JP7834816B2 - system - Google Patents

Description

The technology disclosed herein relates to a system.

Patent Document 1 discloses a persona chatbot control method performed by at least one processor, comprising the steps of: receiving a user utterance; adding the user utterance to a prompt containing instructions 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 that responds to the user utterance.

Japanese Patent Publication No. 2022-180282

With current technology, the means by which visually impaired people can receive support such as directions or having books read to them when they go out are limited, and there is room for improvement.

The system according to this embodiment is intended to provide visually impaired individuals with support such as directions and reading assistance when they are out and about.

The system according to this embodiment comprises an acquisition unit, a generation unit, a provision unit, a reception unit, a response generation unit, a response provision unit, a main reception unit, and a read-aloud provision unit. The acquisition unit determines the current location. The generation unit calculates the route to the destination based on the information acquired by the acquisition unit. The provision unit provides directions based on the route generated by the generation unit. The reception unit receives user input. The response generation unit generates a response based on the information received by the reception unit. The response provision unit provides the response generated by the response generation unit. The main reception unit receives the title of the book the user wants to read. The read-aloud provision unit reads the contents aloud based on the information received by the main reception unit.

The system according to this embodiment allows visually impaired individuals to receive support such as directions and book reading assistance when going out.

This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. This shows an emotion map where multiple emotions are mapped. This shows an emotion map where multiple emotions are mapped.

An example of an embodiment of the system relating to the technology of this disclosure will be described below with reference to the attached drawings.

First, let's explain the terminology used in the following explanation.

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

In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

In the following embodiments, the coded communication interface (I/F) is an interface including a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi®, or Bluetooth®.

In the following embodiments, "A and/or B" is synonymous with "at least one of A and B." That is, "A and/or B" means that A alone, B alone, or a combination of A and B may be used. Furthermore, the same concept applies when expressing three or more things linked by "and/or" in this specification.

[First Embodiment]
Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

As shown in Figure 1, the data processing system 10 comprises 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 comprises a computer 22, a database 24, and a communication interface 26. 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 the communication interface 26 are also connected to the bus 34. The communication interface 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to the bus 52. The receiving 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 and a microphone 38B, and receives user input. The touch panel 38A receives user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting it in a user-perceptible form (e.g., audio and/or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to 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 image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

The communication interface 44 is connected to the network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via the network 54.

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

As shown in Figure 2, in the data processing device 12, specific processing is performed by the processor 28. The storage 32 stores a specific processing program 56. The specific processing program 56 is an example of a "program" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores the specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has data generation model 58 and emotion identification model 59, similar to those of the smart device 14, and can perform processing similar to that of the specific processing unit 290 using these models.

Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by the user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

(Example of form 1)
The wearable device according to an embodiment of the present invention is a system that not only provides directions to visually impaired people when they go out, but also acts as a conversational partner and reads aloud books they wish to read. When a visually impaired person goes out, the system determines their current location and provides directions to their destination. When the user inputs the destination by voice, the generating AI calculates the optimal route and provides voice directions. For example, it provides specific instructions such as, "Turn right at the next intersection." Next, the system functions as a conversational partner. When the user speaks, the generating AI provides an appropriate response and can continue the conversation. This allows visually impaired people to go out with peace of mind without feeling lonely. Furthermore, the system has a function to read aloud books they wish to read. When the user inputs the title of a book they wish to read by voice, the generating AI reads the contents of that book aloud. This allows visually impaired people to enjoy reading freely. Thus, the wearable device of the present invention has multiple functions, such as providing directions, conversational partners, and reading aloud books for visually impaired people when they go out, and functions as a substitute or complement to a guide dog. As a result, the wearable device can provide directions, conversational partners, and read aloud books for visually impaired people when they go out.

The wearable device according to this embodiment comprises an acquisition unit, a generation unit, a provision unit, a reception unit, a response generation unit, a response provision unit, a reception unit, and a read-aloud provision unit. The acquisition unit determines the current location when a visually impaired person goes out. For example, the current location can be acquired using GPS, Wi-Fi location information, cell tower information, etc. The generation unit calculates a route to the destination based on the information acquired by the acquisition unit. For example, it can calculate the shortest distance, shortest time, and a route that takes traffic conditions into account using generation AI. The provision unit provides directions based on the route generated by the generation unit. For example, directions can be provided by methods such as voice guidance, vibration guidance, and visual guidance. The reception unit receives user input. For example, user input can be received by methods such as voice input, touch input, and gesture input. The response generation unit generates a response based on the information received by the reception unit. For example, a response can be generated using methods such as voice response, text response, and vibration response using generation AI. The response provision unit provides the response generated by the response generation unit. For example, a response can be provided by methods such as voice response, text response, and vibration response. This reception desk accepts the title of the book you wish to read. For example, it can accept titles via voice input, text input, etc. The text-to-speech service provides a reading of the content based on the information received by the reception desk. For example, it can use speech synthesis technology to read aloud. This allows visually impaired individuals to receive directions, have someone to talk to, and have books read aloud for them when they are out and about.

The location acquisition unit helps visually impaired individuals determine their current location when they go out. For example, it can acquire their current location using GPS, Wi-Fi location information, and cell tower information. Specifically, GPS receives signals from satellites to pinpoint the current location with high accuracy, while Wi-Fi location information estimates the location based on the signal strength of surrounding Wi-Fi access points. Cell tower information uses signals from cell phone base stations to determine the approximate location. By combining these technologies, highly accurate location information can be acquired both indoors and outdoors. Furthermore, the location acquisition unit can collect not only location information but also surrounding environmental information. For example, it can acquire environmental data such as ambient sound, vibration, temperature, and humidity through sensors to support visually impaired individuals in moving safely. This allows the location acquisition unit to comprehensively collect and provide in real time the information necessary for visually impaired individuals when they go out.

The generation unit calculates the route to the destination based on the information acquired by the acquisition unit. For example, the generation AI can calculate the shortest distance, shortest travel time, and a route that considers traffic conditions. Specifically, the generation AI analyzes map data between the current location and the destination to generate the optimal route. By taking into account traffic conditions, road congestion, and construction information, it ensures smooth travel for visually impaired individuals. Furthermore, the generation AI can learn past travel history and user preferences to provide individually optimized routes. For example, it can suggest safer and more comfortable routes by considering routes frequently used by visually impaired individuals and places they wish to avoid. In addition, the generation unit can recalculate the route based on real-time updated information. For example, in the event of a sudden incident such as a traffic accident or road closure, the generation AI immediately calculates a new route and notifies the visually impaired person. This allows the generation unit to support visually impaired individuals in always traveling along the optimal route.

The service provider provides directions based on the route generated by the generation unit. For example, directions can be provided using methods such as voice guidance, vibration guidance, and visual guidance. Specifically, voice guidance informs visually impaired users of the direction, distance, and obstacles to be aware of. Vibration guidance indicates direction and distance through vibrations, which visually impaired users can feel with their hands and arms. Visual guidance can provide information using braille displays or large print displays when used by visually impaired users. This allows the service provider to help visually impaired users reach their destination safely and reliably. Furthermore, the service provider can customize the guidance method according to the user's preferences and circumstances. For example, vibration guidance can be prioritized for users who have difficulty with voice guidance, and multiple guidance methods can be combined. In addition, the service provider can modify the guidance content based on real-time updated information. For example, if a new obstacle appears on the route, the service provider can immediately provide new guidance to ensure visually impaired users can move safely. This allows the service provider to provide an environment where visually impaired people can go out with peace of mind.

The reception desk receives user input. For example, it can accept user input through methods such as voice input, touch input, and gesture input. Specifically, voice input allows visually impaired users to operate the device by speaking to it, and voice recognition technology is used to accurately understand the user's instructions. Touch input uses the device's touchscreen, allowing visually impaired users to confirm operation through touch. Gesture input uses the device's camera and sensors to detect hand and arm movements, enabling visually impaired users to operate the device with natural movements. This allows the reception desk to support visually impaired users in operating the device intuitively and easily. Furthermore, the reception desk can process user input quickly and accurately and transmit information to other departments. For example, if a destination is specified via voice input, the reception desk sends that information to the generation unit to begin route calculation. This allows the reception desk to provide an environment where visually impaired users can use the device smoothly.

The response generation unit generates a response based on the information received by the reception unit. For example, it can generate responses using methods such as voice responses, text responses, and vibration responses using a generation AI. Specifically, the generation AI analyzes the user's input and generates an appropriate response. Voice responses provide information to visually impaired individuals through voice, using speech synthesis technology to generate natural-sounding speech. Text responses allow visually impaired individuals to access information using braille displays or screen readers, with the generation AI generating appropriate text. Vibration responses convey information through device vibration, allowing visually impaired individuals to receive information through touch. This enables the response generation unit to help visually impaired individuals receive necessary information quickly and accurately. Furthermore, the response generation unit can customize response content according to the user's preferences and circumstances. For example, it can prioritize text responses for users who have difficulty with voice responses and provide a combination of multiple response methods. Additionally, the response generation unit can modify response content based on real-time updated information. This allows the response generation unit to provide an environment where visually impaired individuals can always receive the latest information.

The response provider unit provides responses generated by the response generation unit. For example, responses can be provided through methods such as voice responses, text responses, and vibration responses. Specifically, voice responses provide information to visually impaired individuals through audio, playing the generated audio through speakers or headphones. Text responses allow visually impaired individuals to access information using braille displays or screen readers, displaying the generated text. Vibration responses convey information through device vibration, allowing visually impaired individuals to receive information through touch. This enables the response provider unit to help visually impaired individuals receive necessary information quickly and accurately. Furthermore, the response provider unit can customize response methods according to user preferences and circumstances. For example, it can prioritize text responses for users who have difficulty with voice responses and provide a combination of multiple response methods. Additionally, the response provider unit can modify response content based on real-time updated information. This allows the response provider unit to provide an environment where visually impaired individuals can always receive the most up-to-date information.

This reception desk accepts the titles of books that users wish to read. For example, it can accept book titles via voice input or text input. Specifically, voice input allows visually impaired users to specify book titles by speaking into a device, using voice recognition technology to accurately understand user instructions. Text input allows visually impaired users to enter book titles using a Braille display or screen reader, with features designed to allow them to confirm operation through touch. This enables the reception desk to intuitively and easily assist visually impaired users in specifying the titles of books they wish to read. Furthermore, the reception desk can process user input quickly and accurately and transmit information to other departments. For example, if a book title is specified via voice input, the reception desk sends that information to the text-to-speech service, which then begins reading the title aloud. This allows the reception desk to provide an environment where visually impaired users can smoothly specify the books they wish to read.

The text-to-speech service provides the content based on the information received by the reception desk. For example, it can use speech synthesis technology to provide the content. Specifically, speech synthesis technology analyzes text data and generates natural-sounding speech, providing the content of books to visually impaired individuals in audio format. The text-to-speech service can adjust the reading speed and tone of voice according to the user's preferences and circumstances. For example, it can increase the speed for users who prefer a faster reading pace and decrease it for users who prefer a slower pace. Furthermore, the text-to-speech service can modify the content based on real-time updated information. For example, if the content of a book is updated, the service will immediately read the new content. This allows the service to provide an environment where visually impaired individuals can always receive the latest information. Additionally, the service can collect user feedback and continuously improve the accuracy and effectiveness of the content. This allows the service to provide an environment where visually impaired individuals can comfortably enjoy books.

