JP7796835B2 - system - Google Patents

Description

The technology disclosed herein relates to a system.

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

Japanese Patent Application Laid-Open No. 2022-180282

Existing technology does not adequately recommend optimal sports based on an individual's physical characteristics and growth process, and there is room for improvement.

The system according to the embodiment aims to recommend the most suitable sports competition based on an individual's physical characteristics and growth process.

The system according to the embodiment includes a collection unit, an analysis unit, a recommendation unit, and a provision unit. The collection unit collects data on the user's physical characteristics and growth process. The analysis unit analyzes the data collected by the collection unit. The recommendation unit recommends an appropriate sport based on the analysis results obtained by the analysis unit. The provision unit provides training plans and advice based on the sport recommended by the recommendation unit.

The system according to the embodiment can recommend the most suitable sports competition based on an individual's physical characteristics and growth process.

1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. 10 is a conceptual diagram illustrating an example of the configuration of a data processing system according to a second embodiment. FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. FIG. 10 is a conceptual diagram illustrating an example of the configuration of a data processing system according to a third embodiment. FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. 1 shows an emotion map onto which multiple emotions are mapped. 1 shows an emotion map onto which multiple emotions are mapped.

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

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

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

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

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

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

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

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

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

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

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

The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with an indicator (e.g., a pen or finger) to receive user input by the indicator. The microphone 38B detects the user's voice to receive user input by 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 identification 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 the data in a form that the user can perceive (e.g., audio and/or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

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

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

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

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

In the smart device 14, the 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 specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model and emotion identification model similar to the data generation model 58 and emotion identification model 59, and can use these models to perform processing similar to that of the specific processing unit 290.

Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain processing results (prediction results, etc.) using the data generation model 58. The data processing device 12 may also be a server device, or a terminal device owned by a 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 1)
A sports recommendation system according to an embodiment of the present invention recommends optimal sports based on a user's physical characteristics and provides training plans and advice. The system begins with the user inputting data about their physical characteristics and growth process. For example, the user inputs data such as height, weight, muscle mass, bone density, and flexibility. This data is then input into an AI. The AI then analyzes the input data. Based on a past database, the AI analyzes success factors for each sports and compares them with the user's data. For example, height is an important factor in basketball, and weight and muscle mass are also taken into account. This identifies the sports that best suit the user's physical characteristics. Furthermore, the AI evaluates the user's potential for success in the identified sports. For example, if the user is determined to be suited to basketball, the AI evaluates the user's potential for success in that sports and provides specific training plans and advice. This system allows the user to find the sports that best suit their physical characteristics, thereby enabling even athletes who are overlooked in sports with a large number of participants to discover their potential to lead the world in other sports. For example, athletes who are not successful in baseball or soccer may be successful in track and field or swimming. This allows the sports event recommendation system to recommend the most suitable sports events based on the user's physical characteristics and provide training plans and advice.

The sports competition recommendation system according to the embodiment includes a collection unit, an analysis unit, a recommendation unit, and a provision unit. The collection unit collects data on the user's physical characteristics and growth process. Examples of the user's physical characteristics include, but are not limited to, height, weight, muscle mass, bone density, and flexibility. The collection unit collects data entered by the user, for example. The collection unit can also collect data using sensors or measuring devices. For example, the collection unit measures height and weight using a height chart or a weighing scale and collects the data. The collection unit can also use specialized devices for measuring muscle mass and bone density. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using AI and evaluates the user's physical characteristics. For example, the analysis unit analyzes success factors for each sports competition based on a past database and compares the results with the user's data. For example, the analysis unit analyzes that height is an important factor in basketball, and that weight and muscle mass are also taken into consideration. The recommendation unit identifies the sport that is most suitable for the user's physical characteristics based on the analysis results obtained by the analysis unit. The recommendation unit uses AI to recommend the optimal sport based on the user's physical characteristics. For example, the recommendation unit determines that the user is suited to basketball and recommends that sport. The provision unit provides training plans and advice based on the sport recommended by the recommendation unit. The provision unit uses AI to provide the user with specific training plans and advice. For example, the provision unit provides training menus, dietary advice, mental support, etc. that are suitable for basketball. In this way, the sport recommendation system according to the embodiment can recommend the optimal sport based on the user's physical characteristics and provide training plans and advice.

The collection unit collects data on the user's physical characteristics and growth process. Examples of the user's physical characteristics include, but are not limited to, height, weight, muscle mass, bone density, and flexibility. The collection unit collects data entered by the user. The collection unit can also collect data using sensors or measuring devices. For example, the collection unit may measure height and weight using a height chart or scale and collect the data. The collection unit can also use specialized devices for measuring muscle mass and bone density. Specifically, the user can input their own physical data using a smartphone or tablet. This data includes daily changes in weight and height, diet, and exercise volume. The collection unit can also collect data in real time using a wearable device. For example, a smartwatch or fitness tracker can be used to collect data such as heart rate, number of steps, calories burned, and sleep patterns. This allows for a more detailed understanding of the user's physical condition. The collection unit can also import measurement results from regular health checkups and sports clinics. This data includes blood test results and doctor's diagnoses. This allows for a comprehensive assessment of the user's health status and potential risks. The collection unit centrally manages information obtained from these diverse data sources and makes it accessible to the analysis and recommendation units. This allows the collection unit to gain a detailed and accurate understanding of the user's physical characteristics and growth process, improving the accuracy and reliability of the entire system.

The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the data and evaluate the user's physical characteristics. For example, the analysis unit analyzes success factors for each sport based on a past database and compares them with the user's data. Specifically, the AI uses machine learning algorithms to analyze the user's physical data and predict performance in specific sports. For example, it analyzes that height is an important factor in basketball, and weight and muscle mass are also taken into consideration. The AI comprehensively evaluates these factors and determines which sport the user is best suited to. Furthermore, the analysis unit can evaluate the user's growth process and training history to assess the potential for future performance improvement. For example, it predicts the extent to which performance improvement can be expected if the user maintains their current physical characteristics and engages in specific training. The analysis unit also evaluates the user's health status and potential risks, providing basic data for providing appropriate training plans and advice. This allows the analysis unit to perform a detailed evaluation of the user's physical characteristics and provide data for recommending optimal sports. Furthermore, the analysis unit can incorporate user feedback to continuously improve the accuracy of the analysis algorithm. This allows the analysis unit to always use the latest data and technology to recommend the most suitable sports events to users.

The recommendation unit identifies the sport that best suits the user's physical characteristics based on the analysis results obtained by the analysis unit. The recommendation unit uses AI to recommend the optimal sport based on the user's physical characteristics. For example, the recommendation unit may determine that the user is suited to basketball and recommend that sport. Specifically, the AI compares the user's physical data with past success stories to identify the most suitable sport. For example, if the user is tall and has a lot of muscle mass, it may determine that basketball or volleyball is suitable. Alternatively, if the user is flexible and has a good sense of balance, it may determine that gymnastics or figure skating is suitable. The recommendation unit recommends specific sports to the user based on this information. Furthermore, the recommendation unit can also consider the user's interests and preferences to recommend the optimal sport. For example, if the user is interested in a particular sport, it may preferentially recommend that sport. The recommendation unit can also suggest an appropriate training plan tailored to the user's lifestyle and schedule. This allows the recommendation unit to comprehensively consider the user's physical characteristics and interests and recommend the optimal sport. Furthermore, the recommendation unit can incorporate user feedback and continuously improve the accuracy of the recommendation algorithm. This allows the recommendation unit to always use the latest data and technology to recommend the most suitable sports events to the user.

