System

Abstract

An object of a system according to an embodiment is to enable an elderly person to make a daily exercise plan and receive appropriate advice.SOLUTION: A system includes a voice input unit, an exercise plan generation unit, a data cooperation unit, an advice providing unit, and a question answering unit. The voice input unit receives a voice instruction from a user. The exercise plan generation unit analyzes the instruction received by the voice input unit and generates an appropriate exercise plan. The data linkage unit links the exercise result with another health management application based on the exercise plan generated by the exercise plan generation unit. The advice providing unit provides advice for achieving a target. The question answering unit generates a natural answer to a question by voice.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed 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 character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has made it difficult for elderly people to create daily exercise plans and receive appropriate advice.

[0005] The system according to the embodiment aims to enable elderly people to create daily exercise plans and receive appropriate advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice input unit, an exercise plan generation unit, a data linking unit, an advice providing unit, and a question answering unit. The voice input unit receives voice instructions from the user. The exercise plan generation unit analyzes the instructions received by the voice input unit and generates an appropriate exercise plan. The data linking unit links exercise results with other health management apps based on the exercise plan generated by the exercise plan generation unit. The advice providing unit provides advice for achieving goals. The question answering unit generates natural answers to questions asked by voice. [Effects of the Invention]

[0007] The system according to the embodiment allows elderly people to create daily exercise plans and receive appropriate advice. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] 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. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] 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. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] 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. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices 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).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a 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.

[0018] 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, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the 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).

[0019] 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, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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 the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process 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 process 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.

[0025] The 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.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] 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 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A coaching app according to an embodiment of the present invention is a system that uses AI to provide elderly people with advice on daily exercise. The system is designed for easy use by elderly people using voice input. It provides planned exercise advice based on individually set goals, taking into account information such as the weather and climate. Daily exercise results are automatically linked with other health management apps and data is accumulated. To achieve goals, appropriate advice and motivational information are presented in the form of text and images according to progress and status, and voice readout is also supported. Furthermore, for voice questions and inquiries, natural responses are generated in a conversational format using a chat function, encouraging continued use. This allows elderly people to plan their daily exercise and maintain their health.

[0029] A coaching app according to an embodiment includes a voice input unit, an exercise plan generation unit, a data linking unit, an advice providing unit, and a question-and-answering unit. The voice input unit accepts a user's voice instruction. For example, when the user issues a voice instruction such as "Tell me today's exercise plan," the voice input unit accepts the instruction. The voice input unit also converts the user's voice into text data using voice recognition technology. The exercise plan generation unit analyzes the instruction accepted by the voice input unit and generates an appropriate exercise plan. For example, the generation AI generates an exercise plan taking into account information such as the user's personal goals, the climate, and weather. The data linking unit links the exercise results based on the exercise plan generated by the exercise plan generation unit with other health management apps. For example, it links with a HELPO app or a healthcare app and automatically accumulates the exercise results. The advice providing unit provides advice toward achieving the goal. For example, the generation AI analyzes the user's progress and displays an encouraging message such as "You're almost there! Keep doing your best!". The question-and-answering unit generates natural answers to questions posed by voice. For example, if a user asks, "My knee hurts. What kind of exercise is good?" the AI ​​generator will respond with, "I recommend stretching exercises that don't put strain on the knee." This allows the coaching app to help elderly people plan their daily exercise routines and maintain their health. For example, the app can be easily operated via voice input, and exercise advice based on individual goals enables effective exercise. Furthermore, automatic linking and accumulation of exercise results enables long-term health management, and advice on achieving goals and information to boost motivation encourage continued exercise. Furthermore, responses to questions and consultations via voice enable users to continue exercising with peace of mind.

[0030] The voice input unit can analyze the tone and speed of the user's voice to estimate the user's physical condition and mood and reflect this in the exercise plan generation unit. For example, when a user inputs voice, the voice input unit analyzes the tone and speed of the voice to estimate the user's physical condition and mood. For example, if the user's voice is low and slow, it can determine that the user is feeling tired or stressed and suggest light exercise. This makes it possible to provide an exercise plan that matches the user's physical condition and mood.

