System

Abstract

An object of the system according to the embodiment is to integrally provide a training menu, feedback of a form, and a suggestion of a meal menu which are personalized on the basis of user information.SOLUTION: A system according to an embodiment includes a training menu providing unit, a training form feedback unit, and a meal menu suggestion unit. The training menu providing unit provides a personalized training menu based on the user information. The training form feedback unit analyzes a training video of a user who performs training based on the training menu provided by the training menu providing unit, and provides feedback of a correct form. The meal menu suggestion unit analyzes a meal photograph taken by the user and suggests an improved meal menu effective for training.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] With conventional technology, training menus, form feedback, and meal menu suggestions were provided individually, resulting in a lack of integrated support.

[0005] The system according to the embodiment aims to provide a personalized training menu, form feedback, and meal menu suggestions in an integrated manner based on user information. [Means for solving the problem]

[0006] The system according to the embodiment includes a training menu providing unit, a training form feedback unit, and a meal menu suggestion unit. The training menu providing unit provides a personalized training menu based on user information. The training form feedback unit analyzes a training video of a user who is training based on the training menu provided by the training menu providing unit and provides feedback on correct form. The meal menu suggestion unit analyzes meal photos taken by the user and suggests an improved meal menu that is effective for training. [Effects of the Invention]

[0007] The system according to the embodiment can provide a personalized training menu, form feedback, and meal menu suggestions in an integrated manner based on user information. [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 a 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 emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 training support system according to an embodiment of the present invention provides a personalized training menu based on user information, analyzes training videos to provide feedback on correct form, and analyzes meal photos to suggest an improved meal menu. As a result, the training support system can maximize the effectiveness of training by providing a training menu tailored to the individual needs of the user, feedback on correct form, and an effective meal menu.

[0029] A training support system according to an embodiment includes a training menu providing unit, a training form feedback unit, and a meal menu suggestion unit. The training menu providing unit provides a personalized training menu based on user information. For example, when a user inputs information such as their age, gender, weight, and exercise experience, the generation AI analyzes this data and generates an effective training menu tailored to their individual needs and abilities. The generation AI receives input from the user in the form of prompts containing instructions on what the user wants the generation AI to do, and the generation AI generates a training menu based on the prompts. The training form feedback unit analyzes a training video of a user training based on the training menu provided by the training menu providing unit and provides feedback on correct form. For example, when a user uploads a video of squats, the generation AI analyzes the video and points out areas for improvement in form. The generation AI receives input from a training video filmed by the user, and the generation AI generates feedback based on the video. The meal menu suggestion unit analyzes photos of meals taken by the user and suggests improved meal menus that are effective for training. For example, when a user uploads photos of their meals, the generation AI analyzes the photos, evaluates their nutritional balance and calories, and suggests areas for improvement. The input to the generation AI is photos of meals taken by the user, and the generation AI suggests meal menus based on those photos. This allows the training support system to maximize the effectiveness of training by providing training menus tailored to the individual needs of the user, feedback on correct form, and effective meal menus.

[0030] The training menu providing unit can analyze the user's past training data and adjust the menu in real time according to the user's progress. The training menu providing unit, for example, collects data on the user's past training and adjusts the current training menu based on that data. For example, it analyzes past training records and changes the load and number of repetitions according to the user's progress. The training menu providing unit also monitors the user's training history in real time and dynamically adjusts the training menu according to the user's progress. For example, if the user achieves a goal with a particular exercise, it sets a new goal. The training menu providing unit also analyzes past training data to identify the user's strengths and weaknesses and customizes the training menu based on that. For example, if a particular muscle group is weak, it adds exercises to strengthen that area. This allows the training menu to be dynamically adjusted according to the user's progress, enabling effective training.

[0031] The training menu providing unit can collect lifestyle habit data of the user and optimize the training menu based on that. The training menu providing unit, for example, collects the user's sleep time and stress level and adjusts the training menu based on that. For example, it suggests light exercises when the user is sleep deprived. The training menu providing unit also analyzes the lifestyle habit data and provides a training menu tailored to the user's physical condition. For example, it adds exercises that have a relaxing effect when stress is high. The training menu providing unit also customizes the training menu based on the user's lifestyle habit data. For example, it suggests a menu that emphasizes recovery when the user sleeps little. This allows the training menu to be optimized according to the user's lifestyle, enabling effective training.