The provision unit provides training plans and advice based on the sports event recommended by the recommendation unit. The provision unit uses AI to provide specific training plans and advice to the user. For example, the provision unit provides training menus, dietary advice, mental support, etc. suitable for basketball. Specifically, the AI creates an individually customized training plan based on the user's physical data and the recommended sports event. For example, a training menu suitable for basketball may include plyometric training to improve jumping power and aerobic exercise to increase endurance. Furthermore, the dietary advice may recommend a high-protein diet to increase muscle mass and carbohydrate intake to replenish energy. Furthermore, the mental support may provide relaxation techniques to relieve pre-game tension and mental training to improve concentration. The provision unit continuously provides these training plans and advice to the user to support the user's performance improvement. Furthermore, the provision unit can incorporate user feedback and continuously improve the content of the training plans and advice. As a result, the provision unit can provide the user with optimal training plans and advice and support success in sports events. Furthermore, the provider can monitor the user's progress and adjust the training plan as needed. This allows the provider to support the user in achieving their goals and improve their performance in sports competitions.

The collection unit can collect data on height, weight, muscle mass, bone density, and flexibility. For example, the collection unit collects data on height, weight, muscle mass, bone density, and flexibility entered by the user. For example, the collection unit measures height and weight using a height gauge or a bathroom scale and collects the data. The collection unit can also use dedicated equipment for measuring muscle mass. For example, the collection unit uses bioimpedance to measure muscle mass. Furthermore, the collection unit can use dual-energy X-ray absorptiometry (DXA) to measure bone density. For example, the collection unit measures bone density using DXA and collects the data. The collection unit can also use dedicated equipment for measuring flexibility. For example, the collection unit conducts a stretching test to measure flexibility and collects the data. This allows the collection unit to collect detailed data on the user's body. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data entered by the user into AI, which then collects the data.

The analysis unit can analyze the success factors for each sport based on the past database and compare them with the user's data. The analysis unit, for example, analyzes the success factors for each sport based on the past database. For example, the analysis unit analyzes that height is an important factor in basketball, with weight and muscle mass also being taken into consideration. The analysis unit can also analyze that speed and endurance are important factors in soccer. Furthermore, the analysis unit can analyze that muscle strength and flexibility are important factors in track and field. The analysis unit compares these success factors with the user's data. For example, the analysis unit evaluates the possibility of success in each sport based on the user's height, weight, muscle mass, bone density, and flexibility data. This allows the analysis unit to perform highly accurate analysis based on the user's physical characteristics. Some or all of the above-mentioned processing by the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the past database into AI, which then analyzes the success factors.

The recommendation unit can identify a sport that is appropriate for the user's physical characteristics. The recommendation unit can identify a sport that is most suitable for the user's physical characteristics, for example, based on the analysis results obtained by the analysis unit. For example, the recommendation unit can determine that the user is suited to basketball and recommend that sport. The recommendation unit can also determine that the user is suited to soccer and recommend that sport. The recommendation unit can also determine that the user is suited to track and field and recommend that sport. Based on these recommendations, the recommendation unit can suggest the most suitable sport for the user. This allows the recommendation unit to identify the most suitable sport based on the user's physical characteristics. Some or all of the above-mentioned processing in the recommendation unit can be performed using, or without, AI. For example, the recommendation unit can input the analysis results obtained by the analysis unit into AI, which can identify the most suitable sport.

The providing unit can evaluate the probability of success in the identified sport and provide specific training plans and advice. For example, the providing unit evaluates the probability of success in the sport identified by the recommendation unit. For example, if the providing unit determines that the user is suited to basketball, it evaluates the likelihood of success in that sport. Furthermore, if the providing unit determines that the user is suited to soccer, it can evaluate the likelihood of success in that sport. Furthermore, if the providing unit determines that the user is suited to track and field, it can evaluate the likelihood of success in that sport. Based on these evaluation results, the providing unit provides specific training plans and advice. For example, the providing unit can provide training menus, dietary advice, mental support, etc. suitable for basketball. Furthermore, the providing unit can provide training menus, dietary advice, mental support, etc. suitable for soccer. Furthermore, the providing unit can provide training menus, dietary advice, mental support, etc. suitable for track and field. In this way, the providing unit can increase the likelihood of success in the sport by providing the user with specific training plans and advice. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input the probability of success in a sporting event identified by the recommending unit into the AI, which can then provide specific training plans and advice.

The collection unit can analyze the user's past physical data and select the optimal collection method. The collection unit, for example, selects the optimal measurement method based on the user's past height and weight data. The collection unit, for example, selects the optimal muscle mass measurement method based on the user's past muscle mass data. The collection unit can also select the optimal flexibility measurement method based on the user's past flexibility data. This allows the collection unit to select the optimal collection method based on past physical data, enabling efficient data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the user's past physical data into AI, which then selects the optimal collection method.

When collecting data, the collection unit can filter the data based on the user's current health condition and lifestyle habits. For example, if the user is in good health, the collection unit collects detailed data. For example, if the user is in poor health, the collection unit collects only basic data. The collection unit can also select an appropriate data collection method based on the user's lifestyle habits. For example, the collection unit selects an appropriate data collection method based on data such as the user's dietary records, exercise habits, and sleep patterns. This enables the collection unit to collect data according to the user's health condition and lifestyle habits. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input data on the user's health condition and lifestyle habits into AI, which then performs the filtering.

When collecting data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. For example, if the user lives at high altitude, the collection unit can prioritize collecting data related to exercise at high altitude. For example, if the user lives in an urban area, the collection unit can also prioritize collecting data related to exercise in urban areas. Furthermore, if the user lives near the sea, the collection unit can prioritize collecting data related to swimming and beach sports. This enables the collection unit to collect data based on the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the user's geographical location information into AI, which can then prioritize collecting highly relevant data.

When collecting data, the collection unit can analyze the user's social media activity and collect related data. For example, the collection unit collects data related to exercises that the user frequently posts about on social media. For example, the collection unit can also collect data related to exercises performed by the user's social media friends. The collection unit can also collect data related to sports in which the user has shown interest on social media. This enables the collection unit to collect data based on the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input data on the user's social media activity into AI, which then collects the related data.

During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the physical data. For example, if height is an important factor, the analysis unit performs a detailed analysis of height. For example, if muscle mass is an important factor, the analysis unit performs a detailed analysis of muscle mass. Furthermore, if flexibility is an important factor, the analysis unit can also perform a detailed analysis of flexibility. This enables the analysis unit to perform a detailed analysis according to the importance of the physical data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the importance of the physical data to AI, which can adjust the level of detail of the analysis.

During analysis, the analysis unit can apply different analysis algorithms depending on the category of physical data. For example, the analysis unit applies a growth prediction algorithm to height data. For example, the analysis unit applies a muscle strength evaluation algorithm to muscle mass data. The analysis unit can also apply a flexibility evaluation algorithm to flexibility data. This enables the analysis unit to perform appropriate analysis depending on the category of physical data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the category of physical data into AI, which can then apply different analysis algorithms.

When analyzing, the analysis unit can determine the analysis priority based on when the data was collected. For example, the analysis unit prioritizes analysis of the most recent data. For example, the analysis unit can also perform analysis while referring to past data. The analysis unit can also focus its analysis on data from a specific period. This allows the analysis unit to perform analysis in priority order based on when the data was collected, enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the data collection time into AI, which can then determine the analysis priority.

The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit associates height and weight data and analyzes them. For example, the analysis unit can also associate muscle mass and flexibility data and analyze them. The analysis unit can also associate bone density and athletic ability data and analyze them. This enables the analysis unit to adjust the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI, which can then adjust the order of analysis.

When making recommendations, the recommendation unit can improve the accuracy of the recommendations by taking into account the interrelationships between physical data. For example, the recommendation unit makes recommendations by taking into account the interrelationships between height and weight. For example, the recommendation unit can also make recommendations by taking into account the interrelationships between muscle mass and flexibility. The recommendation unit can also make recommendations by taking into account the interrelationships between bone density and athletic ability. This allows the recommendation unit to make highly accurate recommendations by taking into account the interrelationships between physical data. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input the interrelationships between physical data into AI, which can improve the accuracy of recommendations.

When making a recommendation, the recommendation unit can take the user's attribute information into consideration. For example, the recommendation unit can make a recommendation taking the user's age into consideration. For example, the recommendation unit can make a recommendation taking the user's gender into consideration. The recommendation unit can also make a recommendation taking the user's exercise experience into consideration. This enables the recommendation unit to make recommendations based on the user's attribute information. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input the user's attribute information into AI, which then makes the recommendation.