[0031] The voice input unit can learn the user's past voice data and generate an individually optimized voice recognition model. The voice input unit, for example, collects the user's past voice data and generates an individually optimized voice recognition model. For example, the voice input unit learns the user's pronunciation and accent and improves the accuracy of voice input. This can improve the accuracy of voice input.

[0032] The voice input unit can combine voice input with gesture recognition to allow the user to operate with hand or body movements. For example, the voice input unit can combine voice input with gesture recognition to build a system that allows the user to operate with hand or body movements. For example, switching menus by waving your hand. This enables operations that combine voice input and gesture recognition.

[0033] The voice input unit can analyze background sounds and environmental sounds when voice is input, and suggest an environment that is easy for the user to exercise. For example, the voice input unit can analyze background sounds and environmental sounds when voice is input, and build a system that suggests an environment that is easy for the user to exercise. For example, it can recommend exercising in a quiet place. This makes it possible to suggest an environment that is easy for the user to exercise.

[0034] The exercise plan generation unit can analyze the user's past exercise data and generate an individually optimized exercise plan. For example, the exercise plan generation unit builds a system that collects the user's past exercise data and generates an individually optimized exercise plan. For example, it suggests appropriate exercise intensity and type based on the user's past exercise history. This makes it possible to provide an optimal exercise plan based on the user's past exercise data.

[0035] The exercise plan generation unit can provide exercise advice that minimizes risks by taking into account the user's health condition and medical history. The exercise plan generation unit, for example, collects the user's health condition and medical history and builds a system that provides exercise advice that minimizes risks. For example, it suggests light exercise to a user who is at risk of heart disease. This makes it possible to provide exercise advice that minimizes risks based on the user's health condition and medical history.

[0036] The exercise plan generation unit can automatically generate exercise plans according to different seasons and climatic conditions and propose them to the user. The exercise plan generation unit, for example, builds a system that automatically generates exercise plans according to different seasons and climatic conditions. For example, in summer, the exercise plan generation unit suggests exercising during cooler times of the day. This makes it possible to provide exercise plans according to the seasons and climatic conditions.

[0037] The exercise plan generation unit can provide advice that balances exercise and diet, taking into account the user's diet and nutritional status. The exercise plan generation unit, for example, collects information on the user's diet and nutritional status and builds a system that provides advice that balances exercise and diet. For example, it adjusts the intensity of exercise depending on the content of the meal. This makes it possible to provide advice that balances exercise and diet.

[0038] The data linking unit can analyze exercise results and provide a dashboard that visualizes improvements in the user's exercise performance. The data linking unit, for example, builds a system that analyzes the user's exercise results and provides a dashboard that visualizes improvements in exercise performance. For example, it displays changes in exercise frequency and intensity in a graph. This makes it possible to visualize improvements in the user's exercise performance.

[0039] The data linking unit can analyze the exercise data, predict the user's health risks, and suggest preventive measures. The data linking unit, for example, analyzes the user's exercise data and builds a system that predicts health risks. For example, it detects risks due to lack of exercise and suggests preventive measures. This makes it possible to predict the user's health risks and suggest preventive measures.

[0040] The data linking unit can link with other health management apps to provide a comprehensive health management plan. The data linking unit, for example, links with other health management apps to build a system that provides a comprehensive health management plan. For example, it links with a diet management app to balance exercise and diet. This makes it possible to link with other health management apps to provide a comprehensive health management plan.

[0041] The data linking unit can add a function to share exercise results with family and friends and obtain social support. The data linking unit, for example, builds a system that adds a function to share exercise results with family and friends and obtain social support. For example, the exercise results can be shared on social media. This allows exercise results to be shared with family and friends and obtain social support.

[0042] The advice providing unit can analyze the user's progress and set achievable short-term goals to maintain motivation. The advice providing unit, for example, builds a system that analyzes the user's progress and sets achievable short-term goals. For example, it sets weekly goals and visualizes the degree of achievement. This allows the user to set short-term goals based on their progress and maintain motivation.

