tablet. For example, specific advice such as "You should increase the angle of your bat swing a little more" is displayed.

In addition, the device inputs the user's facial expressions and tone of voice into an emotion engine. The emotion engine uses Affectiva and the Microsoft Azure Emotion API to recognize the user's emotions in real time. The recognition results are stored on the device.

The device sends the recognized emotional information to the server. The server reevaluates the AI model taking the emotional information into account and suggests modifications that the user can easily accept. For example, if the user feels frustrated, the server will generate a suggestion such as "First, relax, and then try to be aware of your release points," and send it to the device. The device will then display this suggestion to the user.

Specific examples

Example 1: Improving baseball batting form

The user uses the "Structure Sensor" to measure their physique and a GoPro camera to record their batting form. The device then sends this data to a server. The server uses TensorFlow to compare the batting form with that of professional baseball players and suggests, via a smartphone app, that the user "increase the angle of the bat's swing a little more." If the user is feeling frustrated, the emotion engine recognizes this and the server suggests, "First, relax, and then try to be more conscious of your swing angle."

Example 2: Improving pitching form

The user uses Kinect to measure their physique and a Vicon motion capture system to record their pitching form. The device then sends this data to a server. The server uses PyTorch to compare the form with that of professional pitchers and suggests, via a tablet app, that the release point should be moved a little further forward. If the user is feeling frustrated, the emotion engine recognizes this and the server suggests, "First, relax, and then try to focus on the release point."

Example prompt

"The user uses a 3D body scanning device to measure their physique and a camera to record their batting form. The AI uses TensorFlow to compare their form with that of professional baseball players and suggests corrections via a smartphone app. If the user is feeling frustrated, the emotion engine recognizes this and the AI suggests corrections in a way that is easy for the user to accept."

The flow of the identification process in Example 2 will be explained using Figure 17.

Step 1:

Users measure their own physique using a 3D body scanning device. Specifically, users acquire physique data using a "Structure Sensor" or "Kinect." The input is the dimensions and shape of the user's body, and the output is physique data as a 3D model. This data is stored on the device.

Step 2:

The user performs a specific movement (e.g., batting or pitching a baseball). The user records the movement using a GoPro camera or a Vicon motion capture system. The input is the user's movement, and the output is high-resolution video data. This data is stored on the device.

Step 3:

The device sends recorded movement data and physique data to the server. The input is movement data and physique data, and the output is data sent to the server. Data is sent using a secure protocol (e.g., HTTPS).

Step 4:

The server inputs the received data into an AI model. The AI model is built using TensorFlow and PyTorch and compares the user's movements with those of professional athletes with a similar physique. The input is movement data and physique data, and the output is the comparison results, including suggestions for movement correction.

Step 5:

The server sends the identified corrections to the terminal. The input is the comparison result, and the output is the corrections sent to the terminal. The terminal then presents the corrections to the user through an application on a device such as a smartphone or tablet. For example, specific advice such as "You should increase the angle of your bat swing a little more" is displayed.

Step 6:

The device inputs the user's facial expressions and tone of voice into the emotion engine. The emotion engine uses "Affectiva" and "Microsoft Azure Emotion API" to recognize the user's emotions in real time. The input is the user's facial expressions and tone of voice, and the output is recognized emotional information. This information is stored on the device.

Step 7:

The device sends the recognized emotional information to the server. The input is emotional information, and the output is the transmission of emotional information to the server. The server reevaluates the AI model taking the emotional information into account and suggests corrections that are easy for the user to accept. For example, if the user is feeling frustrated, the server will generate a suggestion such as "First, relax, and then try to be aware of your release point," and send it to the device. The device will display this suggestion to the user.

(Application Example 2)

Next, we will explain Application Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Conventional motion analysis systems record a user's physique and movements, compare them with those of professional athletes, and suggest corrections, but they were not designed to optimize the movements of robots working in factories. They also lacked the ability to detect abnormalities in the robots and suggest corrections to their movements based on that information. This made it difficult to efficiently manage the movements of robots in factories.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, means for recording the robot's movements and proposing optimal movement patterns, and means for detecting abnormalities in the robot and proposing movement modifications based on that information. This not only improves the user's movements, but also enables efficient movement management and optimization of robots within factories.

"User" refers to an individual who uses the system to measure and analyze their physique and movements.