When making recommendations, the recommendation unit can take the user's geographical distribution into consideration. For example, if the user lives at high altitude, the recommendation unit can make recommendations related to exercise at high altitude. For example, if the user lives in an urban area, the recommendation unit can make recommendations related to exercise in urban areas. Furthermore, if the user lives by the sea, the recommendation unit can make recommendations related to swimming and beach sports. This enables the recommendation unit to make recommendations based on the user's geographical distribution. Some or all of the above-mentioned processing in the recommendation unit may be performed using, or without, AI, for example. For example, the recommendation unit can input the user's geographical distribution into AI, which can then make recommendations.

When making a recommendation, the recommendation unit can improve the accuracy of the recommendation by referring to related literature. The recommendation unit, for example, makes recommendations by referring to the latest research papers. The recommendation unit can also make recommendations by referring to past success stories, for example. The recommendation unit can also make recommendations by referring to expert opinions. In this way, the recommendation unit improves the accuracy of recommendations by referring to related literature. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input related literature into AI, which can improve the accuracy of recommendations.

When providing the plan, the providing unit can refer to the user's past training history and provide the optimal plan. The providing unit, for example, provides the optimal training plan based on the user's past training history. The providing unit can also, for example, suggest effective training methods based on the user's past training history. The providing unit can also analyze the user's past training history and provide a training plan that includes areas for improvement. In this way, the providing unit can provide an optimal plan based on the past training history, enabling effective training. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past training history into AI, which can then provide the optimal training plan.

The providing unit can customize the training plan based on the user's current lifestyle at the time of provision. For example, if the user is busy, the providing unit can provide an effective training plan in a short amount of time. For example, if the user has time, the providing unit can also provide a detailed training plan. The providing unit can also customize an appropriate training plan based on the user's lifestyle habits. This enables the providing unit to customize a training plan according to the user's lifestyle. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's lifestyle into AI, which can then customize the training plan.

When providing the plan, the providing unit can provide the optimal plan taking into account the user's geographical location information. For example, if the user lives at high altitude, the providing unit can provide a plan suitable for training at high altitude. For example, if the user lives in an urban area, the providing unit can also provide a plan suitable for training in urban areas. Furthermore, if the user lives by the sea, the providing unit can also provide a plan suitable for swimming or beach sports. This enables the providing unit to provide the optimal plan based on the user's geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input the user's geographical location information into AI, which can then provide the optimal plan.

At the time of provision, the providing unit can analyze the user's social media activity and suggest training plans and advice. For example, the providing unit can provide training plans related to exercises that the user frequently posts about on social media. For example, the providing unit can also provide training plans related to exercises that the user's social media friends are doing. The providing unit can also provide training plans related to sports in which the user has shown interest on social media. This enables the providing unit to suggest training plans and advice based on the user's social media activity. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input data on the user's social media activity into AI, which can then suggest training plans and advice.

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

The analysis unit can include the user's dietary data in addition to the user's physical data in its analysis. For example, by collecting the user's dietary details and nutritional intake and combining this with physical data for analysis, it is possible to make more accurate recommendations for sports competitions. Furthermore, the analysis unit can provide dietary advice to supplement specific nutrients based on the user's dietary data if they are lacking in certain nutrients. This allows the user to find the sports competition that is best suited to them, not only in terms of their physical characteristics but also their diet.

The collection unit can collect the user's sleep data and provide it to the analysis unit. For example, by measuring the user's sleep time and sleep quality and combining and analyzing this with physical data, it becomes possible to make more comprehensive recommendations for sports competitions. Furthermore, the collection unit can provide advice on improving sleep if the user's sleep quality is declining based on the user's sleep data. This allows the user to find the sports competition that is best suited to them not only in terms of their physical characteristics but also their sleep.

The recommendation unit can recommend sports events taking into account the user's hobbies and interests in addition to the user's physical data. For example, if the user is interested in a particular sport, it can preferentially recommend sports related to that sport. Furthermore, the recommendation unit can also expand the selection of sports events based on the user's hobbies and interests. This allows the user to find the most suitable sports event based not only on their physical characteristics but also on their personal interests and hobbies.

The collection unit can collect the user's exercise history in addition to the user's physical data and provide it to the analysis unit. For example, by collecting the type, frequency, and intensity of past exercise and combining this with physical data and analyzing it, it becomes possible to make more accurate recommendations for sports competitions. Furthermore, the collection unit can identify training methods and approaches that have been successful in the past based on the user's exercise history and provide a training plan based on these. This allows the user to find the sports competition that is best suited to them, not only in terms of their physical characteristics but also their exercise history.

In addition to the user's physical data, the collection unit can collect data on the user's living environment and provide it to the analysis unit. For example, by collecting the user's living environment, work environment, commuting time, etc. and combining this with the physical data and analyzing it, it is possible to recommend sports that are suited to the user's living environment. Furthermore, the collection unit can provide training plans and advice suited to the user's living environment based on the user's living environment data. This allows the user to find the sports that are best suited to their living environment, not just their physical characteristics.

The recommendation unit can recommend sports events taking into account the user's social background in addition to their physical data. For example, it can recommend sports events that are suitable for the user's occupation, family environment, and social role. Furthermore, the recommendation unit can expand the sports event options based on the user's social background. This allows the user to find the most suitable sports event not only in terms of their physical characteristics but also their social background.

The processing flow for Example 1 is briefly explained below.

Step 1: The collection unit collects data on the user's physical characteristics and growth process. The user's physical characteristics include, for example, height, weight, muscle mass, bone density, and flexibility. The collection unit collects data using data entered by the user, sensors, and measuring devices. For example, height and weight can be measured using a height chart or a weight scale, and dedicated devices for measuring muscle mass and bone density can also be used.
Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the data and evaluate the user's physical characteristics. For example, based on a past database, it analyzes the success factors for each sport and compares them with the user's data. It analyzes that height is an important factor in basketball, and that weight and muscle mass are also taken into account.
Step 3: The recommendation unit identifies the sport that best suits the user's physical characteristics based on the analysis results obtained by the analysis unit. The recommendation unit uses AI to recommend the optimal sport based on the user's physical characteristics. For example, the recommendation unit determines that the user is suited to basketball and recommends that sport.
Step 4: The providing unit provides a training plan and advice based on the sports event recommended by the recommending unit. The providing unit uses AI to provide the user with specific training plans and advice. For example, the providing unit provides training menus, dietary advice, mental support, etc. suitable for basketball.

(Example 2)
A sports recommendation system according to an embodiment of the present invention recommends optimal sports based on a user's physical characteristics and provides training plans and advice. The system begins with the user inputting data about their physical characteristics and growth process. For example, the user inputs data such as height, weight, muscle mass, bone density, and flexibility. This data is then input into an AI. The AI then analyzes the input data. Based on a past database, the AI analyzes success factors for each sports and compares them with the user's data. For example, height is an important factor in basketball, and weight and muscle mass are also taken into account. This identifies the sports that best suit the user's physical characteristics. Furthermore, the AI evaluates the user's potential for success in the identified sports. For example, if the user is determined to be suited to basketball, the AI evaluates the user's potential for success in that sports and provides specific training plans and advice. This system allows the user to find the sports that best suit their physical characteristics, thereby enabling even athletes who are overlooked in sports with a large number of participants to discover their potential to lead the world in other sports. For example, athletes who are not successful in baseball or soccer may be successful in track and field or swimming. This allows the sports event recommendation system to recommend the most suitable sports events based on the user's physical characteristics and provide training plans and advice.