[0043] The advice providing unit can provide feedback that emphasizes successful experiences based on the user's exercise history. The advice providing unit, for example, analyzes the user's exercise history and builds a system that provides feedback that emphasizes successful experiences. For example, the advice providing unit looks back on past achievement goals and emphasizes successful experiences. This makes it possible to provide feedback that emphasizes successful experiences based on the user's exercise history.

[0044] The advice providing unit can incorporate gamification elements to provide rewards and badges according to the level of exercise achievement. The advice providing unit, for example, builds a system that incorporates gamification elements to provide rewards and badges according to the level of exercise achievement. For example, a badge is earned each time a goal is achieved. This makes it possible to provide rewards and badges according to the level of exercise achievement.

[0045] The advice providing unit can add a social function that encourages competition and cooperation between users. The advice providing unit, for example, builds a system that adds a social function that encourages competition and cooperation between users. For example, users can share their exercise progress with friends and compete against each other. This allows the addition of a social function that encourages competition and cooperation between users.

[0046] The question answering unit can analyze the content of the user's question and provide the optimal answer based on the past question history. The question answering unit, for example, builds a system that analyzes the content of the user's question and provides the optimal answer based on the past question history. For example, if a similar question has been asked in the past, the answer to that question can be used as a reference. This makes it possible to provide the optimal answer based on the content of the user's question.

[0047] The question answering unit can generate an answer that incorporates expert advice according to the content of the question. The question answering unit, for example, builds a system that generates an answer that incorporates expert advice according to the content of the question. For example, a question about medical care can be answered by incorporating the advice of a doctor. This makes it possible to provide an answer that incorporates expert advice according to the content of the question.

[0048] The question answering unit can analyze the content of the question and automatically suggest related videos and tutorials. The question answering unit, for example, builds a system that analyzes the content of the question and automatically suggests related videos and tutorials. For example, for a question about exercise methods, related videos are suggested. This makes it possible to automatically suggest related videos and tutorials based on the content of the question.

[0049] The question answering unit can add a function to share the experiences and feedback of other users depending on the content of the question. The question answering unit builds a system that adds a function to share the experiences and feedback of other users depending on the content of the question. For example, advice from users who have experienced the same problem is provided. This allows the experiences and feedback of other users to be shared depending on the content of the question.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] Coaching apps can provide customized training plans to improve athletic performance based on a user's exercise history. For example, they can analyze past exercise data and suggest exercises to strengthen specific muscle groups. They can also adjust the frequency and intensity of exercise based on the user's exercise history to provide an optimal training plan. Furthermore, they can maintain motivation by reviewing past goals and providing feedback highlighting successes.

[0052] Coaching apps can provide exercise advice that minimizes risk by taking into account a user's health condition and medical history. For example, they can suggest light exercise for a user at risk of heart disease, or exercises that put less strain on the joints for a user with joint problems. Furthermore, they can adjust the frequency and intensity of exercise based on the user's health condition and provide advice on how to exercise safely.

[0053] In addition to voice input, coaching apps can combine gesture recognition to allow users to operate the app with hand and body movements. For example, a user can switch between menus by waving their hand. A specific gesture can also be used to start or stop an exercise. Furthermore, gesture recognition can be used to check the user's exercise form and provide feedback to ensure correct form.

[0054] Coaching apps can provide advice on balancing exercise and diet, taking into account the user's diet and nutritional status. For example, they can adjust the intensity of exercise depending on the contents of the diet. They can also provide dietary advice to supplement specific nutrients if a user is lacking in certain nutrients. Furthermore, they can provide a customized plan for balancing exercise and diet based on the user's diet history.