[0032] The training menu providing unit can generate menus for rehabilitation and fitness in addition to training menus. For example, the training menu providing unit adds rehabilitation exercises to the training menu taking into account the user's health condition and rehabilitation needs. For example, it suggests exercises that support recovery from an injury. The training menu providing unit also incorporates fitness exercises into the training menu for users who are fitness-oriented. For example, it adds aerobic exercises to improve cardiopulmonary function. The training menu providing unit also generates menus for rehabilitation and fitness and customizes them to suit the user's needs. For example, it suggests exercises to strengthen specific muscle groups. This makes it possible to meet the needs of a wide range of users by providing menus for rehabilitation and fitness.

[0033] The training menu providing unit can incorporate game elements into the training menu based on the user's hobbies and interests. The training menu providing unit incorporates game elements into the training menu, for example, taking into account the user's hobbies and interests. For example, it introduces a point system or a level-up system. The training menu providing unit also incorporates game elements into the training menu to enable the user to enjoy training. For example, it proposes virtual challenges and competitions. The training menu providing unit also adds game elements to the training menu based on the user's interests. For example, it introduces a system that allows the user to receive rewards by completing specific exercises. In this way, incorporating game elements into the training menu based on the user's hobbies and interests increases the enjoyment of training.

[0034] The training form feedback unit can provide detailed feedback by analyzing muscle movement and load, in addition to providing feedback on the training form. The training form feedback unit, for example, analyzes the user's training form and evaluates muscle movement and load. For example, it checks whether an appropriate load is being applied to a specific muscle group. In addition to providing feedback on the training form, the training form feedback unit also analyzes muscle movement and load in detail. For example, it evaluates muscle contraction and extension during exercise. In addition, the training form feedback unit analyzes muscle movement and load, and provides detailed feedback to the user. For example, it gives advice on improving how the muscles are used in a specific exercise. In this way, more detailed feedback can be provided by analyzing muscle movement and load.

[0035] The training form feedback unit can add advice to the training form feedback that takes into account the user's physical condition and fatigue level. The training form feedback unit, for example, takes into account the user's physical condition and fatigue level and adds advice to the training form feedback. For example, if fatigue is accumulating, it suggests resting. The training form feedback unit also provides advice to the training form feedback that reflects the user's physical condition and fatigue level. For example, it suggests lighter exercises when the user is not feeling well. The training form feedback unit also monitors the user's physical condition and fatigue level and customizes the training form feedback based on that. For example, if fatigue is accumulating, it suggests modifying the user's form. In this way, more effective training is possible by providing advice that takes into account the user's physical condition and fatigue level.

[0036] The training form feedback unit can provide stretching and warm-up advice in addition to training form feedback. For example, the training form feedback unit provides stretching and warm-up advice in addition to training form feedback. For example, it suggests stretching methods before and after training. The training form feedback unit also evaluates the user's training form and adds appropriate stretching and warm-up advice. For example, it suggests a warm-up method suitable for a specific exercise. The training form feedback unit also incorporates stretching and warm-up advice into the training form feedback. For example, it suggests stretches that should be performed before training. In this way, providing stretching and warm-up advice improves the effectiveness of training.

[0037] The training form feedback unit can provide audio or visual feedback in real time, allowing the user to make instant improvements. For example, the training form feedback unit may build a system that provides audio or visual feedback on training form in real time. For example, the system may enable the user to receive instant feedback while exercising. The training form feedback unit may also provide audio or visual feedback in real time, allowing the user to instantly improve their form. For example, audio guidance may be used to point out form corrections during exercise. The training form feedback unit may also develop a system that provides real-time feedback, allowing the user to instantly correct their form while exercising. For example, visual feedback may be used to show the correct form. This allows the user to instantly improve their form by providing real-time feedback.

[0038] The meal menu suggestion unit can analyze the user's dietary history and suggest a meal menu that takes into consideration long-term nutritional balance. The meal menu suggestion unit, for example, collects the user's dietary history and suggests a meal menu that takes into consideration long-term nutritional balance. For example, it identifies nutrient deficiencies based on past dietary data and suggests a menu that compensates for them. The meal menu suggestion unit also analyzes the dietary history and builds a system that evaluates the user's nutritional balance. For example, if a specific vitamin or mineral is lacking, it suggests ingredients that contain that nutrient. The meal menu suggestion unit also customizes a meal menu that takes into consideration long-term nutritional balance based on the user's dietary history. For example, it analyzes past dietary data and suggests a balanced meal menu. In this way, the user's health is supported by suggesting a meal menu that takes into consideration long-term nutritional balance.