The sports competition recommendation system according to the embodiment includes a collection unit, an analysis unit, a recommendation unit, and a provision unit. The collection unit collects data on the user's physical characteristics and growth process. Examples of the user's physical characteristics include, but are not limited to, height, weight, muscle mass, bone density, and flexibility. The collection unit collects data entered by the user, for example. The collection unit can also collect data using sensors or measuring devices. For example, the collection unit measures height and weight using a height chart or a weighing scale and collects the data. The collection unit can also use specialized devices for measuring muscle mass and bone density. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using AI and evaluates the user's physical characteristics. For example, the analysis unit analyzes success factors for each sports competition based on a past database and compares the results with the user's data. For example, the analysis unit analyzes that height is an important factor in basketball, and that weight and muscle mass are also taken into consideration. The recommendation unit identifies the sport that is most suitable for the user's physical characteristics based on the analysis results obtained by the analysis unit. The recommendation unit uses AI to recommend the optimal sport based on the user's physical characteristics. For example, the recommendation unit determines that the user is suited to basketball and recommends that sport. The provision unit provides training plans and advice based on the sport recommended by the recommendation unit. The provision unit uses AI to provide the user with specific training plans and advice. For example, the provision unit provides training menus, dietary advice, mental support, etc. that are suitable for basketball. In this way, the sport recommendation system according to the embodiment can recommend the optimal sport based on the user's physical characteristics and provide training plans and advice.

The collection unit collects data on the user's physical characteristics and growth process. Examples of the user's physical characteristics include, but are not limited to, height, weight, muscle mass, bone density, and flexibility. The collection unit collects data entered by the user. The collection unit can also collect data using sensors or measuring devices. For example, the collection unit may measure height and weight using a height chart or scale and collect the data. The collection unit can also use specialized devices for measuring muscle mass and bone density. Specifically, the user can input their own physical data using a smartphone or tablet. This data includes daily changes in weight and height, diet, and exercise volume. The collection unit can also collect data in real time using a wearable device. For example, a smartwatch or fitness tracker can be used to collect data such as heart rate, number of steps, calories burned, and sleep patterns. This allows for a more detailed understanding of the user's physical condition. The collection unit can also import measurement results from regular health checkups and sports clinics. This data includes blood test results and doctor's diagnoses. This allows for a comprehensive assessment of the user's health status and potential risks. The collection unit centrally manages information obtained from these diverse data sources and makes it accessible to the analysis and recommendation units. This allows the collection unit to gain a detailed and accurate understanding of the user's physical characteristics and growth process, improving the accuracy and reliability of the entire system.

The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the data and evaluate the user's physical characteristics. For example, the analysis unit analyzes success factors for each sport based on a past database and compares them with the user's data. Specifically, the AI uses machine learning algorithms to analyze the user's physical data and predict performance in specific sports. For example, it analyzes that height is an important factor in basketball, and weight and muscle mass are also taken into consideration. The AI comprehensively evaluates these factors and determines which sport the user is best suited to. Furthermore, the analysis unit can evaluate the user's growth process and training history to assess the potential for future performance improvement. For example, it predicts the extent to which performance improvement can be expected if the user maintains their current physical characteristics and engages in specific training. The analysis unit also evaluates the user's health status and potential risks, providing basic data for providing appropriate training plans and advice. This allows the analysis unit to perform a detailed evaluation of the user's physical characteristics and provide data for recommending optimal sports. Furthermore, the analysis unit can incorporate user feedback to continuously improve the accuracy of the analysis algorithm. This allows the analysis unit to always use the latest data and technology to recommend the most suitable sports events to users.

The recommendation unit identifies the sport that best suits the user's physical characteristics based on the analysis results obtained by the analysis unit. The recommendation unit uses AI to recommend the optimal sport based on the user's physical characteristics. For example, the recommendation unit may determine that the user is suited to basketball and recommend that sport. Specifically, the AI compares the user's physical data with past success stories to identify the most suitable sport. For example, if the user is tall and has a lot of muscle mass, it may determine that basketball or volleyball is suitable. Alternatively, if the user is flexible and has a good sense of balance, it may determine that gymnastics or figure skating is suitable. The recommendation unit recommends specific sports to the user based on this information. Furthermore, the recommendation unit can also consider the user's interests and preferences to recommend the optimal sport. For example, if the user is interested in a particular sport, it may preferentially recommend that sport. The recommendation unit can also suggest an appropriate training plan tailored to the user's lifestyle and schedule. This allows the recommendation unit to comprehensively consider the user's physical characteristics and interests and recommend the optimal sport. Furthermore, the recommendation unit can incorporate user feedback and continuously improve the accuracy of the recommendation algorithm. This allows the recommendation unit to always use the latest data and technology to recommend the most suitable sports events to the user.

The provision unit provides training plans and advice based on the sports event recommended by the recommendation unit. The provision unit uses AI to provide specific training plans and advice to the user. For example, the provision unit provides training menus, dietary advice, mental support, etc. suitable for basketball. Specifically, the AI creates an individually customized training plan based on the user's physical data and the recommended sports event. For example, a training menu suitable for basketball may include plyometric training to improve jumping power and aerobic exercise to increase endurance. Furthermore, the dietary advice may recommend a high-protein diet to increase muscle mass and carbohydrate intake to replenish energy. Furthermore, the mental support may provide relaxation techniques to relieve pre-game tension and mental training to improve concentration. The provision unit continuously provides these training plans and advice to the user to support the user's performance improvement. Furthermore, the provision unit can incorporate user feedback and continuously improve the content of the training plans and advice. As a result, the provision unit can provide the user with optimal training plans and advice and support success in sports events. Furthermore, the provider can monitor the user's progress and adjust the training plan as needed. This allows the provider to support the user in achieving their goals and improve their performance in sports competitions.

The collection unit can collect data on height, weight, muscle mass, bone density, and flexibility. For example, the collection unit collects data on height, weight, muscle mass, bone density, and flexibility entered by the user. For example, the collection unit measures height and weight using a height gauge or a bathroom scale and collects the data. The collection unit can also use dedicated equipment for measuring muscle mass. For example, the collection unit uses bioimpedance to measure muscle mass. Furthermore, the collection unit can use dual-energy X-ray absorptiometry (DXA) to measure bone density. For example, the collection unit measures bone density using DXA and collects the data. The collection unit can also use dedicated equipment for measuring flexibility. For example, the collection unit conducts a stretching test to measure flexibility and collects the data. This allows the collection unit to collect detailed data on the user's body. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data entered by the user into AI, which then collects the data.

The analysis unit can analyze the success factors for each sport based on the past database and compare them with the user's data. The analysis unit, for example, analyzes the success factors for each sport based on the past database. For example, the analysis unit analyzes that height is an important factor in basketball, with weight and muscle mass also being taken into consideration. The analysis unit can also analyze that speed and endurance are important factors in soccer. Furthermore, the analysis unit can analyze that muscle strength and flexibility are important factors in track and field. The analysis unit compares these success factors with the user's data. For example, the analysis unit evaluates the possibility of success in each sport based on the user's height, weight, muscle mass, bone density, and flexibility data. This allows the analysis unit to perform highly accurate analysis based on the user's physical characteristics. Some or all of the above-mentioned processing by the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the past database into AI, which then analyzes the success factors.

The recommendation unit can identify a sport that is appropriate for the user's physical characteristics. The recommendation unit can identify a sport that is most suitable for the user's physical characteristics, for example, based on the analysis results obtained by the analysis unit. For example, the recommendation unit can determine that the user is suited to basketball and recommend that sport. The recommendation unit can also determine that the user is suited to soccer and recommend that sport. The recommendation unit can also determine that the user is suited to track and field and recommend that sport. Based on these recommendations, the recommendation unit can suggest the most suitable sport for the user. This allows the recommendation unit to identify the most suitable sport based on the user's physical characteristics. Some or all of the above-mentioned processing in the recommendation unit can be performed using, or without, AI. For example, the recommendation unit can input the analysis results obtained by the analysis unit into AI, which can identify the most suitable sport.