[0055] Coaching apps can add a feature that allows users to share their exercise results with family and friends and receive social support. For example, exercise results can be shared on social media. Users can also share their exercise progress with family and friends and encourage each other. Furthermore, adding social features that encourage competition and cooperation between users can increase motivation to exercise.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The voice input unit accepts the user's voice instruction. For example, if the user issues a voice instruction such as "Tell me today's exercise plan," the voice input unit accepts the instruction. The voice input unit also converts the user's voice into text data using voice recognition technology. Step 2: The exercise plan generation unit analyzes the instructions received by the voice input unit and generates an appropriate exercise plan. For example, the generation AI generates an exercise plan taking into account information such as the user's personal goals, climate, and weather. Step 3: The data linking unit links the exercise results with other health management apps based on the exercise plan generated by the exercise plan generation unit. For example, it links with the HELPO app or healthcare app and automatically stores the exercise results. Step 4: The advice provider provides advice for achieving the goal. For example, the generation AI analyzes the user's progress and displays an encouraging message such as, "You're almost there! Keep trying!" Step 5: The question-answering unit generates natural answers to voice questions. For example, if a user asks, "My knee hurts. What kind of exercise is good?", the AI ​​generator will respond with, "I recommend stretching exercises that don't put strain on your knees."

[0058] (Example 2) A coaching app according to an embodiment of the present invention is a system that uses AI to provide elderly people with advice on daily exercise. The system is designed for easy use by elderly people using voice input. It provides planned exercise advice based on individually set goals, taking into account information such as the weather and climate. Daily exercise results are automatically linked with other health management apps and data is accumulated. To achieve goals, appropriate advice and motivational information are presented in the form of text and images according to progress and status, and voice readout is also supported. Furthermore, for voice questions and inquiries, natural responses are generated in a conversational format using a chat function, encouraging continued use. This allows elderly people to plan their daily exercise and maintain their health.

[0059] A coaching app according to an embodiment includes a voice input unit, an exercise plan generation unit, a data linking unit, an advice providing unit, and a question-and-answering unit. The voice input unit accepts a user's voice instruction. For example, when the user issues a voice instruction such as "Tell me today's exercise plan," the voice input unit accepts the instruction. The voice input unit also converts the user's voice into text data using voice recognition technology. The exercise plan generation unit analyzes the instruction accepted by the voice input unit and generates an appropriate exercise plan. For example, the generation AI generates an exercise plan taking into account information such as the user's personal goals, the climate, and weather. The data linking unit links the exercise results based on the exercise plan generated by the exercise plan generation unit with other health management apps. For example, it links with a HELPO app or a healthcare app and automatically accumulates the exercise results. The advice providing unit provides advice toward achieving the goal. For example, the generation AI analyzes the user's progress and displays an encouraging message such as "You're almost there! Keep doing your best!". The question-and-answering unit generates natural answers to questions posed by voice. For example, if a user asks, "My knee hurts. What kind of exercise is good?" the AI ​​generator will respond with, "I recommend stretching exercises that don't put strain on the knee." This allows the coaching app to help elderly people plan their daily exercise routines and maintain their health. For example, the app can be easily operated via voice input, and exercise advice based on individual goals enables effective exercise. Furthermore, automatic linking and accumulation of exercise results enables long-term health management, and advice on achieving goals and information to boost motivation encourage continued exercise. Furthermore, responses to questions and consultations via voice enable users to continue exercising with peace of mind.

[0060] The voice input unit can analyze the tone and speed of the user's voice to estimate the user's physical condition and mood and reflect this in the exercise plan generation unit. For example, when a user inputs voice, the voice input unit analyzes the tone and speed of the voice to estimate the user's physical condition and mood. For example, if the user's voice is low and slow, it can determine that the user is feeling tired or stressed and suggest light exercise. This makes it possible to provide an exercise plan that matches the user's physical condition and mood.

[0061] The voice input unit can learn the user's past voice data and generate an individually optimized voice recognition model. The voice input unit, for example, collects the user's past voice data and generates an individually optimized voice recognition model. For example, the voice input unit learns the user's pronunciation and accent and improves the accuracy of voice input. This can improve the accuracy of voice input.

[0062] The voice input unit can estimate emotions from the user's voice and propose an exercise plan that elicits positive emotions. For example, if the user is feeling depressed, the voice input unit can propose yoga, which will help them relax. This makes it possible to provide an exercise plan that matches the user's emotions.