[0039] The meal menu suggestion unit is capable of linking with the user's training menu to suggest a meal menu that promotes recovery after training. The meal menu suggestion unit, for example, links with the user's training menu to suggest a meal menu that promotes recovery after training. For example, it suggests high-protein meals that help muscle recovery. The meal menu suggestion unit also customizes a meal menu to promote recovery based on the training menu. For example, it suggests ingredients that contain nutrients needed after training. The meal menu suggestion unit also links with the user's training menu to build a system that provides a meal menu that promotes recovery. For example, it suggests meals that contain nutrients that should be taken after training. This maximizes the effectiveness of training by suggesting a meal menu that promotes recovery after training.

[0040] The meal menu suggestion unit can provide a shopping list of ingredients and recipes in addition to suggesting meal menus. For example, the meal menu suggestion unit provides a shopping list of necessary ingredients in addition to suggesting meal menus. For example, it generates a list of ingredients to be purchased at a supermarket based on the proposed menu. The meal menu suggestion unit also provides recipes corresponding to the proposed meal menus to enable the user to easily cook. For example, it provides recipes including step-by-step cooking procedures. The meal menu suggestion unit also constructs a system that provides a shopping list of ingredients and recipes in addition to suggesting meal menus. For example, it provides necessary ingredients and cooking methods based on the proposed menu. In this way, by providing a shopping list of ingredients and recipes, the user can easily prepare meals.

[0041] The meal menu suggestion unit can suggest meal menus using ingredients that correspond to the user's region and season. The meal menu suggestion unit, for example, suggests meal menus that use ingredients that correspond to the user's region and season. For example, it provides menus that use local specialties and seasonal ingredients. The meal menu suggestion unit also customizes meal menus that are suitable for the user, taking into account ingredients that correspond to the region and season. For example, it suggests menus that use seasonal vegetables and fruits. The meal menu suggestion unit also builds a system that provides meal menus that use ingredients that correspond to the user's region and season. For example, it provides menus that use local specialties and seasonal ingredients. In this way, it is possible to provide meal menus that are suitable for the user by using ingredients that correspond to the region and season.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The training menu providing unit can incorporate game elements into the training menu based on the user's hobbies and interests. For example, a point system or level-up system can be introduced into the training menu taking into account the user's hobbies and interests. The training menu providing unit can also suggest virtual challenges and competitions to allow the user to enjoy training. Furthermore, a system can be introduced that allows the user to receive rewards by completing specific exercises. In this way, incorporating game elements into the training menu based on the user's hobbies and interests can increase the enjoyment of training.

[0044] The training menu providing unit can collect data on the user's lifestyle habits and optimize the training menu based on that data. For example, it can collect the user's sleep time and stress level and suggest light exercises if the user is sleep deprived. It can also analyze the lifestyle data and provide a training menu tailored to the user's physical condition. For example, if stress is high, it can add exercises that have a relaxing effect. Furthermore, it can suggest a menu that emphasizes recovery if the user's sleep time is short. This allows the training menu to be optimized according to the user's lifestyle habits, enabling effective training.

[0045] In addition to providing feedback on training form, the training form feedback unit can analyze muscle movement and load and provide detailed feedback. For example, it can analyze the user's training form to check whether appropriate load is being applied to specific muscle groups. It can also evaluate muscle contraction and extension during exercise and provide advice on how to improve muscle usage. Furthermore, by analyzing muscle movement and load, it can provide even more detailed feedback. This allows the user to maximize the effectiveness of their training.

[0046] The training form feedback unit can provide audio and visual feedback in real time, allowing users to make immediate improvements. For example, a system can be developed that provides real-time audio and visual feedback on training form, allowing users to receive instant feedback while exercising. Audio guidance can also be used to point out form corrections, and visual feedback can be used to show the correct form. Furthermore, a system can be developed that provides real-time feedback, allowing users to instantly correct their form while exercising. This allows users to maximize the effectiveness of their training.