The providing unit can evaluate the probability of success in the identified sport and provide specific training plans and advice. For example, the providing unit evaluates the probability of success in the sport identified by the recommendation unit. For example, if the providing unit determines that the user is suited to basketball, it evaluates the likelihood of success in that sport. Furthermore, if the providing unit determines that the user is suited to soccer, it can evaluate the likelihood of success in that sport. Furthermore, if the providing unit determines that the user is suited to track and field, it can evaluate the likelihood of success in that sport. Based on these evaluation results, the providing unit provides specific training plans and advice. For example, the providing unit can provide training menus, dietary advice, mental support, etc. suitable for basketball. Furthermore, the providing unit can provide training menus, dietary advice, mental support, etc. suitable for soccer. Furthermore, the providing unit can provide training menus, dietary advice, mental support, etc. suitable for track and field. In this way, the providing unit can increase the likelihood of success in the sport by providing the user with specific training plans and advice. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input the probability of success in a sporting event identified by the recommending unit into the AI, which can then provide specific training plans and advice.

The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions. For example, if the user is relaxed, the collection unit immediately collects data and acquires detailed information. Furthermore, if the user is feeling stressed, the collection unit can postpone data collection and wait until the user is relaxed. Furthermore, if the user is excited, the collection unit can quickly collect data and acquire the necessary information in a short period of time. This allows the collection unit to adjust the timing of data collection according to the user's emotions, enabling more accurate data collection. 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) and multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input user emotion data into the generation AI, which can then adjust the timing of data collection.

The collection unit can analyze the user's past physical data and select the optimal collection method. The collection unit, for example, selects the optimal measurement method based on the user's past height and weight data. The collection unit, for example, selects the optimal muscle mass measurement method based on the user's past muscle mass data. The collection unit can also select the optimal flexibility measurement method based on the user's past flexibility data. This allows the collection unit to select the optimal collection method based on past physical data, enabling efficient data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the user's past physical data into AI, which then selects the optimal collection method.

When collecting data, the collection unit can filter the data based on the user's current health condition and lifestyle habits. For example, if the user is in good health, the collection unit collects detailed data. For example, if the user is in poor health, the collection unit collects only basic data. The collection unit can also select an appropriate data collection method based on the user's lifestyle habits. For example, the collection unit selects an appropriate data collection method based on data such as the user's dietary records, exercise habits, and sleep patterns. This enables the collection unit to collect data according to the user's health condition and lifestyle habits. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input data on the user's health condition and lifestyle habits into AI, which then performs the filtering.

The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, the collection unit estimates the user's emotions and determines the priority of data to be collected based on the estimated user emotions. For example, when the user is relaxed, the collection unit prioritizes collecting detailed data. Furthermore, when the user is feeling stressed, the collection unit can also prioritize collecting basic data. Furthermore, when the user is excited, the collection unit can prioritize collecting data that can be collected quickly. This enables the collection unit to prioritize data according to the user's emotions, enabling efficient data collection. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit can input the user's emotion data into the generative AI, which then determines the priority of the data.

When collecting data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. For example, if the user lives at high altitude, the collection unit can prioritize collecting data related to exercise at high altitude. For example, if the user lives in an urban area, the collection unit can also prioritize collecting data related to exercise in urban areas. Furthermore, if the user lives near the sea, the collection unit can prioritize collecting data related to swimming and beach sports. This enables the collection unit to collect data based on the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the user's geographical location information into AI, which can then prioritize collecting highly relevant data.

When collecting data, the collection unit can analyze the user's social media activity and collect related data. For example, the collection unit collects data related to exercises that the user frequently posts about on social media. For example, the collection unit can also collect data related to exercises performed by the user's social media friends. The collection unit can also collect data related to sports in which the user has shown interest on social media. This enables the collection unit to collect data based on the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input data on the user's social media activity into AI, which then collects the related data.

The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is stressed. Furthermore, the analysis unit can provide visually appealing analysis results when the user is excited. This enables the analysis unit to provide analysis results that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input user emotion data into generative AI, which then adjusts the presentation method of the analysis.

During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the physical data. For example, if height is an important factor, the analysis unit performs a detailed analysis of height. For example, if muscle mass is an important factor, the analysis unit performs a detailed analysis of muscle mass. Furthermore, if flexibility is an important factor, the analysis unit can also perform a detailed analysis of flexibility. This enables the analysis unit to perform a detailed analysis according to the importance of the physical data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the importance of the physical data to AI, which can adjust the level of detail of the analysis.

During analysis, the analysis unit can apply different analysis algorithms depending on the category of physical data. For example, the analysis unit applies a growth prediction algorithm to height data. For example, the analysis unit applies a muscle strength evaluation algorithm to muscle mass data. The analysis unit can also apply a flexibility evaluation algorithm to flexibility data. This enables the analysis unit to perform appropriate analysis depending on the category of physical data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the category of physical data into AI, which can then apply different analysis algorithms.

The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, the analysis unit performs a detailed analysis when the user is relaxed. The analysis unit can also perform a concise analysis when the user is stressed. Furthermore, the analysis unit can perform a visually appealing analysis when the user is excited. This allows the analysis unit to provide appropriate analysis results by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input the user's emotion data into the generative AI, which then adjusts the length of the analysis.

When analyzing, the analysis unit can determine the analysis priority based on when the data was collected. For example, the analysis unit prioritizes analysis of the most recent data. For example, the analysis unit can also perform analysis while referring to past data. The analysis unit can also focus its analysis on data from a specific period. This allows the analysis unit to perform analysis in priority order based on when the data was collected, enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the data collection time into AI, which can then determine the analysis priority.

The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit associates height and weight data and analyzes them. For example, the analysis unit can also associate muscle mass and flexibility data and analyze them. The analysis unit can also associate bone density and athletic ability data and analyze them. This enables the analysis unit to adjust the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI, which can then adjust the order of analysis.

The recommendation unit can estimate the user's emotions and adjust the recommendation criteria based on the estimated user emotions. For example, the recommendation unit can estimate the user's emotions and adjust the recommendation criteria based on the estimated user emotions. For example, the recommendation unit can provide detailed recommendations when the user is relaxed. The recommendation unit can also provide concise recommendations when the user is stressed. Furthermore, the recommendation unit can provide visually appealing recommendations when the user is excited. This enables the recommendation unit to adjust the recommendation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, or may be performed without AI. For example, the recommendation unit can input user emotion data into the generative AI, which can then adjust the recommendation criteria.

When making recommendations, the recommendation unit can improve the accuracy of the recommendations by taking into account the interrelationships between physical data. For example, the recommendation unit makes recommendations by taking into account the interrelationships between height and weight. For example, the recommendation unit can also make recommendations by taking into account the interrelationships between muscle mass and flexibility. The recommendation unit can also make recommendations by taking into account the interrelationships between bone density and athletic ability. This allows the recommendation unit to make highly accurate recommendations by taking into account the interrelationships between physical data. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input the interrelationships between physical data into AI, which can improve the accuracy of recommendations.

When making a recommendation, the recommendation unit can take the user's attribute information into consideration. For example, the recommendation unit can make a recommendation taking the user's age into consideration. For example, the recommendation unit can make a recommendation taking the user's gender into consideration. The recommendation unit can also make a recommendation taking the user's exercise experience into consideration. This enables the recommendation unit to make recommendations based on the user's attribute information. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input the user's attribute information into AI, which then makes the recommendation.

The recommendation unit can estimate the user's emotions and adjust the display order of recommended results based on the estimated user emotions. For example, the recommendation unit can estimate the user's emotions and adjust the display order of recommended results based on the estimated user emotions. For example, if the user is relaxed, the recommendation unit can prioritize displaying detailed recommended results. Furthermore, if the user is stressed, the recommendation unit can prioritize displaying concise recommended results. Furthermore, if the user is excited, the recommendation unit can prioritize displaying visually appealing recommended results. This allows the recommendation unit to provide more appropriate recommended results by adjusting the display order of recommended results 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 can 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 recommendation unit may be performed using AI, or without AI. For example, the recommendation unit can input user emotion data into the generative AI and adjust the order in which the generative AI displays recommended results.

When making recommendations, the recommendation unit can take the user's geographical distribution into consideration. For example, if the user lives at high altitude, the recommendation unit can make recommendations related to exercise at high altitude. For example, if the user lives in an urban area, the recommendation unit can make recommendations related to exercise in urban areas. Furthermore, if the user lives by the sea, the recommendation unit can make recommendations related to swimming and beach sports. This enables the recommendation unit to make recommendations based on the user's geographical distribution. Some or all of the above-mentioned processing in the recommendation unit may be performed using, or without, AI, for example. For example, the recommendation unit can input the user's geographical distribution into AI, which can then make recommendations.