The wearable device includes a detection unit that detects and avoids obstacles. The detection unit has the function of detecting and avoiding obstacles when a visually impaired person is outdoors. For example, obstacles can be detected using ultrasonic sensors, cameras, or LiDAR. The detection unit can avoid obstacles by changing direction or adjusting speed. For example, it can use an ultrasonic sensor to detect an obstacle ahead and change direction. It can also use a camera to visually detect obstacles and adjust speed. Furthermore, it can use LiDAR to scan the surrounding environment in detail and avoid obstacles. This makes it easier for visually impaired people to avoid obstacles. Some or all of the above-described processes in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data from ultrasonic sensors or cameras into a generating AI, causing the generating AI to perform obstacle detection and avoidance.

The wearable device includes a feedback unit that provides vibration or tactile feedback. The feedback unit has a function to allow visually impaired individuals to receive feedback while outdoors. For example, it can provide feedback by adjusting the intensity and pattern of vibrations. The feedback unit can also provide feedback using a tactile device. For example, it can provide vibrations in a specific pattern using a vibration motor. It can also use a tactile device to make the user feel changes in pressure or temperature. This makes it easier for visually impaired individuals to receive feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user input and environmental data into a generating AI, causing the generating AI to execute the optimal feedback.

Wearable devices are equipped with a function to read aloud specific chapters or pages. This reading function allows visually impaired individuals to select and have specific chapters or pages read aloud. For example, it can read aloud by specifying a chapter number or page number. The reading function can also select specific chapters or pages using keyword search. For example, if a user voice-inputs "Please read chapter 3," the specified chapter will be read aloud. Alternatively, if a user inputs "Please read aloud pages containing the keyword 'adventure'," the corresponding pages will be read aloud. This allows visually impaired individuals to select and have specific chapters or pages read aloud. Some or all of the above-described processes in the reading function may be performed using AI, or not. For example, the reading function can input the user's voice input into a generating AI, which can then perform the reading of the specified chapter or page.

Wearable devices are equipped with multilingual support capabilities. This multilingual support allows visually impaired individuals to access information in multiple languages. For example, it can support languages such as English, Japanese, and Spanish. The multilingual support can accept voice and text input in multiple languages. For example, if a user inputs in English, it will provide a response in English. Similarly, if a user inputs in Japanese, it can provide a response in Japanese. Furthermore, the multilingual support can also perform text-to-speech in multiple languages using speech synthesis technology. For example, when reading an English book, it uses English speech synthesis, and when reading a Japanese book, it uses Japanese speech synthesis. This allows visually impaired individuals to access information in multiple languages. Some or all of the above-described processes in the multilingual support may be performed using AI, or not. For example, the multilingual support can input user input into a generating AI, which can then perform multilingual responses and text-to-speech.

The wearable device includes a service provider that retrieves and provides the latest information online to the user. This service provider has the functionality to enable visually impaired individuals to obtain the latest information in real time. For example, it can retrieve real-time data via the internet and provide it to the user. The service provider can perform regular updates to always provide the latest information. For example, it can retrieve and provide the latest information such as news, weather forecasts, and traffic information. Furthermore, the service provider can retrieve and provide specific information in response to user requests. For example, if a user voice-inputs "Please tell me the latest news," the service provider will retrieve and read aloud the latest news. This allows visually impaired individuals to obtain the latest information in real time. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can input data retrieved from the internet into a generating AI and have the generating AI generate information to provide to the user.

The acquisition unit can analyze the user's past movement history and select the optimal method for acquiring the current location. For example, the acquisition unit analyzes the user's past movement history using GPS logs and movement route records. For instance, it adjusts the accuracy of current location acquisition based on places the user frequently visited in the past. It can also analyze the user's past movement patterns and acquire the current location at the optimal timing. Furthermore, it can identify places where the user has gotten lost in the past and increase the frequency of current location acquisition in those areas. This improves the accuracy of current location acquisition based on past movement history. Some or all of the above processing in the acquisition unit may be performed using AI, or without AI. For example, the acquisition unit can input past movement data into a generating AI and have the generating AI select the optimal method for acquiring the current location.

The acquisition unit can improve the accuracy of location acquisition by considering the user's movement speed and direction. For example, the acquisition unit acquires the user's movement speed using GPS speed sensors and acceleration sensors. For instance, if the user is moving at high speed, the accuracy of location acquisition is increased. Furthermore, by acquiring the user's movement direction using a compass and gyroscope sensor, the frequency of location acquisition can be increased if the user frequently changes direction. Additionally, if the user is walking, the accuracy of location acquisition can be adjusted according to the movement speed. This improves the accuracy of location acquisition by considering movement speed and direction. Some or all of the above processing in the acquisition unit may be performed using AI, or without AI. For example, the acquisition unit can input movement speed and direction data into a generating AI, causing the generating AI to perform the necessary improvements to the accuracy of location acquisition.

The acquisition unit can prioritize the acquisition of highly relevant information by considering the user's geographical location when acquiring the current location. The acquisition unit acquires the user's geographical location information using, for example, GPS, Wi-Fi location information, or cell tower information. For example, if the user is in a tourist area, it can prioritize acquiring information about tourist spots. Similarly, if the user is in a commercial facility, it can prioritize acquiring store information. Furthermore, if the user is using public transportation, it can prioritize acquiring service information. This allows for the priority acquisition of highly relevant information by considering geographical location information. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input geographical location information into a generating AI and have the generating AI acquire highly relevant information.

The acquisition unit can analyze the user's social media activity and obtain relevant information when acquiring the current location. For example, the acquisition unit can analyze the user's social media activity. For instance, it can acquire information about locations where the user has checked in on social media. It can also acquire the current location based on the location information of photos shared by the user on social media. Furthermore, it can prioritize acquiring information about locations the user follows on social media. This allows for the acquisition of relevant information by analyzing social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, or without AI. For example, the acquisition unit can input social media data into a generating AI and have the generating AI perform the acquisition of relevant information.

The generation unit can predict the optimal route by referring to past travel data during route calculation. For example, the generation unit refers to past travel data using GPS logs or travel route records. For instance, it predicts the optimal route based on routes previously used by the user. It can also predict routes that avoid congestion based on the user's past travel data. Furthermore, it can analyze the user's past travel data to predict the most efficient route. This allows for the prediction of the optimal route by referring to past travel data. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input past travel data into a generation AI and have the generation AI perform the optimal route prediction.

The generation unit can generate the optimal route by considering traffic conditions and weather information during route calculation. For example, the generation unit generates the optimal route based on real-time traffic congestion information. For instance, it calculates a route that avoids congestion using traffic information obtained from traffic sensors or the internet. It can also generate the optimal route by considering real-time weather information. For example, it calculates a route that avoids rain and snow based on weather data. Furthermore, it can generate the optimal route by considering the real-time operation status of public transportation. For example, it calculates the most efficient route based on operation information. This allows for the generation of the optimal route by considering traffic conditions and weather information. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input traffic information and weather information into a generation AI and have the generation AI generate the optimal route.

The generation unit can generate the optimal route by considering the user's geographical location information during route calculation. The generation unit obtains the user's geographical location information using, for example, GPS, Wi-Fi location information, or cell tower information. For example, if the user is in a tourist area, it can generate a route that passes through tourist spots. If the user is in a commercial facility, it can also generate a route that considers store information. Furthermore, if the user is using public transportation, it can generate a route that considers service information. This allows for the generation of the optimal route by considering geographical location information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input geographical location information into a generation AI and have the generation AI generate the optimal route.

The generation unit can analyze the user's social media activity during route calculation and generate relevant routes. For example, the generation unit can analyze the user's social media activity. For instance, it can generate routes that pass through locations the user has checked into on social media. It can also generate routes based on location information of photos the user has shared on social media. Furthermore, it can generate routes that pass through locations the user follows on social media. This allows for the generation of relevant routes by analyzing social media activity. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input social media data into a generation AI and have the generation AI generate relevant routes.

The service provider can select the optimal guidance method by referring to the user's past travel history when providing directions. For example, the service provider can refer to the user's past travel history using GPS logs or travel route records. For instance, it can select the optimal guidance method based on routes previously used by the user. It can also select a guidance method that avoids congestion based on the user's past travel history. Furthermore, it can analyze the user's past travel history to select the most efficient guidance method. This allows the service provider to select the optimal guidance method by referring to past travel history. Some or all of the above processing in the service provider may be performed using AI, or without AI. For example, the service provider can input past travel data into a generating AI and have the generating AI select the optimal guidance method.

The guidance unit can adjust the timing of directions by considering the user's current speed and direction. For example, the guidance unit can obtain the user's speed using GPS speed sensors or acceleration sensors. For instance, if the user is moving at high speed, the guidance timing can be advanced. Furthermore, by obtaining the user's direction using a compass or gyroscope sensor, the guidance timing can be shortened if the user frequently changes direction. Additionally, if the user is walking, the guidance timing can be adjusted according to their speed. This allows for optimization of guidance timing by considering speed and direction. Some or all of the above processing in the guidance unit may be performed using AI, or without AI. For example, the guidance unit can input speed and direction data into a generating AI and have the generating AI adjust the guidance timing.

The service provider can select the optimal guidance method by considering the user's geographical location information during directions. The service provider acquires the user's geographical location information using, for example, GPS, Wi-Fi location information, or cell tower information. For example, if the user is in a tourist area, it can select a guidance method that passes through tourist spots. If the user is in a commercial facility, it can also select a guidance method that considers store information. Furthermore, if the user is using public transportation, it can select a guidance method that considers service operation information. This allows for the selection of the optimal guidance method by considering geographical location information. Some or all of the above processing in the service provider may be performed using, for example, AI, or without AI. For example, the service provider can input geographical location information into a generating AI and have the generating AI select the optimal guidance method.

The service provider can analyze the user's social media activity and provide relevant guidance information when providing directions. For example, the service provider can analyze the user's social media activity. For instance, it can provide information about places the user has checked into on social media. It can also provide guidance information based on the location information of photos the user has shared on social media. Furthermore, it can provide information about places the user follows on social media. This allows the service provider to provide relevant guidance information by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input social media data into a generating AI and have the generating AI perform the task of providing relevant guidance information.

The reception unit can select the optimal reception method by referring to the user's past input history when receiving input. For example, the reception unit can refer to the user's past input history using input logs and records of input content. For instance, it can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. It can also provide predictive input based on the user's past input. Furthermore, it can predict and suggest input methods to be used during specific time periods based on the user's past input history. This allows the reception unit to select the optimal reception method by referring to past input history. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input past input data into a generating AI and have the generating AI select the optimal reception method.

The reception unit can improve the accuracy of its reception process by considering the user's current situation and environment during input reception. For example, the reception unit can acquire information about the user's current situation and environment using ambient sound, light intensity, temperature, etc. For instance, if the user is in a noisy environment, the accuracy of voice input can be improved. Furthermore, if the user is moving, the timing of input reception can be adjusted. Additionally, if the user is in a quiet environment, detailed input options can be provided. This improves the accuracy of reception by considering the current situation and environment. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input environmental data into a generating AI and have the generating AI perform the accuracy improvement of the reception process.