[0047] The meal menu suggestion unit can analyze the user's dietary history and suggest a meal menu that takes into account long-term nutritional balance. For example, it can collect the user's dietary history, identify nutrient deficiencies based on past dietary data, and suggest a menu that will compensate for them. It can also analyze the dietary history and, if a specific vitamin or mineral is lacking, suggest ingredients that contain that nutrient. It can also analyze past dietary data and suggest a balanced meal menu. This makes it possible to support the user's health by suggesting a meal menu that takes into account long-term nutritional balance.

[0048] The meal menu suggestion unit can work in conjunction with the user's training menu to suggest a meal menu that promotes post-training recovery. For example, it can work in conjunction with the user's training menu to suggest high-protein meals that help muscle recovery. It can also customize a meal menu to promote recovery based on the training menu. Furthermore, it can also build a system that suggests ingredients that contain nutrients needed after training. This makes it possible to maximize the effectiveness of training by suggesting a meal menu that promotes post-training recovery.

[0049] The meal menu suggestion unit can provide a shopping list of ingredients and recipes in addition to suggesting meal menus. For example, in addition to suggesting meal menus, it can provide a shopping list of necessary ingredients and generate a list of ingredients to purchase at the supermarket. It can also provide recipes corresponding to the suggested meal menus, allowing the user to easily prepare the meals. Furthermore, a system can be constructed that provides a shopping list of ingredients and recipes in addition to suggesting meal menus. This allows the user to easily prepare meals by providing a shopping list of ingredients and recipes.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The training menu provider provides a personalized training menu based on user information. For example, when the user enters information such as age, gender, weight, and exercise experience, the generation AI analyzes this data and generates an effective training menu tailored to individual needs and abilities. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates a training menu based on the prompt. Step 2: The training form feedback unit analyzes the training video of the user who is training based on the training menu provided by the training menu provider and provides feedback on correct form. For example, if a user uploads a video of squats, the generation AI analyzes the video and points out areas for improvement in form. The input to the generation AI is the training video filmed by the user, and the generation AI generates feedback based on that video. Step 3: The meal menu suggestion unit analyzes the meal photos taken by the user and suggests improved meal menus that are effective for training. For example, when a user uploads a photo of their meal, the generation AI analyzes the photo, evaluates the nutritional balance and calories, and suggests areas for improvement. The input to the generation AI is the photo of the meal taken by the user, and the generation AI suggests meal menus based on that photo.

[0052] (Example 2) A training support system according to an embodiment of the present invention provides a personalized training menu based on user information, analyzes training videos to provide feedback on correct form, and analyzes meal photos to suggest an improved meal menu. As a result, the training support system can maximize the effectiveness of training by providing a training menu tailored to the individual needs of the user, feedback on correct form, and an effective meal menu.

[0053] A training support system according to an embodiment includes a training menu providing unit, a training form feedback unit, and a meal menu suggestion unit. The training menu providing unit provides a personalized training menu based on user information. For example, when a user inputs information such as their age, gender, weight, and exercise experience, the generation AI analyzes this data and generates an effective training menu tailored to their individual needs and abilities. The generation AI receives input from the user in the form of prompts containing instructions on what the user wants the generation AI to do, and the generation AI generates a training menu based on the prompts. The training form feedback unit analyzes a training video of a user training based on the training menu provided by the training menu providing unit and provides feedback on correct form. For example, when a user uploads a video of squats, the generation AI analyzes the video and points out areas for improvement in form. The generation AI receives input from a training video filmed by the user, and the generation AI generates feedback based on the video. The meal menu suggestion unit analyzes photos of meals taken by the user and suggests improved meal menus that are effective for training. For example, when a user uploads photos of their meals, the generation AI analyzes the photos, evaluates their nutritional balance and calories, and suggests areas for improvement. The input to the generation AI is photos of meals taken by the user, and the generation AI suggests meal menus based on those photos. This allows the training support system to maximize the effectiveness of training by providing training menus tailored to the individual needs of the user, feedback on correct form, and effective meal menus.

[0054] The training menu providing unit can analyze the user's past training data and adjust the menu in real time according to the user's progress. The training menu providing unit, for example, collects data on the user's past training and adjusts the current training menu based on that data. For example, it analyzes past training records and changes the load and number of repetitions according to the user's progress. The training menu providing unit also monitors the user's training history in real time and dynamically adjusts the training menu according to the user's progress. For example, if the user achieves a goal with a particular exercise, it sets a new goal. The training menu providing unit also analyzes past training data to identify the user's strengths and weaknesses and customizes the training menu based on that. For example, if a particular muscle group is weak, it adds exercises to strengthen that area. This allows the training menu to be dynamically adjusted according to the user's progress, enabling effective training.