When making a recommendation, the recommendation unit can improve the accuracy of the recommendation by referring to related literature. The recommendation unit, for example, makes recommendations by referring to the latest research papers. The recommendation unit can also make recommendations by referring to past success stories, for example. The recommendation unit can also make recommendations by referring to expert opinions. In this way, the recommendation unit improves the accuracy of recommendations by referring to related literature. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input related literature into AI, which can improve the accuracy of recommendations.

The providing unit can estimate the user's emotions and adjust the method of providing the training plan and advice based on the estimated user emotions. For example, the providing unit can estimate the user's emotions and adjust the method of providing the training plan and advice based on the estimated user emotions. For example, the providing unit can provide a detailed training plan when the user is relaxed. Furthermore, the providing unit can provide a concise training plan when the user is stressed. Furthermore, the providing unit can provide a visually appealing training plan when the user is excited. This enables the providing unit to adjust the method of providing the training plan and advice according to the user's emotions, thereby enabling more appropriate support. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can input the user's emotion data into the generative AI, which can then adjust the method of providing the training plan and advice.

When providing the plan, the providing unit can refer to the user's past training history and provide the optimal plan. The providing unit, for example, provides the optimal training plan based on the user's past training history. The providing unit can also, for example, suggest effective training methods based on the user's past training history. The providing unit can also analyze the user's past training history and provide a training plan that includes areas for improvement. In this way, the providing unit can provide an optimal plan based on the past training history, enabling effective training. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past training history into AI, which can then provide the optimal training plan.

The providing unit can customize the training plan based on the user's current lifestyle at the time of provision. For example, if the user is busy, the providing unit can provide an effective training plan in a short amount of time. For example, if the user has time, the providing unit can also provide a detailed training plan. The providing unit can also customize an appropriate training plan based on the user's lifestyle habits. This enables the providing unit to customize a training plan according to the user's lifestyle. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's lifestyle into AI, which can then customize the training plan.

The providing unit can estimate the user's emotions and prioritize training plans and advice based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and prioritizes training plans and advice based on the estimated user emotions. For example, if the user is relaxed, the providing unit can prioritize providing detailed training plans. Furthermore, if the user is feeling stressed, the providing unit can prioritize providing concise training plans. Furthermore, if the user is excited, the providing unit can prioritize visually appealing training plans. This enables the providing unit to provide training plans and advice in order of priority based on the user's emotions, thereby enabling more effective support. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the user's emotional data into the generation AI, which can then determine the priority of training plans and advice.

When providing the plan, the providing unit can provide the optimal plan taking into account the user's geographical location information. For example, if the user lives at high altitude, the providing unit can provide a plan suitable for training at high altitude. For example, if the user lives in an urban area, the providing unit can also provide a plan suitable for training in urban areas. Furthermore, if the user lives by the sea, the providing unit can also provide a plan suitable for swimming or beach sports. This enables the providing unit to provide the optimal plan based on the user's geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input the user's geographical location information into AI, which can then provide the optimal plan.

At the time of provision, the providing unit can analyze the user's social media activity and suggest training plans and advice. For example, the providing unit can provide training plans related to exercises that the user frequently posts about on social media. For example, the providing unit can also provide training plans related to exercises that the user's social media friends are doing. The providing unit can also provide training plans related to sports in which the user has shown interest on social media. This enables the providing unit to suggest training plans and advice based on the user's social media activity. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input data on the user's social media activity into AI, which can then suggest training plans and advice.

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

The analysis unit can include the user's dietary data in addition to the user's physical data in its analysis. For example, by collecting the user's dietary details and nutritional intake and combining this with physical data for analysis, it is possible to make more accurate recommendations for sports competitions. Furthermore, the analysis unit can provide dietary advice to supplement specific nutrients based on the user's dietary data if they are lacking in certain nutrients. This allows the user to find the sports competition that is best suited to them, not only in terms of their physical characteristics but also their diet.

The collection unit can collect the user's sleep data and provide it to the analysis unit. For example, by measuring the user's sleep time and sleep quality and combining and analyzing this with physical data, it becomes possible to make more comprehensive recommendations for sports competitions. Furthermore, the collection unit can provide advice on how to improve sleep if the user's sleep quality is declining based on the user's sleep data. This allows the user to find the sports competition that is best suited to them not only in terms of their physical characteristics but also their sleep.

In addition to the user's physical data, the analysis unit can also include the user's stress level in its analysis. For example, by measuring the user's stress level and analyzing it in combination with the physical data, it can recommend sports that can be played under low stress. Furthermore, if the user's stress level is high, the analysis unit can also provide relaxation methods and mental support to reduce stress. This allows the user to find the sports that are best suited to them not only in terms of their physical characteristics but also in terms of stress.

The recommendation unit can recommend sports events taking into account the user's hobbies and interests in addition to the user's physical data. For example, if the user is interested in a particular sport, it can preferentially recommend sports related to that sport. Furthermore, the recommendation unit can also expand the selection of sports events based on the user's hobbies and interests. This allows the user to find the most suitable sports event based not only on their physical characteristics but also on their personal interests and hobbies.

The provider can provide training plans and advice taking into account the user's motivation level in addition to the user's physical data. For example, if the user is highly motivated, a more challenging training plan can be provided, and if the user is less motivated, a simpler, more achievable training plan can be provided. Furthermore, the provider can also provide mental support and encouraging messages to maintain the user's motivation. This allows the user to receive a training plan that is optimal not only for their physical characteristics but also for their motivation.

In addition to the user's physical data, the collection unit can collect the user's exercise history and provide it to the analysis unit. For example, by collecting the type, frequency, and intensity of past exercise and combining this with physical data and analyzing it, it becomes possible to make more accurate recommendations for sports competitions. Furthermore, the collection unit can identify training methods and approaches that have been successful in the past based on the user's exercise history and provide a training plan based on these. This allows the user to find the sports competition that is best suited to them, not only in terms of their physical characteristics but also their exercise history.

In addition to the user's physical data, the collection unit can collect data on the user's living environment and provide it to the analysis unit. For example, by collecting the user's living environment, work environment, commuting time, etc. and combining this with the physical data and analyzing it, it is possible to recommend sports that are suited to the user's living environment. Furthermore, the collection unit can provide training plans and advice suited to the user's living environment based on the user's living environment data. This allows the user to find the sports that are best suited to their living environment, not just their physical characteristics.

The analysis unit can include the user's emotional data in addition to the user's physical data in the analysis. For example, based on the user's emotional data, it can recommend sports that can be played with minimal emotional fluctuations. Furthermore, based on the user's emotional data, the analysis unit can also provide mental support and relaxation methods to stabilize emotions. This allows the user to find the sports that are best suited to them not only in terms of their physical characteristics but also their emotions.

The recommendation unit can recommend sports events taking into account the user's social background in addition to their physical data. For example, it can recommend sports events that are suitable for the user's occupation, family environment, and social role. Furthermore, the recommendation unit can expand the sports event options based on the user's social background. This allows the user to find the most suitable sports event not only in terms of their physical characteristics but also their social background.

The provision unit can provide training plans and advice that take into account the user's emotional data in addition to their physical data. For example, it can provide a training plan that can be carried out with minimal emotional fluctuations based on the user's emotional data. Furthermore, the provision unit can also provide mental support and relaxation methods to stabilize emotions based on the user's emotional data. This allows the user to receive a training plan that is optimal not only for their physical characteristics but also for their emotions.

The processing flow for Example 2 is briefly explained below.