[0063] The voice input unit can combine voice input with gesture recognition to allow the user to operate with hand or body movements. For example, the voice input unit can combine voice input with gesture recognition to build a system that allows the user to operate with hand or body movements. For example, switching menus by waving your hand. This enables operations that combine voice input and gesture recognition.

[0064] The voice input unit can analyze background sounds and environmental sounds when voice is input, and suggest an environment that is easy for the user to exercise. For example, the voice input unit can analyze background sounds and environmental sounds when voice is input, and build a system that suggests an environment that is easy for the user to exercise. For example, it can recommend exercising in a quiet place. This makes it possible to suggest an environment that is easy for the user to exercise.

[0065] The voice input unit can use the emotion estimation function to analyze the emotion of the user when making voice input in real time and provide appropriate feedback. The voice input unit, for example, uses the emotion estimation function to build a system that analyzes the emotion of the user when making voice input in real time and provides appropriate feedback. For example, if the user is feeling anxious, an encouraging message is displayed. This makes it possible to provide feedback according to the user's emotion.

[0066] The exercise plan generation unit can analyze the user's past exercise data and generate an individually optimized exercise plan. For example, the exercise plan generation unit builds a system that collects the user's past exercise data and generates an individually optimized exercise plan. For example, it suggests appropriate exercise intensity and type based on the user's past exercise history. This makes it possible to provide an optimal exercise plan based on the user's past exercise data.

[0067] The exercise plan generation unit can provide exercise advice that minimizes risks by taking into account the user's health condition and medical history. The exercise plan generation unit, for example, collects the user's health condition and medical history and builds a system that provides exercise advice that minimizes risks. For example, it suggests light exercise to a user who is at risk of heart disease. This makes it possible to provide exercise advice that minimizes risks based on the user's health condition and medical history.

[0068] The exercise plan generation unit uses the emotion estimation function to provide exercise advice according to the user's emotional state, thereby maintaining motivation. The exercise plan generation unit, for example, uses the emotion estimation function to build a system that provides exercise advice according to the user's emotional state. For example, if the user is feeling depressed, the exercise plan generation unit suggests exercises that will help them relax. This allows the exercise advice to be provided according to the user's emotional state, thereby maintaining motivation.

[0069] The exercise plan generation unit can automatically generate exercise plans according to different seasons and climatic conditions and propose them to the user. The exercise plan generation unit, for example, builds a system that automatically generates exercise plans according to different seasons and climatic conditions. For example, in summer, the exercise plan generation unit suggests exercising during cooler times of the day. This makes it possible to provide exercise plans according to the seasons and climatic conditions.

[0070] The exercise plan generation unit can provide advice that balances exercise and diet, taking into account the user's diet and nutritional status. The exercise plan generation unit, for example, collects information on the user's diet and nutritional status and builds a system that provides advice that balances exercise and diet. For example, it adjusts the intensity of exercise depending on the content of the meal. This makes it possible to provide advice that balances exercise and diet.

[0071] The exercise plan generation unit uses the emotion estimation function to provide exercise advice based on the user's emotions and elicit positive emotions. The exercise plan generation unit, for example, uses the emotion estimation function to build a system that provides exercise advice based on the user's emotions. For example, if the user is feeling depressed, the exercise plan generation unit suggests exercises that will help them relax. This allows the exercise advice to be provided based on the user's emotions and elicit positive emotions.

[0072] The data linking unit can analyze exercise results and provide a dashboard that visualizes improvements in the user's exercise performance. The data linking unit, for example, builds a system that analyzes the user's exercise results and provides a dashboard that visualizes improvements in exercise performance. For example, it displays changes in exercise frequency and intensity in a graph. This makes it possible to visualize improvements in the user's exercise performance.

[0073] The data linking unit can analyze the exercise data, predict the user's health risks, and suggest preventive measures. The data linking unit, for example, analyzes the user's exercise data and builds a system that predicts health risks. For example, it detects risks due to lack of exercise and suggests preventive measures. This makes it possible to predict the user's health risks and suggest preventive measures.