"Physique" refers to the user's physical characteristics and measurements.

"Specific movements" refers to specific actions or movements performed by the user (e.g., batting or pitching form in baseball).

"Recording means" refers to devices or methods that use cameras, sensors, etc. to save the actions of users or robots as digital data.

"AI means" refers to a system that uses artificial intelligence to analyze data and output results according to a specific purpose.

"Deep learning" refers to a machine learning technique that uses multi-layered neural networks to learn data and extract complex patterns and features.

A "professional athlete" refers to an athlete who has high skills in a particular sport and engages in the sport professionally.

"Behavior modifications" refers to specific changes or adjustments proposed to improve the user's or robot's behavior.

"Robot" refers to an automated mechanical device that performs work in a factory.

"Abnormal" refers to conditions such as vibrations or temperature rises that deviate from the robot's normal operation.

"Means of sensing" refers to methods or devices that use sensors or monitoring devices to detect abnormalities.

"Optimal movement pattern" refers to a series of steps or methods of robot movement proposed to perform a task efficiently and effectively.

We will now explain the mode for implementing this invention. First, a 3D body scanning device is used to measure the user's physique. Specifically, a 3D scanner such as Intel RealSense is used to acquire the user's physique data. This data is used to accurately record the user's physical characteristics and dimensions.

Next, cameras and sensors are used to record specific actions performed by the user. For example, an Intel RealSense camera is used to record user actions with high accuracy. This recorded data is then analyzed using AI tools, which will be described later.

The recorded movement data is analyzed using AI tools that use deep learning, such as TensorFlow and Keras. The AI tools compare the user's movements with those of professional athletes with a similar physique, and suggest movement corrections. These suggestions are presented to the user via an application on their smartphone or tablet.

Furthermore, in order to optimize the movements of robots working in factories, it includes a means for recording the movements of the robots and proposing optimal movement patterns. The movements of the robots are recorded using a 3D scanner or camera and analyzed by the AI means. The AI means proposes optimal movement patterns and makes the robots' movements more efficient.

In addition, vibration and temperature sensors are used to detect abnormalities in the robot. OpenCV and PyTorch are used to detect abnormalities and suggest corrections to behavior based on that information. This optimizes the robot's behavior and enables it to work more efficiently.

For example, if a robot assembles parts in a factory and its movements slow down or abnormal vibrations occur, the system records the movement and the AI suggests the optimal movement pattern. If the emotion engine detects an abnormality, it will reflect this in the suggestions for correcting the movement.

An example of a prompt to input to the generative AI model is as follows:

"To optimize the movements of robots assembling parts in a factory, AI should suggest optimal movement patterns using data recorded by 3D scans and cameras. It should also detect abnormal vibrations and temperature increases in the robot and suggest movement corrections based on that information."

The flow of the specific processing in Application Example 2 will be explained using Figure 18.

Step 1:

The user uses a 3D body scanning device to obtain physique data.

Input: User's body

Output: 3D body scan data

How it works: The user stands in front of the 3D scanner, which scans their entire body and generates digital data.

Step 2:

The user's specific actions are recorded using cameras and sensors.

Input: User action

Output: Operation record data

Specific actions: The user performs a specific action (e.g., batting or pitching) in front of the camera, and the camera records that action with high accuracy.

Step 3:

The server analyzes the recorded movement data using AI methods.

Input: Motion record data, 3D body scan data

Output: Motion analysis results

Specific operation: The server uses TensorFlow and Keras to input motion recording data and 3D body scan data into an AI model to analyze the user's movements.

Step 4:

Based on the results of the behavior analysis, the server suggests behavioral modifications to the user.

Input: Motion analysis results

Output: Proposed behavior changes

Specific actions: Based on the analysis results, the server generates specific corrections to improve the user's behavior and presents them to the user via a smartphone or tablet application.

Step 5:

The robot's movements are recorded using a 3D scanner and camera.

Input: Robot movement

Output: Robot operation record data

Specific movements: As the robot performs its tasks, 3D scanners and cameras record its movements.

Step 6:

The server analyzes the robot's recorded operation data using AI methods.

Input: Robot movement record data

Output: Robot movement analysis results

Specific operation: The server uses TensorFlow and Keras to input recorded robot movement data into an AI model and analyze the robot's movement.