[0055] The training menu providing unit can evaluate the user's psychological state and motivation level using the emotion estimation function and adjust the training menu based on that. For example, the training menu providing unit evaluates the user's psychological state using the emotion estimation function and suggests a lighter training menu when motivation is low. For example, when stress is high, it adds exercises that have a relaxing effect. The training menu providing unit also monitors the user's motivation level in real time and suggests a more challenging training menu when motivation is high. For example, it introduces new exercises when motivation is high. The training menu providing unit also uses the emotion estimation function to customize the training menu according to the user's psychological state. For example, when the user is tired, it suggests a menu that emphasizes recovery. This allows for effective training by adjusting the training menu according to the user's psychological state and motivation.

[0056] The training menu providing unit can collect lifestyle habit data of the user and optimize the training menu based on that. The training menu providing unit, for example, collects the user's sleep time and stress level and adjusts the training menu based on that. For example, it suggests light exercises when the user is sleep deprived. The training menu providing unit also analyzes the lifestyle habit data and provides a training menu tailored to the user's physical condition. For example, it adds exercises that have a relaxing effect when stress is high. The training menu providing unit also customizes the training menu based on the user's lifestyle habit data. For example, it suggests a menu that emphasizes recovery when the user sleeps little. This allows the training menu to be optimized according to the user's lifestyle, enabling effective training.

[0057] The training menu providing unit can generate menus for rehabilitation and fitness in addition to training menus. For example, the training menu providing unit adds rehabilitation exercises to the training menu taking into account the user's health condition and rehabilitation needs. For example, it suggests exercises that support recovery from an injury. The training menu providing unit also incorporates fitness exercises into the training menu for users who are fitness-oriented. For example, it adds aerobic exercises to improve cardiopulmonary function. The training menu providing unit also generates menus for rehabilitation and fitness and customizes them to suit the user's needs. For example, it suggests exercises to strengthen specific muscle groups. This makes it possible to meet the needs of a wide range of users by providing menus for rehabilitation and fitness.

[0058] The training menu providing unit can incorporate game elements into the training menu based on the user's hobbies and interests. The training menu providing unit incorporates game elements into the training menu, for example, taking into account the user's hobbies and interests. For example, it introduces a point system or a level-up system. The training menu providing unit also incorporates game elements into the training menu to enable the user to enjoy training. For example, it proposes virtual challenges and competitions. The training menu providing unit also adds game elements to the training menu based on the user's interests. For example, it introduces a system that allows the user to receive rewards by completing specific exercises. In this way, incorporating game elements into the training menu based on the user's hobbies and interests increases the enjoyment of training.

[0059] The training menu providing unit uses the emotion estimation function to suggest a training menu that the user can enjoy, thereby maintaining motivation. The training menu providing unit, for example, uses the emotion estimation function to suggest a training menu that the user can enjoy. For example, it selects exercises based on the user's emotion score. The training menu providing unit also monitors the user's emotional state in real time to provide an enjoyable training menu. For example, it suggests challenging exercises when the user has strong positive emotions. The training menu providing unit also uses the emotion estimation function to customize a training menu to maintain the user's motivation. For example, it prioritizes suggesting exercises that the user can enjoy. In this way, suggesting a training menu based on the user's emotions makes it easier to maintain motivation.

[0060] The training form feedback unit can provide detailed feedback by analyzing muscle movement and load, in addition to providing feedback on the training form. The training form feedback unit, for example, analyzes the user's training form and evaluates muscle movement and load. For example, it checks whether an appropriate load is being applied to a specific muscle group. In addition to providing feedback on the training form, the training form feedback unit also analyzes muscle movement and load in detail. For example, it evaluates muscle contraction and extension during exercise. In addition, the training form feedback unit analyzes muscle movement and load, and provides detailed feedback to the user. For example, it gives advice on improving how the muscles are used in a specific exercise. In this way, more detailed feedback can be provided by analyzing muscle movement and load.