The reception unit can select the optimal reception method when receiving user input, taking into account the user's geographical location information. The reception unit obtains the user's geographical location information using, for example, GPS, Wi-Fi location information, or cell tower information. For example, if the user is in a tourist area, tourist information can be prioritized. Similarly, if the user is in a commercial facility, store information can be prioritized. Furthermore, if the user is using public transportation, service information can be prioritized. This allows the reception unit to select the optimal reception method by considering geographical location information. Some or all of the above processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input geographical location information into a generating AI and have the generating AI select the optimal reception method.

The reception unit can analyze the user's social media activity and receive relevant input information upon receiving input. For example, the reception unit can analyze the user's social media activity. For instance, it can receive information about locations where the user has checked in on social media. It can also receive input information based on the location information of photos shared by the user on social media. Furthermore, it can receive information about locations the user follows on social media. This allows the reception unit to receive relevant input information by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI, or without AI. For example, the reception unit can input social media data into a generating AI and have the generating AI perform the reception of relevant input information.

The detection unit can select the optimal detection method by referring to the user's past movement history when an obstacle is detected. For example, the detection unit refers to the user's past movement history using GPS logs or recorded movement routes. For instance, it can select the optimal detection method based on routes frequently used by the user in the past. It can also select a detection method that avoids congestion based on the user's past movement history. Furthermore, it can analyze the user's past movement history to select the most efficient detection method. This allows the optimal detection method to be selected by referring to past movement history. Some or all of the above processing in the detection unit may be performed using AI, or without AI. For example, the detection unit can input past movement data into a generating AI and have the generating AI select the optimal detection method.

The detection unit can select the optimal detection method when detecting an obstacle, taking into account the user's geographical location information. The detection unit acquires the user's geographical location information using, for example, GPS, Wi-Fi location information, or cell tower information. For example, if the user is in a tourist area, it can prioritize detecting obstacles around tourist spots. Similarly, if the user is in a commercial facility, it can prioritize detecting obstacles around stores. Furthermore, if the user is using public transportation, it can prioritize detecting obstacles around stations and bus stops. This allows for the selection of the optimal detection method by considering geographical location information. Some or all of the above processing in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input geographical location information into a generating AI and have the generating AI select the optimal detection method.

The feedback unit can select the optimal feedback method by referring to the user's past feedback history during the feedback process. For example, the feedback unit can refer to the user's past feedback history using feedback logs and records of feedback content. For instance, it can provide optimal feedback based on the user's preferred feedback methods in the past. It can also select a feedback method for a specific situation based on the user's past feedback history. Furthermore, it can analyze the user's past feedback history to select the most effective feedback method. This allows the optimal feedback method to be selected by referring to past feedback history. Some or all of the above processing in the feedback unit may be performed using AI, or without AI. For example, the feedback unit can input past feedback data into a generating AI and have the generating AI select the optimal feedback method.

The feedback unit can select the optimal feedback method by considering the user's geographical location information during the feedback process. The feedback unit acquires the user's geographical location information using, for example, GPS, Wi-Fi location information, or cell tower information. For instance, if the user is in a tourist area, feedback around tourist spots can be prioritized. Similarly, if the user is in a commercial facility, feedback around stores can be prioritized. Furthermore, if the user is using public transportation, feedback around train stations or bus stops can be prioritized. This allows the system to select the optimal feedback method by considering geographical location information. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or without AI. For instance, the feedback unit can input geographical location information into a generating AI and have the generating AI select the optimal feedback method.

The reading unit can select the optimal reading method by referring to the user's past reading history during reading. For example, the unit can refer to the user's past reading history using reading logs or records of reading content. For instance, it can provide the optimal reading method based on the user's past preferred reading methods. It can also select reading methods for specific genres or authors based on the user's past reading history. Furthermore, it can analyze the user's past reading history to select the most effective reading method. This allows the optimal reading method to be selected by referring to past reading history. Some or all of the above processing in the reading unit may be performed using AI, or without AI. For example, the reading unit can input past reading data into a generating AI and have the generating AI select the optimal reading method.

The service provider can select the optimal reading method by considering the user's geographical location information during reading. The service provider acquires the user's geographical location information using, for example, GPS, Wi-Fi location information, or cell tower information. For example, if the user is in a tourist area, it can prioritize reading information related to tourist spots. Similarly, if the user is in a commercial facility, it can prioritize reading store information. Furthermore, if the user is using public transportation, it can prioritize reading service information. This allows the service provider to select the optimal reading method by considering geographical location information. Some or all of the above processing in the service provider may be performed using, for example, AI, or without AI. For example, the service provider can input geographical location information into a generating AI and have the generating AI select the optimal reading method.

The system according to this embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

Wearable devices can also include a health management unit that monitors the user's health. This unit acquires biometric information such as heart rate, blood pressure, and body temperature, and monitors the user's health in real time. For example, if the heart rate is abnormally high, it can provide an alert prompting the user to rest. It can also provide guidance for relaxation if blood pressure is high. Furthermore, if body temperature is elevated, it can warn of the risk of heatstroke. This makes it easier for visually impaired individuals to manage their health while outdoors.

Wearable devices can also include a meal management unit to support users' dietary management. This unit records the content of meals consumed by the user and analyzes nutritional balance. For example, if the user inputs the details of their meal by voice, it provides information on calories and nutrients. It can also suggest ingredients and recipes containing specific nutrients if the user needs to consume them. Furthermore, it can provide healthy eating advice based on the user's meal history. This makes it easier for visually impaired individuals to maintain a healthy diet.

Wearable devices can also be equipped with an exercise management unit to support the user's physical activity. This unit records the user's exercise volume and type, and analyzes the effects of the exercise. For example, if the user walks or runs, it calculates the distance and calories burned. Furthermore, if the user sets specific exercise goals, it can monitor their progress and report on their achievement. It can also provide advice on the user's form and posture during exercise. This makes it easier for visually impaired individuals to exercise effectively.

Wearable devices can also be equipped with a sleep management unit to support the user's sleep. This unit records the user's sleep patterns and analyzes sleep quality. For example, it can provide music or guidance to help the user relax before going to sleep. It can also provide an alarm to encourage natural waking. Furthermore, it can provide advice to improve sleep quality based on the user's sleep data. This can make it easier for visually impaired individuals to achieve high-quality sleep.

Wearable devices can also be equipped with a learning support unit to assist users in their learning. This unit provides information related to what the user wants to learn, based on voice input. For example, if a user wants to learn about a specific topic, it can provide articles and audio materials on that topic. It can also teach pronunciation practice and word meanings if the user wants to learn a language. Furthermore, it can record the user's learning progress and suggest what to learn next. This makes it easier for visually impaired individuals to learn effectively even when they are out and about.

The following briefly describes the processing flow for example 1.

Step 1: The acquisition unit determines the current location of a visually impaired person when they go out. For example, it acquires the current location using GPS, Wi-Fi location information, cell tower information, etc.
Step 2: The generation unit calculates the route to the destination based on the information acquired by the acquisition unit. For example, it uses generation AI to calculate the shortest distance, shortest time, and a route that takes traffic conditions into consideration.
Step 3: The providing unit provides directions based on the route generated by the generating unit. For example, directions are provided using methods such as voice guidance, vibration guidance, or visual guidance.
Step 4: The reception desk receives user input. For example, it accepts user input through methods such as voice input, touch input, or gesture input.
Step 5: The response generation unit generates a response based on the information received by the reception unit. For example, it generates a response using a generation AI in the form of a voice response, text response, vibration response, etc.
Step 6: The response provider provides the response generated by the response generator. For example, the response is provided by methods such as voice response, text response, or vibration response.
Step 7: This reception desk accepts the title of the book you want to read. For example, it accepts the book title via voice input, text input, etc.
Step 8: The text-to-speech service unit reads the content aloud based on the information received by the reception unit. For example, it may use speech synthesis technology to perform the reading.

(Example of form 2)
The wearable device according to an embodiment of the present invention is a system that not only provides directions to visually impaired people when they go out, but also acts as a conversational partner and reads aloud books they wish to read. When a visually impaired person goes out, the system determines their current location and provides directions to their destination. When the user inputs the destination by voice, the generating AI calculates the optimal route and provides voice directions. For example, it provides specific instructions such as, "Turn right at the next intersection." Next, the system functions as a conversational partner. When the user speaks, the generating AI provides an appropriate response and can continue the conversation. This allows visually impaired people to go out with peace of mind without feeling lonely. Furthermore, the system has a function to read aloud books they wish to read. When the user inputs the title of a book they wish to read by voice, the generating AI reads the contents of that book aloud. This allows visually impaired people to enjoy reading freely. Thus, the wearable device of the present invention has multiple functions, such as providing directions, conversational partners, and reading aloud books for visually impaired people when they go out, and functions as a substitute or complement to a guide dog. As a result, the wearable device can provide directions, conversational partners, and read aloud books for visually impaired people when they go out.

The wearable device according to this embodiment comprises an acquisition unit, a generation unit, a provision unit, a reception unit, a response generation unit, a response provision unit, a reception unit, and a read-aloud provision unit. The acquisition unit determines the current location when a visually impaired person goes out. For example, the current location can be acquired using GPS, Wi-Fi location information, cell tower information, etc. The generation unit calculates a route to the destination based on the information acquired by the acquisition unit. For example, it can calculate the shortest distance, shortest time, and a route that takes traffic conditions into account using generation AI. The provision unit provides directions based on the route generated by the generation unit. For example, directions can be provided by methods such as voice guidance, vibration guidance, and visual guidance. The reception unit receives user input. For example, user input can be received by methods such as voice input, touch input, and gesture input. The response generation unit generates a response based on the information received by the reception unit. For example, a response can be generated using methods such as voice response, text response, and vibration response using generation AI. The response provision unit provides the response generated by the response generation unit. For example, a response can be provided by methods such as voice response, text response, and vibration response. This reception desk accepts the title of the book you wish to read. For example, it can accept titles via voice input, text input, etc. The text-to-speech service provides a reading of the content based on the information received by the reception desk. For example, it can use speech synthesis technology to read aloud. This allows visually impaired individuals to receive directions, have someone to talk to, and have books read aloud for them when they are out and about.

The location acquisition unit helps visually impaired individuals determine their current location when they go out. For example, it can acquire their current location using GPS, Wi-Fi location information, and cell tower information. Specifically, GPS receives signals from satellites to pinpoint the current location with high accuracy, while Wi-Fi location information estimates the location based on the signal strength of surrounding Wi-Fi access points. Cell tower information uses signals from cell phone base stations to determine the approximate location. By combining these technologies, highly accurate location information can be acquired both indoors and outdoors. Furthermore, the location acquisition unit can collect not only location information but also surrounding environmental information. For example, it can acquire environmental data such as ambient sound, vibration, temperature, and humidity through sensors to support visually impaired individuals in moving safely. This allows the location acquisition unit to comprehensively collect and provide in real time the information necessary for visually impaired individuals when they go out.