Step 7:

The server detects an abnormality in the robot.

Input: Robot vibration data, temperature data

Output: Anomaly detection results

Specific operation: The server analyzes data from vibration and temperature sensors using OpenCV and PyTorch to detect abnormalities.

Step 8:

The server proposes optimal movement patterns based on the results of the robot's movement analysis and anomaly detection.

Input: Robot behavior analysis results, anomaly detection results

Output: Optimal motion pattern proposal

Specific behavior: The server integrates the analysis results and anomaly detection results, generates specific behavior patterns to optimize the robot's behavior, and applies them to the robot.

The specific processing unit 290 transmits the results of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the results of the specific processing. The microphone 238 acquires audio indicating the user input regarding the results of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

Data generation model 58 is a so-called generative AI (Artificial Intelligence) model. An example of data generation model 58 is generative AI such as ChatGPT (Internet search <URL: https://openai.com/blog/chatgpt>). Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. Data generation model 58 performs inference on the input inference data in accordance with the instructions indicated by the prompt, and outputs the inference results in the form of data such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization.

Another example of generative AI is Gemini (Internet search <URL: https://gemini.google.com/?hl=ja>).

In the above embodiment, an example was given in which the specific processing was performed by the data processing device 12, but the technology disclosed herein is not limited to this, and the specific processing may also be performed by the robot 414.

The emotion identification model 59, which serves as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see Figure 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

Figure 9 shows an emotion map 400 on which multiple emotions are mapped. In emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both emotions and mental states. Emotions that are generally generated from reactions that occur in the brain are arranged on the left side of the concentric circles. Emotions that are generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged above and below the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the top side of the concentric circles, and the emotion of "discomfort" is arranged on the bottom side. In this way, emotion map 400 maps multiple emotions based on the structure by which emotions are generated, with emotions that tend to occur simultaneously being mapped close together.

These emotions are distributed in the 3 o'clock direction on emotion map 400, and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness takes precedence over internal sensations, resulting in a sense of calm.

The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible (expressed in behavior) the emotion becomes.

Here, human emotions are based on various balances such as posture and blood sugar levels, and when these balances deviate from the ideal, it indicates discomfort, and when they approach the ideal, it indicates pleasure. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and remaining battery life, so that when these balances deviate from the ideal, it indicates discomfort, and when they approach the ideal, it indicates pleasure. Emotion maps may be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and emotional brain physiological signal analysis systems, Tokushima University, doctoral dissertation: https://ci.nii.ac.jp/naid/500000375379). The left half of the emotion map is lined with emotions belonging to an area called "reaction," where sensation is dominant. The right half of the emotion map is lined with emotions belonging to an area called "situation," where situational awareness is dominant.

The emotion map defines two emotions that encourage learning. One is the negative emotion around the middle of "repentance" or "reflection" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot experiences positive feelings such as "I want more" or "I want to know more."

The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are close to each other have similar values, as in the emotion map 900 shown in Figure 10. Figure 10 shows an example in which multiple emotions, such as "relieved," "calm," and "reassuring," have similar emotion values.

In the above embodiment, an example was given in which a specific process is performed by a single computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may also be performed by multiple computers, including computer 22.

In the above embodiment, an example was described in which the specific processing program 56 is stored in the storage 32, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

It is not necessary to store the entire specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entire specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

The following types of processors can be used as hardware resources for executing specific processes. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource for executing specific processes by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which are processors with a circuit configuration designed specifically for executing specific processes. All processors have built-in or connected memory, and all use the memory to execute specific processes.

The hardware resource that executes the specific processing may be composed of one of these various processors, or may be composed of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). The hardware resource that executes the specific processing may also be a single processor.

As an example of a configuration using a single processor, first, there is a configuration in which one processor is configured using a combination of one or more CPUs and software, and this processor functions as a hardware resource that executes specific processing. Second, there is a configuration in which a processor is used to realize the functions of an entire system, including multiple hardware resources that execute specific processing, on a single IC chip, as typified by SoC (System-on-a-chip). In this way, specific processing is realized using one or more of the various processors listed above as hardware resources.

More specifically, the hardware structure of these various processors can be an electrical circuit that combines circuit elements such as semiconductor devices. Furthermore, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps can be added, or the processing order can be rearranged, all within the scope of the spirit of the invention.