[0061] The training form feedback unit can add advice to the training form feedback that takes into account the user's physical condition and fatigue level. The training form feedback unit, for example, takes into account the user's physical condition and fatigue level and adds advice to the training form feedback. For example, if fatigue is accumulating, it suggests resting. The training form feedback unit also provides advice to the training form feedback that reflects the user's physical condition and fatigue level. For example, it suggests lighter exercises when the user is not feeling well. The training form feedback unit also monitors the user's physical condition and fatigue level and customizes the training form feedback based on that. For example, if fatigue is accumulating, it suggests modifying the user's form. In this way, more effective training is possible by providing advice that takes into account the user's physical condition and fatigue level.

[0062] The training form feedback unit can provide stretching and warm-up advice in addition to training form feedback. For example, the training form feedback unit provides stretching and warm-up advice in addition to training form feedback. For example, it suggests stretching methods before and after training. The training form feedback unit also evaluates the user's training form and adds appropriate stretching and warm-up advice. For example, it suggests a warm-up method suitable for a specific exercise. The training form feedback unit also incorporates stretching and warm-up advice into the training form feedback. For example, it suggests stretches that should be performed before training. In this way, providing stretching and warm-up advice improves the effectiveness of training.

[0063] The training form feedback unit can provide audio or visual feedback in real time, allowing the user to make instant improvements. For example, the training form feedback unit may build a system that provides audio or visual feedback on training form in real time. For example, the system may enable the user to receive instant feedback while exercising. The training form feedback unit may also provide audio or visual feedback in real time, allowing the user to instantly improve their form. For example, audio guidance may be used to point out form corrections during exercise. The training form feedback unit may also develop a system that provides real-time feedback, allowing the user to instantly correct their form while exercising. For example, visual feedback may be used to show the correct form. This allows the user to instantly improve their form by providing real-time feedback.

[0064] The training form feedback unit can use the emotion estimation function to identify the timing when the user is likely to accept feedback and provide feedback at that timing. The training form feedback unit, for example, uses the emotion estimation function to identify the timing when the user is likely to accept feedback. For example, feedback is provided when the user is relaxed. The training form feedback unit also monitors the user's emotional state in real time and provides feedback at a timing when the user is likely to accept feedback. For example, feedback is provided when the user has strong positive emotions. The training form feedback unit also uses the emotion estimation function to identify the timing when the user is likely to accept feedback and builds a system that provides feedback at that timing. For example, feedback is provided when the user is concentrating. This allows for effective form improvement by providing feedback at a timing when the user is likely to accept feedback.

[0065] The meal menu suggestion unit can analyze the user's dietary history and suggest a meal menu that takes into consideration long-term nutritional balance. The meal menu suggestion unit, for example, collects the user's dietary history and suggests a meal menu that takes into consideration long-term nutritional balance. For example, it identifies nutrient deficiencies based on past dietary data and suggests a menu that compensates for them. The meal menu suggestion unit also analyzes the dietary history and builds a system that evaluates the user's nutritional balance. For example, if a specific vitamin or mineral is lacking, it suggests ingredients that contain that nutrient. The meal menu suggestion unit also customizes a meal menu that takes into consideration long-term nutritional balance based on the user's dietary history. For example, it analyzes past dietary data and suggests a balanced meal menu. In this way, the user's health is supported by suggesting a meal menu that takes into consideration long-term nutritional balance.

[0066] The meal menu suggestion unit can evaluate a user's taste preferences and allergy information using an emotion estimation function and customize a meal menu based on the evaluation. The meal menu suggestion unit, for example, evaluates a user's taste preferences and allergy information using the emotion estimation function and customizes a meal menu based on the evaluation. For example, it suggests a menu using ingredients with flavors that the user likes. The meal menu suggestion unit also uses the emotion estimation function to analyze the user's taste preferences and allergy information in real time and customizes the meal menu. For example, it suggests a menu that avoids allergens. The meal menu suggestion unit also customizes the meal menu using data evaluated by the emotion estimation function based on the user's taste preferences and allergy information. For example, it suggests a menu using ingredients with flavors that the user likes. In this way, customizing a meal menu based on the user's taste preferences and allergy information improves user satisfaction.