The generation unit calculates the route to the destination based on the information acquired by the acquisition unit. For example, the generation AI can calculate the shortest distance, shortest travel time, and a route that considers traffic conditions. Specifically, the generation AI analyzes map data between the current location and the destination to generate the optimal route. By taking into account traffic conditions, road congestion, and construction information, it ensures smooth travel for visually impaired individuals. Furthermore, the generation AI can learn past travel history and user preferences to provide individually optimized routes. For example, it can suggest safer and more comfortable routes by considering routes frequently used by visually impaired individuals and places they wish to avoid. In addition, the generation unit can recalculate the route based on real-time updated information. For example, in the event of a sudden incident such as a traffic accident or road closure, the generation AI immediately calculates a new route and notifies the visually impaired person. This allows the generation unit to support visually impaired individuals in always traveling along the optimal route.

The service provider provides directions based on the route generated by the generation unit. For example, directions can be provided using methods such as voice guidance, vibration guidance, and visual guidance. Specifically, voice guidance informs visually impaired users of the direction, distance, and obstacles to be aware of. Vibration guidance indicates direction and distance through vibrations, which visually impaired users can feel with their hands and arms. Visual guidance can provide information using braille displays or large print displays when used by visually impaired users. This allows the service provider to help visually impaired users reach their destination safely and reliably. Furthermore, the service provider can customize the guidance method according to the user's preferences and circumstances. For example, vibration guidance can be prioritized for users who have difficulty with voice guidance, and multiple guidance methods can be combined. In addition, the service provider can modify the guidance content based on real-time updated information. For example, if a new obstacle appears on the route, the service provider can immediately provide new guidance to ensure visually impaired users can move safely. This allows the service provider to provide an environment where visually impaired people can go out with peace of mind.

The reception desk receives user input. For example, it can accept user input through methods such as voice input, touch input, and gesture input. Specifically, voice input allows visually impaired users to operate the device by speaking to it, and voice recognition technology is used to accurately understand the user's instructions. Touch input uses the device's touchscreen, allowing visually impaired users to confirm operation through touch. Gesture input uses the device's camera and sensors to detect hand and arm movements, enabling visually impaired users to operate the device with natural movements. This allows the reception desk to support visually impaired users in operating the device intuitively and easily. Furthermore, the reception desk can process user input quickly and accurately and transmit information to other departments. For example, if a destination is specified via voice input, the reception desk sends that information to the generation unit to begin route calculation. This allows the reception desk to provide an environment where visually impaired users can use the device smoothly.

The response generation unit generates a response based on the information received by the reception unit. For example, it can generate responses using methods such as voice responses, text responses, and vibration responses using a generation AI. Specifically, the generation AI analyzes the user's input and generates an appropriate response. Voice responses provide information to visually impaired individuals through voice, using speech synthesis technology to generate natural-sounding speech. Text responses allow visually impaired individuals to access information using braille displays or screen readers, with the generation AI generating appropriate text. Vibration responses convey information through device vibration, allowing visually impaired individuals to receive information through touch. This enables the response generation unit to help visually impaired individuals receive necessary information quickly and accurately. Furthermore, the response generation unit can customize response content according to the user's preferences and circumstances. For example, it can prioritize text responses for users who have difficulty with voice responses and provide a combination of multiple response methods. Additionally, the response generation unit can modify response content based on real-time updated information. This allows the response generation unit to provide an environment where visually impaired individuals can always receive the latest information.

The response provider unit provides responses generated by the response generation unit. For example, responses can be provided through methods such as voice responses, text responses, and vibration responses. Specifically, voice responses provide information to visually impaired individuals through audio, playing the generated audio through speakers or headphones. Text responses allow visually impaired individuals to access information using braille displays or screen readers, displaying the generated text. Vibration responses convey information through device vibration, allowing visually impaired individuals to receive information through touch. This enables the response provider unit to help visually impaired individuals receive necessary information quickly and accurately. Furthermore, the response provider unit can customize response methods according to user preferences and circumstances. For example, it can prioritize text responses for users who have difficulty with voice responses and provide a combination of multiple response methods. Additionally, the response provider unit can modify response content based on real-time updated information. This allows the response provider unit to provide an environment where visually impaired individuals can always receive the most up-to-date information.

This reception desk accepts the titles of books that users wish to read. For example, it can accept book titles via voice input or text input. Specifically, voice input allows visually impaired users to specify book titles by speaking into a device, using voice recognition technology to accurately understand user instructions. Text input allows visually impaired users to enter book titles using a Braille display or screen reader, with features designed to allow them to confirm operation through touch. This enables the reception desk to intuitively and easily assist visually impaired users in specifying the titles of books they wish to read. Furthermore, the reception desk can process user input quickly and accurately and transmit information to other departments. For example, if a book title is specified via voice input, the reception desk sends that information to the text-to-speech service, which then begins reading the title aloud. This allows the reception desk to provide an environment where visually impaired users can smoothly specify the books they wish to read.

The text-to-speech service provides the content based on the information received by the reception desk. For example, it can use speech synthesis technology to provide the content. Specifically, speech synthesis technology analyzes text data and generates natural-sounding speech, providing the content of books to visually impaired individuals in audio format. The text-to-speech service can adjust the reading speed and tone of voice according to the user's preferences and circumstances. For example, it can increase the speed for users who prefer a faster reading pace and decrease it for users who prefer a slower pace. Furthermore, the text-to-speech service can modify the content based on real-time updated information. For example, if the content of a book is updated, the service will immediately read the new content. This allows the service to provide an environment where visually impaired individuals can always receive the latest information. Additionally, the service can collect user feedback and continuously improve the accuracy and effectiveness of the content. This allows the service to provide an environment where visually impaired individuals can comfortably enjoy books.

The wearable device includes a detection unit that detects and avoids obstacles. The detection unit has the function of detecting and avoiding obstacles when a visually impaired person is outdoors. For example, obstacles can be detected using ultrasonic sensors, cameras, or LiDAR. The detection unit can avoid obstacles by changing direction or adjusting speed. For example, it can use an ultrasonic sensor to detect an obstacle ahead and change direction. It can also use a camera to visually detect obstacles and adjust speed. Furthermore, it can use LiDAR to scan the surrounding environment in detail and avoid obstacles. This makes it easier for visually impaired people to avoid obstacles. Some or all of the above-described processes in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data from ultrasonic sensors or cameras into a generating AI, causing the generating AI to perform obstacle detection and avoidance.

The wearable device includes a feedback unit that provides vibration or tactile feedback. The feedback unit has a function to allow visually impaired individuals to receive feedback while outdoors. For example, it can provide feedback by adjusting the intensity and pattern of vibrations. The feedback unit can also provide feedback using a tactile device. For example, it can provide vibrations in a specific pattern using a vibration motor. It can also use a tactile device to make the user feel changes in pressure or temperature. This makes it easier for visually impaired individuals to receive feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user input and environmental data into a generating AI, causing the generating AI to execute the optimal feedback.

Wearable devices are equipped with a function to read aloud specific chapters or pages. This reading function allows visually impaired individuals to select and have specific chapters or pages read aloud. For example, it can read aloud by specifying a chapter number or page number. The reading function can also select specific chapters or pages using keyword search. For example, if a user voice-inputs "Please read chapter 3," the specified chapter will be read aloud. Alternatively, if a user inputs "Please read aloud pages containing the keyword 'adventure'," the corresponding pages will be read aloud. This allows visually impaired individuals to select and have specific chapters or pages read aloud. Some or all of the above-described processes in the reading function may be performed using AI, or not. For example, the reading function can input the user's voice input into a generating AI, which can then perform the reading of the specified chapter or page.

Wearable devices are equipped with multilingual support capabilities. This multilingual support allows visually impaired individuals to access information in multiple languages. For example, it can support languages such as English, Japanese, and Spanish. The multilingual support can accept voice and text input in multiple languages. For example, if a user inputs in English, it will provide a response in English. Similarly, if a user inputs in Japanese, it can provide a response in Japanese. Furthermore, the multilingual support can also perform text-to-speech in multiple languages using speech synthesis technology. For example, when reading an English book, it uses English speech synthesis, and when reading a Japanese book, it uses Japanese speech synthesis. This allows visually impaired individuals to access information in multiple languages. Some or all of the above-described processes in the multilingual support may be performed using AI, or not. For example, the multilingual support can input user input into a generating AI, which can then perform multilingual responses and text-to-speech.

The wearable device includes a service provider that retrieves and provides the latest information online to the user. This service provider has the functionality to enable visually impaired individuals to obtain the latest information in real time. For example, it can retrieve real-time data via the internet and provide it to the user. The service provider can perform regular updates to always provide the latest information. For example, it can retrieve and provide the latest information such as news, weather forecasts, and traffic information. Furthermore, the service provider can retrieve and provide specific information in response to user requests. For example, if a user voice-inputs "Please tell me the latest news," the service provider will retrieve and read aloud the latest news. This allows visually impaired individuals to obtain the latest information in real time. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can input data retrieved from the internet into a generating AI and have the generating AI generate information to provide to the user.

The acquisition unit can estimate the user's emotions and adjust the frequency of acquiring the current location based on the estimated user emotions. The acquisition unit estimates the user's emotions using, for example, voice analysis, facial expression analysis, heart rate, etc. For example, if the user is feeling anxious, the frequency of acquiring the current location can be increased to provide more detailed navigation information. Conversely, if the user is relaxed, the frequency of acquiring the current location can be decreased to conserve battery power. Furthermore, if the user is in a hurry, the frequency of acquiring the current location can be increased to provide faster navigation. This allows for more appropriate navigation by adjusting the frequency of acquiring the current location according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input user voice data and facial expression data into the generating AI, allowing the AI to perform emotion estimation and adjust the frequency of acquiring the current location.

The acquisition unit can analyze the user's past movement history and select the optimal method for acquiring the current location. For example, the acquisition unit analyzes the user's past movement history using GPS logs and movement route records. For instance, it adjusts the accuracy of current location acquisition based on places the user frequently visited in the past. It can also analyze the user's past movement patterns and acquire the current location at the optimal timing. Furthermore, it can identify places where the user has gotten lost in the past and increase the frequency of current location acquisition in those areas. This improves the accuracy of current location acquisition based on past movement history. Some or all of the above processing in the acquisition unit may be performed using AI, or without AI. For example, the acquisition unit can input past movement data into a generating AI and have the generating AI select the optimal method for acquiring the current location.

The acquisition unit can improve the accuracy of location acquisition by considering the user's movement speed and direction. For example, the acquisition unit acquires the user's movement speed using GPS speed sensors and acceleration sensors. For instance, if the user is moving at high speed, the accuracy of location acquisition is increased. Furthermore, by acquiring the user's movement direction using a compass and gyroscope sensor, the frequency of location acquisition can be increased if the user frequently changes direction. Additionally, if the user is walking, the accuracy of location acquisition can be adjusted according to the movement speed. This improves the accuracy of location acquisition by considering movement speed and direction. Some or all of the above processing in the acquisition unit may be performed using AI, or without AI. For example, the acquisition unit can input movement speed and direction data into a generating AI, causing the generating AI to perform the necessary improvements to the accuracy of location acquisition.