The above-described written content and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above explanation of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or substitutions may be made to the above-described written content and illustrations, as long as they do not deviate from the spirit of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described written content and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

All publications, patent applications, and technical standards mentioned in this specification are incorporated by reference herein to the same extent as if each individual publication, patent application, and technical standard was specifically and individually indicated to be incorporated by reference.

The following is further disclosed regarding the above embodiments.

(Claim 1)
The system includes a means for measuring a user's physique, a means for recording specific movements performed by the user, an AI means for analyzing the recorded movements and comparing them with movements of professional athletes with a similar physique, and a means for suggesting to the user corrections to the movements based on the comparison results.

(Claim 2)
10. The system of claim 1, wherein the means for measuring the user's physique uses a three-dimensional body scan.

(Claim 3)
2. The system of claim 1, wherein the AI means uses deep learning to compare the user's movements with those of professional athletes.

(Claim 4)
2. The system of claim 1, wherein the AI means includes an emotion engine that recognizes emotions of a user.

(Claim 5)
5. The system according to claim 4, wherein the emotion engine recognizes emotions from the user's facial expressions and tone of voice.

(Claim 6)
5. The system according to claim 4, wherein the means for suggesting to the user points for correcting the user's actions based on the comparison result makes the suggestion taking into consideration the user's feelings.

"Example 1"

(Claim 1)
a means for measuring the user's physique;
a means of recording certain actions taken by the user;
means for transmitting the recorded motion data and physique data to a server;
AI means to analyze recorded movements and compare them with those of professional athletes of similar builds;
A means for suggesting operational modifications to the user based on the comparison results;
a means for displaying the proposed fixes on the user's device; and
A system including:

(Claim 2)
10. The system of claim 1, wherein the means for determining the user's physique uses a three-dimensional body scan.

(Claim 3)
2. The system of claim 1, wherein the AI means uses deep learning to compare the user's movements with those of professional athletes.

"Application Example 1"

(Claim 1)
a means for measuring the user's physique;
a means of recording certain actions taken by the user;
An AI means for analyzing the recorded movements and comparing them with the movements of workers with similar physiques;
A means for suggesting operational modifications to the user based on the comparison results;
means for displaying suggestions for optimizing the behavior of factory workers;
A system including:

(Claim 2)
10. The system of claim 1, wherein the means for measuring the user's physique uses a three-dimensional body scan.

(Claim 3)
2. The system of claim 1, wherein the AI means uses deep learning to compare the user's actions with the worker's actions.

"Example 2"

(Claim 1)
a means for measuring the user's physique;
a means of recording certain actions taken by the user;
artificial intelligence means for analyzing the recorded movements and comparing them with the movements of professional athletes of similar build;
A means for suggesting operational modifications to the user based on the comparison results;
The system includes means for presenting said modifications to a user.

(Claim 2)
10. The system of claim 1, wherein the means for determining the user's physique uses a three-dimensional body scan.

(Claim 3)
2. The system of claim 1, wherein the artificial intelligence means uses deep learning to compare the user's movements with those of professional athletes.

"Application Example 2"

(Claim 1)

a means for measuring the user's physique;
a means of recording certain actions taken by the user;
AI means for analyzing the recorded movements and comparing them with the movements of professional athletes of similar physique;
A means for suggesting operational modifications to the user based on the comparison results;
a means for recording and analyzing the behavior of robots used in the factory;
The system includes a means for suggesting modifications to optimize the robot's behavior.

(Claim 2)
10. The system of claim 1, wherein the means for measuring the user's physique uses a three-dimensional body scan.

(Claim 3)
2. The system of claim 1, wherein the AI means uses deep learning to compare the user's movements with those of professional athletes.
"Example 1 of combining emotion engines"

(Claim 1)
a means for measuring the user's physique;
a means of recording certain actions taken by the user;
artificial intelligence means for analyzing the recorded movements and comparing them with the movements of professional athletes of similar build;
A means for suggesting operational modifications to the user based on the comparison results;
a means of recognizing a user's emotions;
The system includes a means for suggesting behavioral modifications taking into account emotional data.

(Claim 2)
10. The system of claim 1, wherein the means for determining the user's physique uses a three-dimensional body scan.