[0067] The meal menu suggestion unit is capable of linking with the user's training menu to suggest a meal menu that promotes recovery after training. The meal menu suggestion unit, for example, links with the user's training menu to suggest a meal menu that promotes recovery after training. For example, it suggests high-protein meals that help muscle recovery. The meal menu suggestion unit also customizes a meal menu to promote recovery based on the training menu. For example, it suggests ingredients that contain nutrients needed after training. The meal menu suggestion unit also links with the user's training menu to build a system that provides a meal menu that promotes recovery. For example, it suggests meals that contain nutrients that should be taken after training. This maximizes the effectiveness of training by suggesting a meal menu that promotes recovery after training.

[0068] The meal menu suggestion unit can provide a shopping list of ingredients and recipes in addition to suggesting meal menus. For example, the meal menu suggestion unit provides a shopping list of necessary ingredients in addition to suggesting meal menus. For example, it generates a list of ingredients to be purchased at a supermarket based on the proposed menu. The meal menu suggestion unit also provides recipes corresponding to the proposed meal menus to enable the user to easily cook. For example, it provides recipes including step-by-step cooking procedures. The meal menu suggestion unit also constructs a system that provides a shopping list of ingredients and recipes in addition to suggesting meal menus. For example, it provides necessary ingredients and cooking methods based on the proposed menu. In this way, by providing a shopping list of ingredients and recipes, the user can easily prepare meals.

[0069] The meal menu suggestion unit can suggest meal menus using ingredients that correspond to the user's region and season. The meal menu suggestion unit, for example, suggests meal menus that use ingredients that correspond to the user's region and season. For example, it provides menus that use local specialties and seasonal ingredients. The meal menu suggestion unit also customizes meal menus that are suitable for the user, taking into account ingredients that correspond to the region and season. For example, it suggests menus that use seasonal vegetables and fruits. The meal menu suggestion unit also builds a system that provides meal menus that use ingredients that correspond to the user's region and season. For example, it provides menus that use local specialties and seasonal ingredients. In this way, it is possible to provide meal menus that are suitable for the user by using ingredients that correspond to the region and season.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The training menu providing unit can incorporate game elements into the training menu based on the user's hobbies and interests. For example, a point system or level-up system can be introduced into the training menu taking into account the user's hobbies and interests. The training menu providing unit can also suggest virtual challenges and competitions to allow the user to enjoy training. Furthermore, a system can be introduced that allows the user to receive rewards by completing specific exercises. In this way, incorporating game elements into the training menu based on the user's hobbies and interests can increase the enjoyment of training.

[0072] The training menu providing unit can collect data on the user's lifestyle habits and optimize the training menu based on that data. For example, it can collect the user's sleep time and stress level and suggest light exercises if the user is sleep deprived. It can also analyze the lifestyle data and provide a training menu tailored to the user's physical condition. For example, if stress is high, it can add exercises that have a relaxing effect. Furthermore, it can suggest a menu that emphasizes recovery if the user's sleep time is short. This allows the training menu to be optimized according to the user's lifestyle habits, enabling effective training.

[0073] The training menu providing unit can evaluate the user's psychological state and motivation level using the emotion estimation function and adjust the training menu based on that. For example, the emotion estimation function can evaluate the user's psychological state and suggest a lighter training menu if motivation is low. It can also add exercises that have a relaxing effect when stress levels are high. It can also monitor the user's motivation level in real time and provide a challenging training menu when motivation is high. Furthermore, it can suggest a menu that emphasizes recovery if the user is tired. This allows for effective training by adjusting the training menu according to the user's psychological state and motivation.

[0074] In addition to providing feedback on training form, the training form feedback unit can analyze muscle movement and load and provide detailed feedback. For example, it can analyze the user's training form to check whether appropriate load is being applied to specific muscle groups. It can also evaluate muscle contraction and extension during exercise and provide advice on how to improve muscle usage. Furthermore, by analyzing muscle movement and load, it can provide even more detailed feedback. This allows the user to maximize the effectiveness of their training.

[0075] The training form feedback unit can provide audio and visual feedback in real time, allowing users to make immediate improvements. For example, a system can be developed that provides real-time audio and visual feedback on training form, allowing users to receive instant feedback while exercising. Audio guidance can also be used to point out form corrections, and visual feedback can be used to show the correct form. Furthermore, a system can be developed that provides real-time feedback, allowing users to instantly correct their form while exercising. This allows users to maximize the effectiveness of their training.