The acquisition unit can estimate the user's emotions and adjust the timing of acquiring the current location based on the estimated user emotions. The acquisition unit estimates the user's emotions using, for example, voice analysis, facial expression analysis, heart rate, etc. For example, if the user is tense, the timing of acquiring the current location can be shortened to provide a sense of security. Also, if the user is relaxed, the timing of acquiring the current location can be lengthened to reduce battery consumption. Furthermore, if the user is in a hurry, the timing of acquiring the current location can be shortened to provide rapid navigation. In this way, by adjusting the timing of acquiring the current location according to the user's emotions, more appropriate navigation becomes possible. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input user voice data and facial expression data into the generating AI, allowing the AI to perform emotion estimation and adjust the timing of location acquisition.

The acquisition unit can prioritize the acquisition of highly relevant information by considering the user's geographical location when acquiring the current location. The acquisition unit acquires the user's geographical location information using, for example, GPS, Wi-Fi location information, or cell tower information. For example, if the user is in a tourist area, it can prioritize acquiring information about tourist spots. Similarly, if the user is in a commercial facility, it can prioritize acquiring store information. Furthermore, if the user is using public transportation, it can prioritize acquiring service information. This allows for the priority acquisition of highly relevant information by considering geographical location information. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input geographical location information into a generating AI and have the generating AI acquire highly relevant information.

The acquisition unit can analyze the user's social media activity and obtain relevant information when acquiring the current location. For example, the acquisition unit can analyze the user's social media activity. For instance, it can acquire information about locations where the user has checked in on social media. It can also acquire the current location based on the location information of photos shared by the user on social media. Furthermore, it can prioritize acquiring information about locations the user follows on social media. This allows for the acquisition of relevant information by analyzing social media activity. Some or all of the above-described processes in the acquisition unit may be performed using AI, or without AI. For example, the acquisition unit can input social media data into a generating AI and have the generating AI perform the acquisition of relevant information.

The generation unit can estimate the user's emotions and adjust the route calculation algorithm based on the estimated emotions. The generation unit estimates the user's emotions using, for example, voice analysis, facial expression analysis, heart rate, etc. For example, if the user is feeling anxious, it can use an algorithm that prioritizes the safest route. If the user is relaxed, it can also use an algorithm that prioritizes a route with good scenery. Furthermore, if the user is in a hurry, it can use an algorithm that prioritizes the route that can be reached in the shortest time. This allows for the provision of a more appropriate route by adjusting the route calculation algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the user's voice data and facial expression data into the generative AI and have the generative AI perform emotion estimation and adjust the route calculation algorithm.

The generation unit can predict the optimal route by referring to past travel data during route calculation. For example, the generation unit refers to past travel data using GPS logs or travel route records. For instance, it predicts the optimal route based on routes previously used by the user. It can also predict routes that avoid congestion based on the user's past travel data. Furthermore, it can analyze the user's past travel data to predict the most efficient route. This allows for the prediction of the optimal route by referring to past travel data. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input past travel data into a generation AI and have the generation AI perform the optimal route prediction.

The generation unit can generate the optimal route by considering traffic conditions and weather information during route calculation. For example, the generation unit generates the optimal route based on real-time traffic congestion information. For instance, it calculates a route that avoids congestion using traffic information obtained from traffic sensors or the internet. It can also generate the optimal route by considering real-time weather information. For example, it calculates a route that avoids rain and snow based on weather data. Furthermore, it can generate the optimal route by considering the real-time operation status of public transportation. For example, it calculates the most efficient route based on operation information. This allows for the generation of the optimal route by considering traffic conditions and weather information. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input traffic information and weather information into a generation AI and have the generation AI generate the optimal route.

The generation unit can estimate the user's emotions and determine route priorities based on those emotions. The generation unit estimates the user's emotions using methods such as voice analysis, facial expression analysis, and heart rate. For example, if the user is feeling anxious, it prioritizes a safe route. If the user is relaxed, it can prioritize a route with good scenery. Furthermore, if the user is in a hurry, it can prioritize the route that can be reached in the shortest time. This allows for the provision of more appropriate routes by prioritizing routes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit may be performed using AI, or not. For example, the generation unit can input user voice data and facial expression data into the generative AI and have the generative AI perform emotion estimation and route priority determination.

The generation unit can generate the optimal route by considering the user's geographical location information during route calculation. The generation unit obtains the user's geographical location information using, for example, GPS, Wi-Fi location information, or cell tower information. For example, if the user is in a tourist area, it can generate a route that passes through tourist spots. If the user is in a commercial facility, it can also generate a route that considers store information. Furthermore, if the user is using public transportation, it can generate a route that considers service information. This allows for the generation of the optimal route by considering geographical location information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input geographical location information into a generation AI and have the generation AI generate the optimal route.

The generation unit can analyze the user's social media activity and generate relevant routes during route calculation. For example, the generation unit analyzes the user's social media activity. For instance, it can generate routes that pass through locations the user has checked into on social media. It can also generate routes based on location information of photos the user has shared on social media. Furthermore, it can generate routes that pass through locations the user follows on social media. This allows for the generation of relevant routes by analyzing social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input social media data into a generation AI and have the generation AI generate relevant routes.

The service provider can estimate the user's emotions and adjust the way directions are presented based on those emotions. The service provider estimates the user's emotions using methods such as voice analysis, facial expression analysis, and heart rate. For example, if the user is feeling anxious, detailed directions are provided. Conversely, if the user is relaxed, concise directions can be provided. Furthermore, if the user is in a hurry, quick directions can be provided. This allows for more appropriate guidance by adjusting the way directions are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the service provider may be performed using AI, or without AI. For example, the service provider can input user voice data and facial expression data into the generative AI and have the generative AI perform emotion estimation and adjustment of the way directions are presented.

The service provider can select the optimal guidance method by referring to the user's past travel history when providing directions. For example, the service provider can refer to the user's past travel history using GPS logs or travel route records. For instance, it can select the optimal guidance method based on routes previously used by the user. It can also select guidance methods that avoid congestion based on the user's past travel history. Furthermore, it can analyze the user's past travel history to select the most efficient guidance method. This allows the service provider to select the optimal guidance method by referring to past travel history. Some or all of the above processing in the service provider may be performed using AI, or without AI. For example, the service provider can input past travel data into a generating AI and have the generating AI select the optimal guidance method.

The guidance unit can adjust the timing of directions by considering the user's current speed and direction. For example, the guidance unit can obtain the user's speed using GPS speed sensors or acceleration sensors. For instance, if the user is moving at high speed, the guidance timing can be advanced. Furthermore, by obtaining the user's direction using a compass or gyroscope sensor, the guidance timing can be shortened if the user frequently changes direction. Additionally, if the user is walking, the guidance timing can be adjusted according to their speed. This allows for optimization of guidance timing by considering speed and direction. Some or all of the above processing in the guidance unit may be performed using AI, or without AI. For example, the guidance unit can input speed and direction data into a generating AI and have the generating AI adjust the guidance timing.

The service provider can estimate the user's emotions and determine the priority of directions based on those emotions. The service provider estimates the user's emotions using methods such as voice analysis, facial expression analysis, and heart rate. For example, if the user is feeling anxious, it prioritizes a safe route. If the user is relaxed, it can prioritize a route with good scenery. Furthermore, if the user is in a hurry, it can prioritize the route that will get them there in the shortest time. This allows for more appropriate directions by prioritizing directions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's voice data and facial expression data into the generative AI and have the generative AI perform emotion estimation and determine the priority of directions.

The service provider can select the optimal guidance method by considering the user's geographical location information during directions. The service provider acquires the user's geographical location information using, for example, GPS, Wi-Fi location information, or cell tower information. For example, if the user is in a tourist area, it can select a guidance method that passes through tourist spots. If the user is in a commercial facility, it can also select a guidance method that considers store information. Furthermore, if the user is using public transportation, it can select a guidance method that considers service operation information. This allows for the selection of the optimal guidance method by considering geographical location information. Some or all of the above processing in the service provider may be performed using, for example, AI, or without AI. For example, the service provider can input geographical location information into a generating AI and have the generating AI select the optimal guidance method.

The service provider can analyze the user's social media activity and provide relevant guidance information during navigation. For example, the service provider can analyze the user's social media activity. For instance, it can provide information about locations where the user has checked in on social media. It can also provide guidance information based on the location information of photos shared by the user on social media. Furthermore, it can provide information about locations the user follows on social media. This allows the service provider to provide relevant guidance information by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input social media data into a generating AI and have the generating AI perform the provision of relevant guidance information.

The reception unit can estimate the user's emotions and adjust the input reception method based on the estimated emotions. The reception unit estimates the user's emotions using methods such as voice analysis, facial expression analysis, and heart rate. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input and accept input quickly. This allows for more appropriate input by adjusting the input reception method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception desk can input user voice data and facial expression data into a generating AI, which can then perform emotion estimation and adjust the input reception method.

The reception unit can select the optimal reception method by referring to the user's past input history when receiving input. For example, the reception unit can refer to the user's past input history using input logs and records of input content. For instance, it can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. It can also provide predictive input based on the user's past input. Furthermore, it can predict and suggest input methods to be used during specific time periods based on the user's past input history. This allows the reception unit to select the optimal reception method by referring to past input history. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input past input data into a generating AI and have the generating AI select the optimal reception method.

The reception unit can improve the accuracy of its reception process by considering the user's current situation and environment during input reception. For example, the reception unit can acquire information about the user's current situation and environment using ambient sound, light intensity, temperature, etc. For instance, if the user is in a noisy environment, the accuracy of voice input can be improved. Furthermore, if the user is moving, the timing of input reception can be adjusted. Additionally, if the user is in a quiet environment, detailed input options can be provided. This improves the accuracy of reception by considering the current situation and environment. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input environmental data into a generating AI and have the generating AI perform the accuracy improvement of the reception process.

The reception unit can estimate the user's emotions and determine the priority of input processing based on the estimated emotions. The reception unit estimates the user's emotions using methods such as voice analysis, facial expression analysis, and heart rate. For example, if the user is feeling anxious, important inputs may be prioritized. If the user is relaxed, detailed inputs may be prioritized. Furthermore, if the user is in a hurry, quick inputs may be prioritized. This allows for more appropriate input by prioritizing input processing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's voice data and facial expression data into the generative AI and have the generative AI perform emotion estimation and input processing priority determination.

The reception unit can select the optimal reception method when receiving user input, taking into account the user's geographical location information. The reception unit obtains the user's geographical location information using, for example, GPS, Wi-Fi location information, or cell tower information. For example, if the user is in a tourist area, tourist information can be prioritized. Similarly, if the user is in a commercial facility, store information can be prioritized. Furthermore, if the user is using public transportation, service information can be prioritized. This allows the reception unit to select the optimal reception method by considering geographical location information. Some or all of the above processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input geographical location information into a generating AI and have the generating AI select the optimal reception method.

The reception unit can analyze the user's social media activity and receive relevant input information upon receiving input. For example, the reception unit can analyze the user's social media activity. For instance, it can receive information about locations where the user has checked in on social media. It can also receive input information based on the location information of photos shared by the user on social media. Furthermore, it can receive information about locations the user follows on social media. This allows the reception unit to receive relevant input information by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI, or without AI. For example, the reception unit can input social media data into a generating AI and have the generating AI perform the reception of relevant input information.