Step 1: The collection unit collects data on the user's physical characteristics and growth process. The user's physical characteristics include, for example, height, weight, muscle mass, bone density, and flexibility. The collection unit collects data using data entered by the user, sensors, and measuring devices. For example, height and weight can be measured using a height chart or a weight scale, and dedicated devices for measuring muscle mass and bone density can also be used.
Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the data and evaluate the user's physical characteristics. For example, based on a past database, it analyzes the success factors for each sport and compares them with the user's data. It analyzes that height is an important factor in basketball, and that weight and muscle mass are also taken into account.
Step 3: The recommendation unit identifies the sport that best suits the user's physical characteristics based on the analysis results obtained by the analysis unit. The recommendation unit uses AI to recommend the optimal sport based on the user's physical characteristics. For example, the recommendation unit determines that the user is suited to basketball and recommends that sport.
Step 4: The providing unit provides a training plan and advice based on the sports event recommended by the recommending unit. The providing unit uses AI to provide the user with specific training plans and advice. For example, the providing unit provides training menus, dietary advice, mental support, etc. suitable for basketball.

The specific processing unit 290 transmits the results of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the results of the specific processing. The microphone 38B acquires audio indicating the user input regarding the results of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B 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 what is known as generative AI (artificial intelligence). An example of a data generation model 58 is generative AI such as ChatGPT (registered trademark) (Internet search <URL: https://openai.com/blog/chatgpt>). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data) is also input. The data generation model 58 performs inference on the input inference data in accordance with the instructions indicated by the prompt and outputs the inference results in one or more data formats, such as voice 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not include instructions. In this case, the data generation model 58 can output inference results from prompts that do not include instructions. The data processing device 12 or the like includes multiple types of data generation models 58, and the data generation model 58 includes AI other than the generation AI. Examples of AI other than the generation AI include 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), and naive Bayes, and can perform various processes, but are not limited to these examples. The AI may also be an AI agent. When the processing of each of the above-mentioned parts is performed by AI, the processing may be performed in part or in whole by AI, but is not limited to these examples. In addition, processing performed by AI, including generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generation AI.

Furthermore, the processing 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 may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, etc., and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device, etc.

Each of the multiple elements including the collection unit, analysis unit, recommendation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the user's physical data using sensors and measuring devices in the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using AI. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends the optimal sports event based on the analysis results. The provision unit is realized by the control unit 46A of the smart device 14 and provides training plans and advice based on the recommended sports event. The correspondence between each unit and the device or control unit is not limited to the example described above and various modifications are possible.

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

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

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 includes a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN and/or a LAN.

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

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

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

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

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

The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

Storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including estimation and prediction of the user's emotion, but is 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. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and emotion identification model similar to the data generation model 58 and emotion identification model 59, and can use these models to perform processing similar to that of the specific processing unit 290.

Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain processing results (such as prediction results) using the data generation model 58. The data processing device 12 may also be a server device, or a terminal device owned by a user (for example, a mobile phone, robot, home appliance, etc.).

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

The data generation model 58 is what is known as generative AI. An example of a data generation model 58 is generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as voice 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not include instructions. In this case, the data generation model 58 can output inference results from prompts that do not include instructions. The data processing device 12 or the like includes multiple types of data generation models 58, and the data generation model 58 includes AI other than the generation AI. Examples of AI other than the generation AI include 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), and naive Bayes, and can perform various processes, but are not limited to these examples. The AI may also be an AI agent. When the processing of each of the above-mentioned parts is performed by AI, the processing may be performed in part or in whole by AI, but is not limited to these examples. In addition, processing performed by AI, including generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generation AI.

The data processing system 210 according to the second embodiment performs processing similar to that of the data processing system 10 according to the first embodiment. 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, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device, etc.

Each of the multiple elements, including the collection unit, analysis unit, recommendation unit, and provision unit described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the user's physical data using sensors and measuring devices in the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using AI. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends the optimal sports event based on the analysis results. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides training plans and advice based on the recommended sports event. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

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

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 includes a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN and/or a LAN.

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

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

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

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

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

The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

In the headset type terminal 314, the specific processing is performed by the processor 46. A specific program 60 is stored in the storage 50. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The headset type terminal 314 also has a data generation model and emotion identification model similar to the data generation model 58 and emotion identification model 59, and can use these models to perform processing similar to that of the identification processing unit 290.

Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain processing results (such as prediction results) using the data generation model 58. The data processing device 12 may also be a server device, or a terminal device owned by a user (for example, a mobile phone, robot, home appliance, etc.).

The specific processing unit 290 transmits the results of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the results of the specific processing. The microphone 238 acquires audio indicating the user input regarding the results of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

The data generation model 58 is what is known as generative AI. An example of a data generation model 58 is generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as voice 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not include instructions. In this case, the data generation model 58 can output inference results from prompts that do not include instructions. The data processing device 12 or the like includes multiple types of data generation models 58, and the data generation model 58 includes AI other than the generation AI. Examples of AI other than the generation AI include 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), and naive Bayes, and can perform various processes, but are not limited to these examples. The AI may also be an AI agent. When the processing of each of the above-mentioned parts is performed by AI, the processing may be performed in part or in whole by AI, but is not limited to these examples. In addition, processing performed by AI, including generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generation 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. 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, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

Each of the multiple elements including the collection unit, analysis unit, recommendation unit, and provision unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects the user's physical data using sensors and measuring devices in the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using AI. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends the optimal sports event based on the analysis results. The provision unit is realized by the control unit 46A of the headset-type terminal 314 and provides training plans and advice based on the recommended sports event. The correspondence between each unit and the device or control unit is not limited to the example described above and various modifications are possible.

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

As shown in FIG. 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 includes a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN and/or a LAN.

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

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

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

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

The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32.

The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

In robot 414, specific processing is performed by processor 46. Specific program 60 is stored in storage 50. Processor 46 reads specific program 60 from storage 50 and executes the read specific program 60 on RAM 48. The specific processing is realized by processor 46 operating as control unit 46A in accordance with specific program 60 executed on RAM 48. Robot 414 also has a data generation model and emotion identification model similar to data generation model 58 and emotion identification model 59, and can use these models to perform processing similar to that of identification processing unit 290.

Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain processing results (such as prediction results) using the data generation model 58. The data processing device 12 may also be a server device, or a terminal device owned by a user (for example, a mobile phone, robot, home appliance, etc.).

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

The data generation model 58 is what is known as generative AI. An example of a data generation model 58 is generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as voice 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not include instructions. In this case, the data generation model 58 can output inference results from prompts that do not include instructions. The data processing device 12 or the like includes multiple types of data generation models 58, and the data generation model 58 includes AI other than the generation AI. Examples of AI other than the generation AI include 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), and naive Bayes, and can perform various processes, but are not limited to these examples. The AI may also be an AI agent. When the processing of each of the above-mentioned parts is performed by AI, the processing may be performed in part or in whole by AI, but is not limited to these examples. In addition, processing performed by AI, including generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generation AI.

The data processing system 410 according to the fourth embodiment performs processing similar to that of the data processing system 10 according to the first embodiment. 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 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

Each of the multiple elements including the collection unit, analysis unit, recommendation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the user's physical data using sensors and measuring devices in the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using AI. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends the optimal sports competition based on the analysis results. The provision unit is realized by the control unit 46A of the robot 414 and provides training plans and advice based on the recommended sports competition. The correspondence between each unit and the device or control unit is not limited to the example described above and various modifications are possible.

The emotion identification model 59, which serves as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see Figure 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

Figure 9 shows an emotion map 400 on which multiple emotions are mapped. In emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both emotions and mental states. Emotions that are generally generated from reactions that occur in the brain are arranged on the left side of the concentric circles. Emotions that are generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, emotion map 400 maps multiple emotions based on the structure by which emotions are generated, with emotions that tend to occur simultaneously being mapped close together.

These emotions are distributed in the 3 o'clock direction on emotion map 400, and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness takes precedence over internal sensations, resulting in a sense of calm.

The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible (expressed in behavior) the emotion becomes.