[0074] The data linking unit can use the emotion estimation function to analyze the user's emotional response to the exercise results and provide positive feedback. The data linking unit, for example, uses the emotion estimation function to analyze the user's emotional response to the exercise results and build a system that provides positive feedback. For example, an encouraging message is displayed in response to the exercise results. This makes it possible to analyze the user's emotional response to the exercise results and provide positive feedback.

[0075] The data linking unit can link with other health management apps to provide a comprehensive health management plan. The data linking unit, for example, links with other health management apps to build a system that provides a comprehensive health management plan. For example, it links with a diet management app to balance exercise and diet. This makes it possible to link with other health management apps to provide a comprehensive health management plan.

[0076] The data linking unit can add a function to share exercise results with family and friends and obtain social support. The data linking unit, for example, builds a system that adds a function to share exercise results with family and friends and obtain social support. For example, the exercise results can be shared on social media. This allows exercise results to be shared with family and friends and obtain social support.

[0077] The data linking unit can use the emotion estimation function to analyze the user's emotions regarding the exercise results and provide advice to maintain motivation. The data linking unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotions regarding the exercise results and provides advice to maintain motivation. For example, an encouraging message is displayed in response to the exercise results. This makes it possible to analyze the user's emotions regarding the exercise results and provide advice to maintain motivation.

[0078] The advice providing unit can analyze the user's progress and set achievable short-term goals to maintain motivation. The advice providing unit, for example, builds a system that analyzes the user's progress and sets achievable short-term goals. For example, it sets weekly goals and visualizes the degree of achievement. This allows the user to set short-term goals based on their progress and maintain motivation.

[0079] The advice providing unit can provide feedback that emphasizes successful experiences based on the user's exercise history. The advice providing unit, for example, analyzes the user's exercise history and builds a system that provides feedback that emphasizes successful experiences. For example, the advice providing unit looks back on past achievement goals and emphasizes successful experiences. This makes it possible to provide feedback that emphasizes successful experiences based on the user's exercise history.

[0080] The advice providing unit can provide an encouraging message according to the user's emotional state using the emotion estimation function. The advice providing unit, for example, uses the emotion estimation function to build a system that provides an encouraging message according to the user's emotional state. For example, if the user is feeling down, an encouraging message is displayed. This makes it possible to provide an encouraging message according to the user's emotional state.

[0081] The advice providing unit can incorporate gamification elements to provide rewards and badges according to the level of exercise achievement. The advice providing unit, for example, builds a system that incorporates gamification elements to provide rewards and badges according to the level of exercise achievement. For example, a badge is earned each time a goal is achieved. This makes it possible to provide rewards and badges according to the level of exercise achievement.

[0082] The advice providing unit can add a social function that encourages competition and cooperation between users. The advice providing unit, for example, builds a system that adds a social function that encourages competition and cooperation between users. For example, users can share their exercise progress with friends and compete against each other. This allows the addition of a social function that encourages competition and cooperation between users.

[0083] The advice providing unit can use the emotion estimation function to provide information to increase motivation based on the user's emotion. The advice providing unit, for example, uses the emotion estimation function to build a system that provides information to increase motivation based on the user's emotion. For example, if the user is feeling down, an encouraging message is displayed. This makes it possible to provide information to increase motivation based on the user's emotion.

[0084] The question answering unit can analyze the content of the user's question and provide the optimal answer based on the past question history. The question answering unit, for example, builds a system that analyzes the content of the user's question and provides the optimal answer based on the past question history. For example, if a similar question has been asked in the past, the answer to that question can be used as a reference. This makes it possible to provide the optimal answer based on the content of the user's question.

[0085] The question answering unit can generate an answer that incorporates expert advice according to the content of the question. The question answering unit, for example, builds a system that generates an answer that incorporates expert advice according to the content of the question. For example, a question about medical care can be answered by incorporating the advice of a doctor. This makes it possible to provide an answer that incorporates expert advice according to the content of the question.