The detection unit can estimate the user's emotions and adjust the sensitivity of obstacle detection based on the estimated emotions. The detection unit estimates the user's emotions using methods such as voice analysis, facial expression analysis, and heart rate. For example, if the user is feeling anxious, the sensitivity of obstacle detection can be increased. Conversely, if the user is relaxed, the sensitivity can be decreased. Furthermore, if the user is in a hurry, the sensitivity can be increased. This allows for more accurate detection by adjusting the sensitivity of obstacle detection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using AI, or without AI. For example, the detection unit can input user voice data and facial expression data into the generative AI and have the generative AI perform emotion estimation and adjustment of obstacle detection sensitivity.

The detection unit can select the optimal detection method by referring to the user's past movement history when an obstacle is detected. For example, the detection unit refers to the user's past movement history using GPS logs or recorded movement routes. For instance, it can select the optimal detection method based on routes frequently used by the user in the past. It can also select a detection method that avoids congestion based on the user's past movement history. Furthermore, it can analyze the user's past movement history to select the most efficient detection method. This allows the optimal detection method to be selected by referring to past movement history. Some or all of the above processing in the detection unit may be performed using AI, or without AI. For example, the detection unit can input past movement data into a generating AI and have the generating AI select the optimal detection method.

The detection unit can estimate the user's emotions and determine the priority of obstacle detection based on the estimated emotions. The detection unit estimates the user's emotions using methods such as voice analysis, facial expression analysis, and heart rate. For example, if the user is feeling anxious, it prioritizes detecting important obstacles. If the user is relaxed, it can prioritize detecting detailed obstacles. Furthermore, if the user is in a hurry, it can prioritize rapid obstacle detection. This allows for more appropriate detection by determining the priority of obstacle detection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using AI, or not. For example, the detection unit can input user voice data and facial expression data into the generative AI and have the generative AI perform emotion estimation and determine the priority of obstacle detection.

The detection unit can select the optimal detection method when detecting an obstacle, taking into account the user's geographical location information. The detection unit acquires the user's geographical location information using, for example, GPS, Wi-Fi location information, or cell tower information. For example, if the user is in a tourist area, it can prioritize detecting obstacles around tourist spots. Similarly, if the user is in a commercial facility, it can prioritize detecting obstacles around stores. Furthermore, if the user is using public transportation, it can prioritize detecting obstacles around stations and bus stops. This allows for the selection of the optimal detection method by considering geographical location information. Some or all of the above processing in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input geographical location information into a generating AI and have the generating AI select the optimal detection method.

The feedback unit can estimate the user's emotions and adjust the intensity of the feedback based on the estimated emotions. The feedback unit estimates the user's emotions using methods such as voice analysis, facial expression analysis, and heart rate. For example, if the user is feeling anxious, it provides strong feedback. Conversely, if the user is relaxed, it can provide weaker feedback. Furthermore, if the user is in a hurry, it can provide rapid feedback. This allows for more appropriate feedback by adjusting the intensity of the feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the feedback unit may be performed using AI, or not. For example, the feedback unit can input user voice data and facial expression data into the generative AI and have the generative AI perform emotion estimation and adjustment of feedback intensity.

The feedback unit can select the optimal feedback method by referring to the user's past feedback history during the feedback process. For example, the feedback unit can refer to the user's past feedback history using feedback logs and records of feedback content. For instance, it can provide optimal feedback based on the user's preferred feedback methods in the past. It can also select a feedback method for a specific situation based on the user's past feedback history. Furthermore, it can analyze the user's past feedback history to select the most effective feedback method. This allows the optimal feedback method to be selected by referring to past feedback history. Some or all of the above processing in the feedback unit may be performed using AI, or without AI. For example, the feedback unit can input past feedback data into a generating AI and have the generating AI select the optimal feedback method.

The feedback unit can estimate the user's emotions and determine the priority of feedback based on those emotions. The feedback unit estimates the user's emotions using methods such as voice analysis, facial expression analysis, and heart rate. For example, if the user is feeling anxious, important feedback can be prioritized. If the user is relaxed, detailed feedback can be prioritized. Furthermore, if the user is in a hurry, quick feedback can be prioritized. This allows for more appropriate feedback by prioritizing feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the feedback unit may be performed using AI, or not. For example, the feedback unit can input user voice data and facial expression data into the generative AI and have the generative AI perform emotion estimation and determine the priority of feedback.

The feedback unit can select the optimal feedback method by considering the user's geographical location information during the feedback process. The feedback unit acquires the user's geographical location information using, for example, GPS, Wi-Fi location information, or cell tower information. For instance, if the user is in a tourist area, feedback around tourist spots can be prioritized. Similarly, if the user is in a commercial facility, feedback around stores can be prioritized. Furthermore, if the user is using public transportation, feedback around train stations or bus stops can be prioritized. This allows the system to select the optimal feedback method by considering geographical location information. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or without AI. For instance, the feedback unit can input geographical location information into a generating AI and have the generating AI select the optimal feedback method.

The service provider can estimate the user's emotions and adjust the reading style based on the estimated emotions. The service provider estimates the user's emotions using methods such as voice analysis, facial expression analysis, and heart rate. For example, if the user is feeling anxious, the service provider will read in a calm voice. If the user is relaxed, the service provider can read in a cheerful voice. Furthermore, if the user is in a hurry, the service provider can read quickly and concisely. This allows for more appropriate reading by adjusting the reading style according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the service provider may be performed using AI, or without AI. For example, the service provider can input user voice data and facial expression data into the generative AI and have the generative AI perform emotion estimation and adjustment of the reading style.

The reading unit can select the optimal reading method by referring to the user's past reading history during reading. For example, the unit can refer to the user's past reading history using reading logs or records of reading content. For instance, it can provide the optimal reading method based on the user's past preferred reading methods. It can also select reading methods for specific genres or authors based on the user's past reading history. Furthermore, it can analyze the user's past reading history to select the most effective reading method. This allows the optimal reading method to be selected by referring to past reading history. Some or all of the above processing in the reading unit may be performed using AI, or without AI. For example, the reading unit can input past reading data into a generating AI and have the generating AI select the optimal reading method.

The service provider can estimate the user's emotions and determine the reading priority based on those emotions. The service provider estimates the user's emotions using methods such as voice analysis, facial expression analysis, and heart rate. For example, if the user is feeling anxious, important information will be prioritized. If the user is relaxed, detailed information can be prioritized. Furthermore, if the user is in a hurry, quick information can be prioritized. This allows for more appropriate reading by determining the reading priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the service provider may be performed using AI, or without AI. For example, the service provider can input user voice data and facial expression data into the generative AI and have the generative AI perform emotion estimation and determine the reading priority.

The service provider can select the optimal reading method by considering the user's geographical location information during reading. The service provider acquires the user's geographical location information using, for example, GPS, Wi-Fi location information, or cell tower information. For example, if the user is in a tourist area, it can prioritize reading information related to tourist spots. Similarly, if the user is in a commercial facility, it can prioritize reading store information. Furthermore, if the user is using public transportation, it can prioritize reading service information. This allows the service provider to select the optimal reading method by considering geographical location information. Some or all of the above processing in the service provider may be performed using, for example, AI, or without AI. For example, the service provider can input geographical location information into a generating AI and have the generating AI select the optimal reading method.

The system according to this embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

Wearable devices can also include a health management unit that monitors the user's health. This unit acquires biometric information such as heart rate, blood pressure, and body temperature, and monitors the user's health in real time. For example, if the heart rate is abnormally high, it can provide an alert prompting the user to rest. It can also provide guidance for relaxation if blood pressure is high. Furthermore, if body temperature is elevated, it can warn of the risk of heatstroke. This makes it easier for visually impaired individuals to manage their health while outdoors.

Wearable devices can also include a music playback unit that estimates the user's emotions and selects music based on those emotions. For example, if the user is relaxed, it can provide calming music. If the user is stressed, it can provide relaxing music. Furthermore, if the user wants to feel energized, it can provide upbeat music. This allows visually impaired individuals to enjoy music tailored to their emotions even when outdoors.

Wearable devices can also include a meal management unit to support users' dietary management. This unit records the content of meals consumed by the user and analyzes nutritional balance. For example, if the user inputs the details of their meal by voice, it provides information on calories and nutrients. It can also suggest ingredients and recipes containing specific nutrients if the user needs to consume them. Furthermore, it can provide healthy eating advice based on the user's meal history. This makes it easier for visually impaired individuals to maintain a healthy diet.

Wearable devices can also include a relaxation section that estimates the user's emotions and provides relaxation guidance based on those emotions. For example, if the user is feeling stressed, it can provide guidance on deep breathing or meditation. If the user is relaxed, it can provide relaxation music or nature sounds. Furthermore, if the user is feeling anxious, it can provide reassuring messages. This makes it easier for visually impaired individuals to relax even when outdoors.

Wearable devices can also be equipped with an exercise management unit to support the user's physical activity. This unit records the user's exercise volume and type, and analyzes the effects of the exercise. For example, if the user walks or runs, it calculates the distance and calories burned. Furthermore, if the user sets specific exercise goals, it can monitor their progress and report on their achievement. It can also provide advice on the user's form and posture during exercise. This makes it easier for visually impaired individuals to exercise effectively.

Wearable devices can also include a communication unit that estimates the user's emotions and adjusts the communication method based on those emotions. For example, if the user is feeling lonely, it can provide messages encouraging them to contact friends or family. If the user is relaxed, it can offer lighthearted topics. Furthermore, if the user is stressed, it can provide encouraging messages. This makes it easier for visually impaired individuals to communicate appropriately even when outdoors.

Wearable devices can also be equipped with a sleep management unit to support the user's sleep. This unit records the user's sleep patterns and analyzes sleep quality. For example, it can provide music or guidance to help the user relax before going to sleep. It can also provide an alarm to encourage natural waking. Furthermore, it can provide advice to improve sleep quality based on the user's sleep data. This can make it easier for visually impaired individuals to achieve high-quality sleep.

Wearable devices can also incorporate an exercise unit that estimates the user's emotions and adjusts the exercise content based on those emotions. For example, if the user is feeling stressed, it can suggest relaxing exercises. If the user wants to feel more energetic, it can suggest energetic exercises. Furthermore, if the user is relaxed, it can suggest light stretching or yoga. This makes it easier for visually impaired individuals to perform appropriate exercises even when outdoors.

Wearable devices can also be equipped with a learning support unit to assist users in their learning. This unit provides information related to what the user wants to learn, based on voice input. For example, if a user wants to learn about a specific topic, it can provide articles and audio materials on that topic. It can also teach pronunciation practice and word meanings if the user wants to learn a language. Furthermore, it can record the user's learning progress and suggest what to learn next. This makes it easier for visually impaired individuals to learn effectively even when they are out and about.

Wearable devices can also feature a reminder function that estimates the user's emotions and adjusts the content of reminders based on those emotions. For example, if the user is stressed, the reminder content can be made concise. Conversely, if the user is relaxed, a more detailed reminder can be provided. Furthermore, if the user is in a hurry, important reminders can be prioritized. This makes it easier for visually impaired individuals to receive appropriate reminders even when they are out and about.