[0076] The training form feedback unit can use the emotion estimation function to identify the timing when the user is most receptive to feedback and provide feedback at that timing. For example, the emotion estimation function can be used to provide feedback when the user is relaxed. It can also monitor the user's emotional state in real time and provide feedback when the user is feeling strongly positive. It can also build a system that provides feedback when the user is concentrating. This allows for effective form improvement by providing feedback at a timing when the user is most receptive.

[0077] The meal menu suggestion unit can analyze the user's dietary history and suggest a meal menu that takes into account long-term nutritional balance. For example, it can collect the user's dietary history, identify nutrient deficiencies based on past dietary data, and suggest a menu that will compensate for them. It can also analyze the dietary history and, if a specific vitamin or mineral is lacking, suggest ingredients that contain that nutrient. It can also analyze past dietary data and suggest a balanced meal menu. This makes it possible to support the user's health by suggesting a meal menu that takes into account long-term nutritional balance.

[0078] The meal menu suggestion unit can evaluate the user's taste preferences and allergy information using the emotion estimation function and customize the meal menu based on the evaluation. For example, the emotion estimation function can evaluate the user's taste preferences and allergy information and suggest a menu using ingredients with the user's favorite flavors. The emotion estimation function can also be used to suggest a menu that avoids allergens. Furthermore, the meal menu can be customized using data evaluated by the emotion estimation function based on the user's taste preferences and allergy information. In this way, user satisfaction can be improved by customizing the meal menu based on the user's taste preferences and allergy information.

[0079] The meal menu suggestion unit can work in conjunction with the user's training menu to suggest a meal menu that promotes post-training recovery. For example, it can work in conjunction with the user's training menu to suggest high-protein meals that help muscle recovery. It can also customize a meal menu to promote recovery based on the training menu. Furthermore, it can also build a system that suggests ingredients that contain nutrients needed after training. This makes it possible to maximize the effectiveness of training by suggesting a meal menu that promotes post-training recovery.

[0080] The meal menu suggestion unit can provide a shopping list of ingredients and recipes in addition to suggesting meal menus. For example, in addition to suggesting meal menus, it can provide a shopping list of necessary ingredients and generate a list of ingredients to purchase at the supermarket. It can also provide recipes corresponding to the suggested meal menus, allowing the user to easily prepare the meals. Furthermore, a system can be constructed that provides a shopping list of ingredients and recipes in addition to suggesting meal menus. This allows the user to easily prepare meals by providing a shopping list of ingredients and recipes.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The training menu provider provides a personalized training menu based on user information. For example, when the user enters information such as age, gender, weight, and exercise experience, the generation AI analyzes this data and generates an effective training menu tailored to individual needs and abilities. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates a training menu based on the prompt. Step 2: The training form feedback unit analyzes the training video of the user who is training based on the training menu provided by the training menu provider and provides feedback on correct form. For example, if a user uploads a video of squats, the generation AI analyzes the video and points out areas for improvement in form. The input to the generation AI is the training video filmed by the user, and the generation AI generates feedback based on that video. Step 3: The meal menu suggestion unit analyzes the meal photos taken by the user and suggests improved meal menus that are effective for training. For example, when a user uploads a photo of their meal, the generation AI analyzes the photo, evaluates the nutritional balance and calories, and suggests areas for improvement. The input to the generation AI is the photo of the meal taken by the user, and the generation AI suggests meal menus based on that photo.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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 a 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 emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] 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.

[0098] 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.

[0099] 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 AI 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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 a 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 emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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 AI 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 a 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 emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, 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. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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 AI 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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. The device comprises a training menu providing unit that provides a personalized training menu based on user information, a training form feedback unit that analyzes a training video of a user who is training based on the training menu provided by the training menu providing unit and provides feedback on correct form, and a meal menu suggestion unit that analyzes meal photos taken by the user and suggests an improved meal menu that is effective for training. A system characterized by:

2. The training menu providing unit Analyzing the user's past training data and adjusting the training menu in real time according to the progress of the training menu 2. The system of claim 1.

3. The training menu providing unit The user's mental state and motivation level are evaluated using an emotion estimation function, and the training menu is adjusted based on the evaluation.

2. The system of claim 1.

4. The training menu providing unit Collecting lifestyle data of the user and optimizing the training menu based on the collected data 2. The system of claim 1.

5. The training menu providing unit In addition to the training menu, menus for rehabilitation and fitness are also generated.

2. The system of claim 1.

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