[0086] The question answering unit uses the emotion estimation function to provide an answer that corresponds to the emotional state of the user, thereby giving the user a sense of security. The question answering unit, for example, uses the emotion estimation function to build a system that provides an answer that corresponds to the emotional state of the user. For example, if the user is feeling anxious, an answer that gives the user a sense of security is provided. This makes it possible to provide an answer that corresponds to the emotional state of the user, thereby giving the user a sense of security.

[0087] The question answering unit can analyze the content of the question and automatically suggest related videos and tutorials. The question answering unit, for example, builds a system that analyzes the content of the question and automatically suggests related videos and tutorials. For example, for a question about exercise methods, related videos are suggested. This makes it possible to automatically suggest related videos and tutorials based on the content of the question.

[0088] The question answering unit can add a function to share the experiences and feedback of other users depending on the content of the question. The question answering unit builds a system that adds a function to share the experiences and feedback of other users depending on the content of the question. For example, advice from users who have experienced the same problem is provided. This allows the experiences and feedback of other users to be shared depending on the content of the question.

[0089] The question answering unit uses the emotion estimation function to provide answers based on the user's emotions and elicit positive emotions. The question answering unit, for example, uses the emotion estimation function to build a system that provides answers based on the user's emotions. For example, if the user is feeling down, an encouraging message is provided. This allows the system to provide answers based on the user's emotions and elicit positive emotions.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] Coaching apps can provide customized training plans to improve athletic performance based on a user's exercise history. For example, they can analyze past exercise data and suggest exercises to strengthen specific muscle groups. They can also adjust the frequency and intensity of exercise based on the user's exercise history to provide an optimal training plan. Furthermore, they can maintain motivation by reviewing past goals and providing feedback highlighting successes.

[0092] Coaching apps can estimate a user's emotions and provide exercise advice based on their emotions. For example, if a user is feeling stressed, they can suggest relaxing yoga or stretching exercises. If a user is feeling positive, they can suggest energetic exercises. Furthermore, they can encourage exercise by providing encouraging messages and motivational information according to the user's emotions.

[0093] Coaching apps can provide exercise advice that minimizes risk by taking into account a user's health condition and medical history. For example, they can suggest light exercise for a user at risk of heart disease, or exercises that put less strain on the joints for a user with joint problems. Furthermore, they can adjust the frequency and intensity of exercise based on the user's health condition and provide advice on how to exercise safely.

[0094] Coaching apps can estimate a user's emotions and provide feedback based on their emotions. For example, if a user is feeling down, they can display an encouraging message. If a user is feeling anxious, they can provide a reassuring response. Furthermore, they can support the user's emotions by providing advice and information to elicit positive emotions according to the user's emotions.

[0095] In addition to voice input, coaching apps can combine gesture recognition to allow users to operate the app with hand and body movements. For example, a user can switch between menus by waving their hand. A specific gesture can also be used to start or stop an exercise. Furthermore, gesture recognition can be used to check the user's exercise form and provide feedback to ensure correct form.

[0096] Coaching apps can estimate a user's emotions and provide motivational information based on their emotions. For example, if a user is feeling down, they can display encouraging messages. If a user has positive emotions, they can provide messages encouraging them to try harder. Furthermore, they can maintain motivation by providing rewards and badges according to the user's level of exercise achievement, depending on their emotions.

[0097] Coaching apps can provide advice on balancing exercise and diet, taking into account the user's diet and nutritional status. For example, they can adjust the intensity of exercise depending on the contents of the diet. They can also provide dietary advice to supplement specific nutrients if a user is lacking in certain nutrients. Furthermore, they can provide a customized plan for balancing exercise and diet based on the user's diet history.

[0098] Coaching apps can estimate a user's emotions and provide exercise advice based on their emotions. For example, if a user is feeling stressed, they can suggest relaxing yoga or stretching exercises. If a user is feeling positive, they can suggest energetic exercises. Furthermore, they can encourage exercise by providing encouraging messages and motivational information according to the user's emotions.

[0099] Coaching apps can add a feature that allows users to share their exercise results with family and friends and receive social support. For example, exercise results can be shared on social media. Users can also share their exercise progress with family and friends and encourage each other. Furthermore, adding social features that encourage competition and cooperation between users can increase motivation to exercise.