The following briefly describes the processing flow for example 2.

Step 1: The acquisition unit determines the current location of a visually impaired person when they go out. For example, it acquires the current location using GPS, Wi-Fi location information, cell tower information, etc.
Step 2: The generation unit calculates the route to the destination based on the information acquired by the acquisition unit. For example, it uses generation AI to calculate the shortest distance, shortest time, and a route that takes traffic conditions into consideration.
Step 3: The providing unit provides directions based on the route generated by the generating unit. For example, directions are provided using methods such as voice guidance, vibration guidance, or visual guidance.
Step 4: The reception desk receives user input. For example, it accepts user input through methods such as voice input, touch input, or gesture input.
Step 5: The response generation unit generates a response based on the information received by the reception unit. For example, it generates a response using a generation AI in the form of a voice response, text response, vibration response, etc.
Step 6: The response provider provides the response generated by the response generator. For example, the response is provided by methods such as voice response, text response, or vibration response.
Step 7: This reception desk accepts the title of the book you want to read. For example, it accepts the book title via voice input, text input, etc.
Step 8: The text-to-speech service unit reads the content aloud based on the information received by the reception unit. For example, it may use speech synthesis technology to perform the reading.

The specific processing unit 290 transmits the result of the specific processing to the smart device 14. On the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input regarding the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. On the data processing device 12, the specific processing unit 290 acquires the audio data.

The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is a generative AI such as ChatGPT® (Internet search <URL: https://openai.com/blog/chatgpt>). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a finely tuned model that outputs inference results from prompts that do not contain instructions. In this case, the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform various processes, but is not limited to these examples. The AI may also be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI in part or in whole, but is not limited to these examples. Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

Furthermore, the processing performed by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be executed by both the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Also, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or external devices, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or external devices.

Each of the multiple elements described above, including the acquisition unit, generation unit, provision unit, reception unit, response generation unit, response provision unit, main reception unit, read-aloud provision unit, detection unit, feedback unit, read-aloud function, multi-language support function, provision unit, and emotion estimation function, is implemented, for example, in at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires the current location using the GPS or Wi-Fi location information of the smart device 14. The generation unit calculates the optimal route using the specific processing unit 290 of the data processing device 12. The provision unit provides voice guidance using the control unit 46A of the smart device 14. The reception unit receives user input using the touch panel 38A or microphone 38B of the smart device 14. The response generation unit generates a response using the specific processing unit 290 of the data processing device 12. The response provision unit provides the response using the speaker 40B of the smart device 14. The main reception unit receives the book title using the microphone 38B of the smart device 14. The text-to-speech unit reads the content aloud using the specific processing unit 290 of the data processing device 12. The detection unit detects obstacles using the camera 42 and ultrasonic sensor of the smart device 14. The feedback unit provides feedback using the vibration motor of the smart device 14. The text-to-speech function reads specific chapters or pages aloud using the specific processing unit 290 of the data processing device 12. The multi-language support function provides responses and text-to-speech in multiple languages using the specific processing unit 290 of the data processing device 12. The provision unit acquires the latest information using the specific processing unit 290 of the data processing device 12 and provides it using the speaker 40B of the smart device 14. The emotion estimation function estimates the user's emotion using the specific processing unit 290 of the data processing device 12 and adjusts the frequency of acquiring the current location. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[Second Embodiment]
Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

As shown in Figure 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 comprises a computer 22, a database 24, and a communication interface 26. 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 the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and/or LAN.

The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 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 user voice commands and other information. The microphone 238 captures the user's voice, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. It captures images of the user's surroundings (for example, an imaging range defined by a field of view equivalent to that of a typical healthy person).

The communication interface 44 is connected to the network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via the network 54. The exchange of various types of information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

The processor 28 reads a specific processing program 56 from the storage 32 and executes the read program on the RAM 30. The specific processing is achieved by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores the specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes it on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have data generation model 58 and emotion identification model 59, similar to those of the smart glasses 214, and can perform processing similar to that of the specific processing unit 290 using these models.

Furthermore, other devices besides the data processing device 12 may also possess the data generation model 58. For example, a server device may possess the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device possessing the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (e.g., a mobile phone, robot, home appliance, etc.).

The specific processing unit 290 transmits the result 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 result of the specific processing. The microphone 238 acquires audio indicating user input regarding the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a finely tuned model that outputs inference results from prompts that do not contain instructions. In this case, the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform various processes, but is not limited to these examples. The AI may also be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI in part or in whole, but is not limited to these examples. Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, or it may be executed by both the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or external devices, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or external devices.

Each of the multiple elements described above, including the acquisition unit, generation unit, provision unit, reception unit, response generation unit, response provision unit, main reception unit, read-aloud provision unit, detection unit, feedback unit, read-aloud function, multi-language support function, provision unit, and emotion estimation function, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires the current location using the GPS or Wi-Fi location information of the smart glasses 214. The generation unit calculates the optimal route using the specific processing unit 290 of the data processing unit 12. The provision unit provides voice guidance using the control unit 46A of the smart glasses 214. The reception unit receives user input using the microphone 238 of the smart glasses 214. The response generation unit generates a response using the specific processing unit 290 of the data processing unit 12. The response provision unit provides the response using the speaker 240 of the smart glasses 214. The main reception unit receives the book title using the microphone 238 of the smart glasses 214. The text-to-speech unit reads the content aloud using the specific processing unit 290 of the data processing device 12. The detection unit detects obstacles using the camera 42 and ultrasonic sensor of the smart glasses 214. The feedback unit provides feedback using the vibration motor of the smart glasses 214. The text-to-speech function reads specific chapters or pages aloud using the specific processing unit 290 of the data processing device 12. The multi-language support function provides responses and text-to-speech in multiple languages using the specific processing unit 290 of the data processing device 12. The information provision unit acquires the latest information using the specific processing unit 290 of the data processing device 12 and provides it using the speaker 240 of the smart glasses 214. The emotion estimation function estimates the user's emotion using the specific processing unit 290 of the data processing device 12 and adjusts the frequency of acquiring the current location. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[Third Embodiment]
Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

As shown in Figure 5, the data processing system 310 comprises a data processing device 12 and a headset-type terminal 314. An example of the data processing device 12 is a server.

The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and/or LAN.

The headset terminal 314 comprises a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 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 the bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

The microphone 238 receives user voice commands and other information. The microphone 238 captures the user's voice, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. It captures images of the user's surroundings (for example, an imaging range defined by a field of view equivalent to that of a typical healthy person).

The communication interface 44 is connected to the network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via the network 54. The exchange of various types of information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

The processor 28 reads a specific processing program 56 from the storage 32 and executes the read program on the RAM 30. The specific processing is achieved by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read program 60 on the RAM 48. The specific processing is achieved by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has data generation model 58 and emotion identification model 59, similar to those of the data generation model 58 and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

Furthermore, other devices besides the data processing device 12 may also possess the data generation model 58. For example, a server device may possess the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device possessing the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (e.g., a mobile phone, robot, home appliance, etc.).

The specific processing unit 290 transmits the result 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 result of the specific processing. The microphone 238 acquires audio indicating user input regarding the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a finely tuned model that outputs inference results from prompts that do not contain instructions. In this case, the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform various processes, but is not limited to these examples. The AI may also be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI in part or in whole, but is not limited to these examples. Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, or it may be executed by both the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

Each of the multiple elements described above, including the acquisition unit, generation unit, provision unit, reception unit, response generation unit, response provision unit, main reception unit, read-aloud provision unit, detection unit, feedback unit, read-aloud function, multi-language support function, provision unit, and emotion estimation function, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires the current location using the GPS or Wi-Fi location information of the headset terminal 314. The generation unit calculates the optimal route using the specific processing unit 290 of the data processing unit 12. The provision unit provides voice guidance using the control unit 46A of the headset terminal 314. The reception unit receives user input using the microphone 238 of the headset terminal 314. The response generation unit generates a response using the specific processing unit 290 of the data processing unit 12. The response provision unit provides a response using the speaker 240 of the headset terminal 314. The main reception unit receives the book title using the microphone 238 of the headset terminal 314. The text-to-speech unit reads the content aloud using the specific processing unit 290 of the data processing device 12. The detection unit detects obstacles using the camera 42 and ultrasonic sensor of the headset terminal 314. The feedback unit provides feedback using the vibration motor of the headset terminal 314. The text-to-speech function reads specific chapters or pages aloud using the specific processing unit 290 of the data processing device 12. The multi-language support function provides responses and text-to-speech in multiple languages using the specific processing unit 290 of the data processing device 12. The provision unit acquires the latest information using the specific processing unit 290 of the data processing device 12 and provides it using the speaker 240 of the headset terminal 314. The emotion estimation function estimates the user's emotion using the specific processing unit 290 of the data processing device 12 and adjusts the frequency of acquiring the current location. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[Fourth Embodiment]
Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

As shown in Figure 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 comprises a computer 22, a database 24, and a communication interface 26. 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 the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and/or LAN.

The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to the bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

The microphone 238 receives user voice commands and other information. The microphone 238 captures the user's voice, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the user's surroundings (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

The communication interface 44 is connected to the network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via the network 54. The exchange of various types of information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

The controlled 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

The processor 28 reads a specific processing program 56 from the storage 32 and executes the read program on the RAM 30. The specific processing is achieved by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

In robot 414, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read program 60 on the RAM 48. The specific processing is achieved by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. Furthermore, robot 414 has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

Furthermore, other devices besides the data processing device 12 may also possess the data generation model 58. For example, a server device may possess the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device possessing the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (e.g., a mobile phone, robot, home appliance, etc.).

The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input regarding the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a finely tuned model that outputs inference results from prompts that do not contain instructions. In this case, the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform various processes, but is not limited to these examples. The AI may also be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI in part or in whole, but is not limited to these examples. Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by both the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or external devices, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or external devices.

Each of the multiple elements described above, including the acquisition unit, generation unit, provision unit, reception unit, response generation unit, response provision unit, main reception unit, read-aloud provision unit, detection unit, feedback unit, read-aloud function, multi-language support function, provision unit, and emotion estimation function, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires the current location using the GPS or Wi-Fi location information of the robot 414. The generation unit calculates the optimal route using the specific processing unit 290 of the data processing unit 12. The provision unit provides voice guidance using the control unit 46A of the robot 414. The reception unit receives user input using the microphone 238 of the robot 414. The response generation unit generates a response using the specific processing unit 290 of the data processing unit 12. The response provision unit provides a response using the speaker 240 of the robot 414. The main reception unit receives the book title using the microphone 238 of the robot 414. The text-to-speech unit reads the content aloud using the specific processing unit 290 of the data processing device 12. The detection unit detects obstacles using the camera 42 and ultrasonic sensor of the robot 414. The feedback unit provides feedback using the vibration motor of the robot 414. The text-to-speech function reads specific chapters or pages aloud using the specific processing unit 290 of the data processing device 12. The multi-language support function provides responses and text-to-speech in multiple languages using the specific processing unit 290 of the data processing device 12. The provision unit acquires the latest information using the specific processing unit 290 of the data processing device 12 and provides it using the speaker 240 of the robot 414. The emotion estimation function estimates the user's emotion using the specific processing unit 290 of the data processing device 12 and adjusts the frequency of acquiring the current location. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

Furthermore, the emotion identification model 59, acting 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 a specific mapping, namely an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also 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 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Furthermore, the emotions of "pleasure" are located on the upper side of the concentric circles, and emotions of "displeasure" are located on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calmer impression.