Here, human emotions are based on various balances such as posture and blood sugar levels, and when these balances deviate from the ideal, it indicates discomfort, and when they approach the ideal, it indicates pleasure. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and remaining battery life, and when these balances deviate from the ideal, it indicates discomfort, and when they approach the ideal, it indicates pleasure. Emotion maps can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and emotional brain physiological signal analysis systems, Tokushima University, doctoral dissertation: https://ci.nii.ac.jp/naid/500000375379). The left half of the emotion map is lined with emotions belonging to an area called "reaction," where sensation is dominant. The right half of the emotion map is lined with emotions belonging to an area called "situation," where situational awareness is dominant.

The emotion map defines two emotions that encourage learning. One is the negative emotion around the middle of "repentance" or "reflection" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot experiences positive feelings such as "I want more" or "I want to know more."

The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are close to each other have similar values, as in the emotion map 900 shown in Figure 10. Figure 10 shows an example in which multiple emotions, such as "relieved," "calm," and "reassuring," have similar emotion values.

In the above embodiment, an example was given in which a specific process is performed by a single computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may also be performed by multiple computers, including computer 22.

In the above embodiment, an example was described in which the specific processing program 56 is stored in the storage 32, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

It is not necessary to store the entire specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entire specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

The following types of processors can be used as hardware resources for executing specific processes. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource for executing specific processes by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which are processors with a circuit configuration designed specifically for executing specific processes. All processors have built-in or connected memory, and all use the memory to execute specific processes.

The hardware resource that executes the specific processing may be composed of one of these various processors, or may be composed of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). The hardware resource that executes the specific processing may also be a single processor.

As an example of a configuration using a single processor, first, there is a configuration in which one processor is configured using a combination of one or more CPUs and software, and this processor functions as a hardware resource that executes specific processing. Second, there is a configuration in which a processor is used to realize the functions of an entire system, including multiple hardware resources that execute specific processing, on a single IC chip, as typified by SoC (System-on-a-chip). In this way, specific processing is realized using one or more of the various processors listed above as hardware resources.

More specifically, the hardware structure of these various processors can be an electrical circuit that combines circuit elements such as semiconductor devices. Furthermore, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps can be added, or the processing order can be rearranged, all within the scope of the spirit of the invention.

Furthermore, in the above examples, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. Furthermore, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are only examples, and they may be combined, or other devices may be used. Furthermore, in the above examples, the first and second embodiments have been described separately, but these may also be combined.

The above-described written content and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above explanation of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or substitutions may be made to the above-described written content and illustrations, as long as they do not deviate from the spirit of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described written content and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

All publications, patent applications, and technical standards mentioned in this specification are incorporated herein by reference to the same extent as if each individual publication, patent application, and technical standard was specifically and individually indicated to be incorporated by reference.

(Appendix 1)
a collection unit that collects data on the user's physical characteristics and growth process;
an analysis unit that analyzes the data collected by the collection unit;
a recommendation unit that recommends an appropriate sport based on the analysis results obtained by the analysis unit;
a providing unit that provides training plans and advice based on the sports events recommended by the recommending unit.
(Appendix 2)
The collecting unit
The system of claim 1, wherein data on height, weight, muscle mass, bone density, and flexibility is collected.
(Appendix 3)
The analysis unit
The system described in Appendix 1 is characterized by analyzing the success factors of each sporting event based on a past database and comparing them with the user's data.
(Appendix 4)
The recommendation unit
The system described in Appendix 1 is characterized by identifying a sports event that is suitable for the user's physical characteristics.
(Appendix 5)
The providing unit
2. The system of claim 1, further comprising: assessing the probability of success in a specified sport and providing specific training plans and advice.
(Appendix 6)
The collecting unit
2. The system of claim 1, further comprising: estimating a user's emotion; and adjusting timing of data collection based on the estimated user's emotion.
(Appendix 7)
The collecting unit
The system according to claim 1, further comprising: analyzing the user's past physical data and selecting the optimal collection method.
(Appendix 8)
The collecting unit
The system according to claim 1, wherein filtering is performed based on the user's current health condition and lifestyle habits when collecting data.
(Appendix 9)
The collecting unit
The system according to claim 1, further comprising: estimating a user's emotion; and determining a priority of data to be collected based on the estimated user's emotion.
(Appendix 10)
The collecting unit
The system according to claim 1, wherein when collecting data, highly relevant data is collected preferentially based on the user's geographical location information.
(Appendix 11)
The collecting unit
The system of claim 1, wherein the system analyzes the user's social media activity and collects relevant data when collecting data.
(Appendix 12)
The analysis unit
2. The system of claim 1, further comprising: estimating a user's emotion; and adjusting a method of expressing the analysis based on the estimated user's emotion.
(Appendix 13)
The analysis unit
The system of claim 1, wherein the level of detail of the analysis is adjusted based on the importance of the physical data during the analysis.
(Appendix 14)
The analysis unit
10. The system of claim 1, wherein during analysis, different analysis algorithms are applied depending on the category of physical data.
(Appendix 15)
The analysis unit
10. The system of claim 1, further comprising: estimating a user's emotion; and adjusting the length of the analysis based on the estimated user's emotion.
(Appendix 16)
The analysis unit
2. The system of claim 1, wherein during analysis, analysis priorities are determined based on when the data was collected.
(Appendix 17)
The analysis unit
2. The system of claim 1, wherein during analysis, the order of analysis is adjusted based on data relevance.
(Appendix 18)
The recommendation unit
2. The system of claim 1, further comprising: estimating a user's emotion; and adjusting recommendation criteria based on the estimated user's emotion.
(Appendix 19)
The recommendation unit
The system of claim 1, wherein the system takes into account correlations between physical data when making recommendations to improve the accuracy of the recommendations.
(Appendix 20)
The recommendation unit
The system according to claim 1, wherein the system makes recommendations taking into consideration user attribute information.
(Appendix 21)
The recommendation unit
The system of claim 1, further comprising: estimating a user's emotion; and adjusting an order in which the recommended results are displayed based on the estimated user's emotion.
(Appendix 22)
The recommendation unit
The system according to claim 1, wherein the recommendation is made taking into account the geographical distribution of users.
(Appendix 23)
The recommendation unit
The system of claim 1, wherein the system improves the accuracy of the recommendations based on related literature when making recommendations.
(Appendix 24)
The providing unit
2. The system according to claim 1, wherein the system estimates a user's emotions and adjusts a training plan and a method of providing advice based on the estimated user's emotions.
(Appendix 25)
The providing unit
The system according to claim 1, wherein the system provides an optimal plan by referring to the user's past training history.
(Appendix 26)
The providing unit
10. The system of claim 1, wherein the training plan is customized at the time of provision based on the user's current living situation.
(Appendix 27)
The providing unit
2. The system according to claim 1, wherein the system estimates a user's emotions and prioritizes training plans and advice based on the estimated user's emotions.
(Appendix 28)
The providing unit
The system according to claim 1, wherein the system provides an optimal plan by taking into consideration the user's geographical location information when providing the plan.
(Appendix 29)
The providing unit
The system of claim 1, wherein the system analyzes the user's social media activity at the time of provision to suggest training plans and advice.

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

Claims (5)

a collection unit that collects data on the user's physical characteristics and growth process;
an analysis unit that analyzes the data collected by the collection unit;
a recommendation unit that recommends an appropriate sport based on the analysis results obtained by the analysis unit;
a providing unit that provides training plans and advice based on the sports competition recommended by the recommending unit ,
The analysis unit estimates the user's emotion, and provides an analysis result when the estimated user's emotion is relaxed, provides a more concise analysis result than the analysis result provided when the estimated user's emotion is stressed, and adjusts a method of presenting the analysis so as to provide a visually appealing analysis result when the estimated user's emotion is excited.
A system characterized by: The collecting unit
The system according to claim 1, wherein data on height, weight, muscle mass, bone density, and flexibility are collected. The analysis unit
The system according to claim 1, characterized in that it analyzes success factors in each sporting event based on a past database and compares the results with the user's data. The recommendation unit
The system of claim 1, further comprising: identifying a sport suitable for a user's physical characteristics; The providing unit
10. The system of claim 1, further comprising: a computer program for evaluating the probability of success in a specified sport and providing specific training plans and advice.