[0100] Coaching apps can estimate a user's emotions and provide feedback based on their emotions. For example, if a user is feeling down, they can display an encouraging message. If a user is feeling anxious, they can provide a reassuring response. Furthermore, they can support the user's emotions by providing advice and information to elicit positive emotions according to the user's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The voice input unit accepts the user's voice instruction. For example, if the user issues a voice instruction such as "Tell me today's exercise plan," the voice input unit accepts the instruction. The voice input unit also converts the user's voice into text data using voice recognition technology. Step 2: The exercise plan generation unit analyzes the instructions received by the voice input unit and generates an appropriate exercise plan. For example, the generation AI generates an exercise plan taking into account information such as the user's personal goals, climate, and weather. Step 3: The data linking unit links the exercise results with other health management apps based on the exercise plan generated by the exercise plan generation unit. For example, it links with the HELPO app or healthcare app and automatically stores the exercise results. Step 4: The advice provider provides advice for achieving the goal. For example, the generation AI analyzes the user's progress and displays an encouraging message such as, "You're almost there! Keep trying!" Step 5: The question-answering unit generates natural answers to voice questions. For example, if a user asks, "My knee hurts. What kind of exercise is good?", the AI ​​generator will respond with, "I recommend stretching exercises that don't put strain on your knees."

[0103] The specific processing unit 290 transmits the result 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 result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including 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 is also input to the data generation model 58. 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 a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the 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.

[0109] 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, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the 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.

[0110] The microphone 238 receives instructions and the like 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 according to instructions from the processor 46.

[0111] 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).

[0112] The communication I / F 44 is connected to a 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 state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] 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.

[0115] The 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.

[0116] In the smart glasses 214, the 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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 a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text data. 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 so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, 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. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the 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.

[0124] The headset type 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, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the 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.

[0125] The microphone 238 receives instructions and the like 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 according to instructions from the processor 46.

[0126] 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).

[0127] The communication I / F 44 is connected to a 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 state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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.

[0130] The 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.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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 a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text data. 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 so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type 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 type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a 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.

[0138] 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, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the 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.

[0139] 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, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the 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.

[0140] The microphone 238 receives instructions and the like 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 according to instructions from the processor 46.

[0141] 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 a CCD 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).

[0142] The communication I / F 44 is connected to a 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 state.

[0143] 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.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] 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.

[0146] The 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.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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 a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice 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 voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text data. 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 so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by 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.

[0152] The emotion identification model 59 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 FIG. 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.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the 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 behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and 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, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] 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 you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] 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 located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, 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.

[0161] 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.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description 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 description 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 replacements may be made to the above-described description and illustrations within the scope of the gist 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 description 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.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a voice input unit that accepts voice instructions from a user; an exercise plan generation unit that analyzes the instruction received by the voice input unit and generates an appropriate exercise plan; a data linking unit that links the exercise results with other health management applications based on the exercise plan generated by the exercise plan generating unit; An advice department that provides advice to help achieve goals; a question answering unit that generates natural answers to questions asked by voice A system characterized by:

2. The voice input unit Learning from the user's past voice data to generate an individually optimized voice recognition model 2. The system of claim 1.

3. The exercise plan generation unit Analyzing the user's past exercise data and generating an individually optimized exercise plan 2. The system of claim 1.

4. The data linking unit Analyzing the exercise results and providing a dashboard that visualizes the improvement in the exercise performance of the user.

2. The system of claim 1.

5. The advice providing unit Analyze the user's progress and set achievable short-term goals to keep them motivated 2. The system of claim 1.

6. The question answering unit Providing the answer according to the emotional state of the user to give a sense of security 2. The system of claim 1.

7. The voice input unit Estimating emotions from the user's voice and proposing the exercise plan that elicits positive emotions 2. The system of claim 1.

8. The exercise plan generation unit Providing exercise advice according to the emotional state of the user to maintain motivation 2. The system of claim 1.

Images