The inner part of the Emotion Map 400 represents inner thoughts, while the outer part represents actions. Therefore, the further you go from the outer edge of the Emotion Map 400, the more visible (expressed through actions) your emotions become.

Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort; when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort; when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a Brain Physiological Signal Analysis System for Speech Emotion Recognition and Emotion, Tokushima University, Doctoral Dissertation: https://ci.nii.ac.jp/naid/500000375379). The left half of the emotion map displays emotions belonging to the "response" region, where sensation is dominant. The right half of the emotion map displays emotions belonging to the "situation" region, where situational awareness is dominant.

The emotion map defines two emotions that promote learning. One is the negative emotion around the middle of "repentance" or "reflection" on the situation side. 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. This is when the robot feels positive emotions 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 representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassurance," "peace of mind," and "reassurance" have similar emotion values.

In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, involving 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 this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing according to 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.

Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, nor is it necessary to store the entirety of the specific processing program 56 in the storage device 32. It is acceptable to store only a portion of the specific processing program 56.

The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing 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 have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

The hardware resource performing a specific process may consist of one of these various processors, or it may consist 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). Alternatively, the hardware resource performing the specific process may consist of a single processor.

Examples of configurations using a single processor include, firstly, a configuration where one or more CPUs and software are combined to form a single processor, which functions as a hardware resource for executing specific processing. Secondly, a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources for executing specific processing, on a single IC chip, as exemplified by SoC (System-on-a-chip). Thus, specific processing is realized using one or more of the above-mentioned types of processors as hardware resources.

Furthermore, the hardware structure of these various processors can, more specifically, utilize electrical circuits combining circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be removed, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

Furthermore, although the above examples were described separately as the first to fourth embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset-type terminal 314, and robot 414 are examples, and they may be combined, or other devices may be used. Furthermore, although the above examples were described separately as Embodiment Example 1 and Embodiment Example 2, these may be combined.

The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technology. For example, the above descriptions of the structure, function, operation, and effects are examples of the structure, function, operation, and effects of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technology presented. Furthermore, to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other information that do not require special explanation to enable the implementation of the technology have been omitted from the descriptions and illustrations presented above.

All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

(Note 1)
A unit that acquires the current location,
A generation unit calculates a route to the destination based on the information acquired by the acquisition unit,
A providing unit that provides directions based on the route generated by the generation unit,
A reception area that receives user input,
A response generation unit that generates a response based on the information received by the reception unit,
A response providing unit that provides the response generated by the response generating unit,
The book submission department accepts the titles of books you want to read,
A system characterized by comprising: a reading and providing unit that reads out content based on information received by the aforementioned receiving unit.
(Note 2)
The system according to Appendix 1, characterized by comprising a detection unit that detects and avoids obstacles.
(Note 3)
The system according to Appendix 1, characterized by comprising a feedback unit that provides vibration or tactile feedback.
(Note 4)
The system described in Appendix 1, characterized by having a function to read aloud specific chapters or pages.
(Note 5)
The system described in Appendix 1, characterized by having the ability to support multiple languages.
(Note 6)
The system described in Appendix 1, characterized by having a provisioning unit that obtains the latest information online and provides it to the user.
(Note 7)
The acquisition unit is,
The system described in Appendix 1, characterized by estimating the user's emotions and adjusting the frequency of acquiring the current location based on the estimated user emotions.
(Note 8)
The acquisition unit is,
The system described in Appendix 1, characterized by analyzing the user's past movement history and selecting the optimal method for obtaining the current location.
(Note 9)
The acquisition unit is,
The system described in Appendix 1, characterized in that it improves accuracy when acquiring the current location by taking into account the user's movement speed and direction.
(Note 10)
The acquisition unit is,
The system described in Appendix 1, characterized by estimating the user's emotions and adjusting the timing of acquiring the current location based on the estimated user emotions.
(Note 11)
The acquisition unit is,
The system described in Appendix 1, characterized in that when acquiring the current location, it prioritizes acquiring highly relevant information by taking into account the user's geographical location information.
(Note 12)
The acquisition unit is,
The system described in Appendix 1, characterized in that it analyzes the user's social media activity and obtains relevant information when acquiring the user's current location.
(Note 13)
The generating unit is
The system described in Appendix 1, characterized by estimating the user's emotions and adjusting the root calculation algorithm based on the estimated user emotions.
(Note 14)
The generating unit is
The system described in Appendix 1, characterized in that it predicts the optimal route by referring to past travel data when calculating the route.
(Note 15)
The generating unit is
The system described in Appendix 1, characterized in that it generates the optimal route by taking into account traffic conditions and weather information when calculating the route.
(Note 16)
The generating unit is
The system described in Appendix 1, characterized by estimating the user's emotions and determining route priorities based on the estimated user emotions.
(Note 17)
The generating unit is
The system described in Appendix 1, characterized in that it generates the optimal route by taking into account the user's geographical location information when calculating the route.
(Note 18)
The generating unit is
The system described in Appendix 1, characterized in that it analyzes the user's social media activity and generates relevant routes when calculating routes.
(Note 19)
The aforementioned supply unit is,
The system described in Appendix 1, characterized by estimating the user's emotions and adjusting the way directions are presented based on the estimated user emotions.
(Note 20)
The aforementioned supply unit is,
The system described in Appendix 1, characterized in that it selects the optimal guidance method by referring to the user's past travel history when providing directions.
(Note 21)
The aforementioned supply unit is,
The system described in Appendix 1, characterized in that it adjusts the timing of directions while taking into account the user's current speed and direction of movement.
(Note 22)
The aforementioned supply unit is,
The system described in Appendix 1, characterized by estimating the user's emotions and determining the priority of directions based on the estimated user emotions.
(Note 23)
The aforementioned supply unit is,
The system described in Appendix 1, characterized in that it selects the optimal guidance method when providing directions, taking into account the user's geographical location information.
(Note 24)
The aforementioned supply unit is,
The system described in Appendix 1, characterized in that it analyzes the user's social media activity and provides relevant guidance information when providing directions.
(Note 25)
The aforementioned reception unit is
The system described in Appendix 1, characterized by estimating the user's emotions and adjusting the input acceptance method based on the estimated user emotions.
(Note 26)
The aforementioned reception unit is
The system described in Appendix 1, characterized in that, when receiving input, it refers to the user's past input history to select the optimal reception method.
(Note 27)
The aforementioned reception unit is
The system described in Appendix 1, characterized in that it improves the accuracy of input by taking into account the user's current situation and environment when receiving input.
(Note 28)
The aforementioned reception unit is
The system described in Appendix 1, characterized by estimating the user's emotions and determining the priority of input acceptance based on the estimated user emotions.
(Note 29)
The aforementioned reception unit is
The system described in Appendix 1, characterized in that it selects the optimal reception method considering the user's geographical location information when receiving input.
(Note 30)
The aforementioned reception unit is
The system described in Appendix 1, characterized in that it analyzes the user's social media activity and accepts relevant input information when receiving input.
(Note 31)
The detection unit,
The system described in Appendix 2, characterized by estimating the user's emotions and adjusting the sensitivity of obstacle detection based on the estimated user emotions.
(Note 32)
The detection unit,
The system described in Appendix 2, characterized in that, when an obstacle is detected, it selects the optimal detection method by referring to the user's past movement history.
(Note 33)
The detection unit,
The system described in Appendix 2, characterized by estimating the user's emotions and determining the priority of obstacle detection based on the estimated user emotions.
(Note 34)
The detection unit is
The system described in Appendix 2, characterized in that, when detecting an obstacle, it selects the optimal detection method taking into account the user's geographical location information.
(Note 35)
The aforementioned feedback unit is
The system described in Appendix 3, characterized by estimating the user's emotions and adjusting the intensity of feedback based on the estimated user emotions.
(Note 36)
The aforementioned feedback unit is
The system described in Appendix 3, characterized in that, when providing feedback, it selects the optimal feedback method by referring to the user's past feedback history.
(Note 37)
The aforementioned feedback unit is
The system described in Appendix 3, characterized by estimating the user's emotions and determining the priority of feedback based on the estimated user emotions.
(Note 38)
The aforementioned feedback unit is
The system described in Appendix 3, characterized in that, when providing feedback, it selects the optimal feedback method considering the user's geographical location information.
(Note 39)
The aforementioned supply unit is,
The system described in Appendix 1, characterized by estimating the user's emotions and adjusting the reading style based on the estimated user emotions.
(Note 40)
The aforementioned supply unit is,
The system described in Appendix 1, characterized in that, when reading aloud, it selects the optimal reading method by referring to the user's past reading history.
(Note 41)
The aforementioned supply unit is,
The system described in Appendix 1, characterized by estimating the user's emotions and determining the priority of reading aloud based on the estimated user emotions.
(Note 42)
The aforementioned supply unit is,
The system described in Appendix 1, characterized in that it selects the optimal reading method when reading aloud, taking into account the user's geographical location information.

10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset terminal 414 Robot

Claims (6)

An emotion estimation unit that estimates the user's emotions,
A unit that acquires the current location,
A generation unit calculates a route to the destination based on the information acquired by the acquisition unit,
A providing unit that provides directions based on the route generated by the generation unit,
A reception area that receives user input,
A response generation unit that generates a response based on the information received by the reception unit,
A response providing unit that provides the response generated by the response generating unit,
The book submission department accepts the titles of books you want to read,
The system includes a reading and providing unit that reads out the content based on the information received by the aforementioned reception unit,
The acquisition unit analyzes logs of past travel routes, duration of stay at each travel route, and current location acquisition methods used, which are recorded as the user's past travel history. Based on the analysis results and the characteristics of places that the user is likely to visit based on the current time and day of the week, the unit selects the optimal current location acquisition method from among a plurality of predefined current location acquisition methods, including GPS-based current location acquisition, Wi-Fi access point signal strength-based current location acquisition, and mobile phone base station signal-based current location acquisition, which is judged to provide the highest positioning accuracy in the current situation. The system is characterized in that, if the user's emotion estimated by the emotion estimation unit is a pre-set first emotional state, the frequency of acquiring the current location is increased compared to normal, and if the estimated user's emotion is a pre-set second emotional state, the frequency of acquiring the current location is decreased compared to normal. The system according to claim 1, characterized in that it includes a detection unit that detects and avoids obstacles. The system according to claim 1, characterized by comprising a feedback unit that provides vibration or tactile feedback. The system according to claim 1, characterized in that the text-to-speech unit has the function of reading aloud the content from a specific chapter or page designated by the user. The system according to claim 1, wherein the reading-aloud unit has a function to read aloud the content in a language selected by the user from among a plurality of pre-set languages, and the response-providing unit has a function to provide the response in a language selected by the user from among the plurality of languages. The system according to claim 1, characterized in that it includes a provisioning unit that obtains the latest information online and provides it to the user.