(Claim 3)
2. The system of claim 1, wherein the artificial intelligence means uses deep learning to compare the user's movements with those of professional athletes.
"Application example 1 when combining emotion engines"

(Claim 1)
a means for measuring the user's physique;
a means of recording certain actions taken by the user;
AI means for analyzing the recorded movements and comparing them with the movements of professional athletes of similar physique;
A means for suggesting operational modifications to the user based on the comparison results;
An emotion engine that recognizes user emotions;
a means for suggesting behavioral modifications taking into account information from the emotion engine;
means for optimizing the behavior of the robot;
A means for monitoring the robot's operational efficiency and error rate;
A system that includes a means to provide improvement suggestions in real time.

(Claim 2)
10. The system of claim 1, wherein the means for measuring the user's physique uses a three-dimensional body scan.

(Claim 3)
2. The system of claim 1, wherein the AI means uses deep learning to compare the user's movements with those of professional athletes.
"Example 2 when combining emotion engines"

(Claim 1)
a means for measuring the user's physique;
a means of recording certain actions taken by the user;
means for transmitting the recorded actions;
artificial intelligence means for analyzing the recorded movements and comparing them with the movements of professional athletes of similar build;
A means for suggesting operational modifications to the user based on the comparison results;
a means of recognizing a user's emotions;
The system includes a means for suggesting behavioral modifications taking into account the recognized emotions.

(Claim 2)
10. The system of claim 1, wherein the means for determining the user's physique uses a three-dimensional body scan.

(Claim 3)
2. The system of claim 1, wherein the artificial intelligence means uses deep learning to compare the user's movements with those of professional athletes.
"Application example 2 when combining emotion engines"

(Claim 1)
a means for measuring the user's physique;
a means of recording certain actions taken by the user;
AI means for analyzing the recorded movements and comparing them with the movements of professional athletes of similar physique;
A means for suggesting operational modifications to the user based on the comparison results;
A means for recording the robot's movements and proposing optimal movement patterns;
A means to detect abnormalities in the robot and suggest corrections to its behavior based on that information;
A system including:

(Claim 2)
10. The system of claim 1, wherein the means for measuring the user's physique uses a three-dimensional body scan.

(Claim 3)
2. The system of claim 1, wherein the AI means uses deep learning to compare the user's movements with those of professional athletes.

(Claim 1)
a measuring means for measuring the user's physique;
a recording means for recording a specific action performed by the user;
an emotion engine that recognizes the emotion of the user;
a means for transmitting the physique data measured by the measuring means and the motion data recorded by the recording means to a server;
a comparison means for comparing the user's motion with the motion of a specific professional athlete based on the physique data and the motion data, and inputting a prompt sentence that suggests corrections to the user's motion into a generation AI model, thereby comparing the user's physique with that of the professional athlete, and, based on the comparison result, comparing the recorded user's motion with the motion of the specific professional athlete whose physique is similar to that of the user;
means for suggesting a modification of an action to the user based on the result of the comparison and taking into account information from the emotion engine;
means for displaying the proposed modifications on the user's terminal;
A system including:

(Claim 2)
The user is a factory worker,
The system of claim 1 , wherein the user actions are actions of the factory worker.

(Claim 3)
The system of claim 1 , wherein the comparison means uses deep learning to compare the user's movements with the professional athlete's movements.

10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset type terminal 414 Robot

Claims (1)

means for acquiring physique data obtained by measuring the physique of a user from a terminal of the user ;
means for acquiring image data representing a specific action performed by the user from the terminal ;
A means for inputting the image data into a generative AI model, and the generative AI model outputting the user's motion feature amount;
a means for inputting a prompt sentence to the generative AI model, instructing the model to suggest corrections to the user's movements based on the physique data and the movement feature amount, and for the generative AI model to compare the movement feature amount with the movements of a professional athlete having a physique similar to that of the user, and outputting a comparison result including corrections to the user's movements;
A means for presenting on the terminal the correction points for the user's actions output by the generative AI model;
a means for inputting at least one of the facial expression and tone of voice of the user acquired via the terminal into an emotion engine, and for the emotion engine to recognize the emotion of the user;
means for reevaluating points for correcting the user's actions based on the recognized emotions of the user, and presenting the reevaluated points for correcting the actions on the terminal in a form that is easy for the user to accept;
A system including: