System

Abstract

An object of a system according to an embodiment is to create an optimal training menu for an individual user and provide effective guidance and advice.SOLUTION: A system according to an embodiment includes a training menu generator, an instructor, a data collector, and an advice provider. The training menu preparing unit prepares a training menu on the basis of at least one piece of information among physical data, a goal, and an athletic event of the user. The instruction unit performs an instruction based on the training menu created by the training menu creation unit. The data collection unit collects training data. The advice providing unit analyzes the training data collected by the data collection unit and provides advice to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] Conventional techniques have had the problem of making it difficult to create optimal training menus for individual users and provide effective guidance and advice.

[0005] The system according to the embodiment aims to create an optimal training menu for each individual user and provide effective guidance and advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a training menu creation unit, an instruction unit, a data collection unit, and an advice provision unit. The training menu creation unit creates a training menu based on at least one of the user's physical data, goals, and sport. The instruction unit provides instruction based on the training menu created by the training menu creation unit. The data collection unit collects training data. The advice provision unit analyzes the training data collected by the data collection unit and provides advice to the user. [Effects of the Invention]

[0007] The system according to the embodiment can create an optimal training menu for each individual user and provide effective guidance and advice. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The personal trainer system according to an embodiment of the present invention uses a generation AI to aggregate training know-how and knowledge for various sports, and creates training menus, provides instruction, collects data, and provides advice to users. This allows the personal trainer system to provide users with individually optimized training menus and achieve effective training.

[0029] A personal trainer system according to an embodiment includes a training menu creation unit, an instruction unit, a data collection unit, and an advice provision unit. The training menu creation unit creates a training menu based on at least one of the user's physical data, goals, and sport. For example, the training menu creation unit receives input information such as the user's age, gender, weight, height, exercise experience, and goals (e.g., improving muscle strength or endurance), and a generation AI analyzes this information to create a training menu optimized for each individual user. The instruction unit provides instruction based on the training menu created by the training menu creation unit. For example, the instruction unit explains correct exercise form and important points using audio and video. The instruction unit also provides real-time feedback during training to correct form and adjust load. The data collection unit collects training data. For example, the data collection unit collects data such as training progress, heart rate, calories burned, and changes in muscle strength. The advice provision unit analyzes the training data collected by the data collection unit and provides advice to the user. For example, the advice providing unit may provide advice on diet and rest to maximize the effectiveness of training, as well as advice on adjusting the frequency and intensity of training, etc. This allows the personal trainer system according to the embodiment to provide an optimal training menu for each individual user, enabling effective training.

[0030] The training menu creation unit can analyze the user's past training history and dynamically adjust the training menu based on past results. In the training menu creation unit, for example, the generation AI analyzes the user's past training history and dynamically adjusts the training menu based on past results and failures. For example, it prioritizes incorporating exercises that were effective in the past. The training menu creation unit also analyzes the user's training history and adjusts the training menu based on results over a specific period. For example, it optimizes the load and number of repetitions based on past data. In addition, the generation AI analyzes the user's past training data, eliminates exercises that did not produce results, and suggests new exercises. For example, it replaces exercises that were less effective with other exercises. In this way, by dynamically adjusting the training menu based on past training history, it is possible to provide more effective training.

[0031] The training menu creation unit can analyze the user's dietary data and propose a training menu based on nutritional balance. In the training menu creation unit, for example, a generation AI analyzes the user's dietary data and proposes a training menu based on nutritional balance. For example, if protein intake is insufficient, a menu is created that strengthens strength training. The training menu creation unit also analyzes the user's dietary data and proposes a training menu that takes nutritional balance into consideration. For example, it incorporates exercises to compensate for vitamin deficiencies. In addition, the training menu creation unit uses the generation AI to propose a training menu that optimizes nutritional balance based on the user's dietary data. For example, it creates a menu that includes many exercises that take calorie consumption into consideration. In this way, by analyzing the user's dietary data and proposing a training menu based on nutritional balance, more effective training can be provided.

[0032] The training menu creation unit can analyze the user's sleep data and suggest the optimal training time slot. In the training menu creation unit, for example, a generation AI analyzes the user's sleep data and suggests the optimal training time slot. For example, the menu is adjusted so that the user trains during the time slot when they are most energetic. The training menu creation unit also suggests a time slot for maximizing the effectiveness of training based on the user's sleep data. For example, training can be performed during the time slot after deep sleep. In addition, the training menu creation unit uses a generation AI to analyze the user's sleep data and suggest a training time slot that takes fatigue recovery into consideration. For example, it suggests light exercises on days when the user has insufficient sleep. In this way, the effectiveness of training can be maximized by analyzing the user's sleep data and suggesting the optimal training time slot.

[0033] The training menu creation unit can analyze the user's genetic information and suggest a training menu based on the genetic characteristics. In the training menu creation unit, for example, a generation AI analyzes the user's genetic information and suggests a training menu based on the genetic characteristics. For example, genetic characteristics related to muscle growth rate and endurance are taken into consideration. The training menu creation unit also creates an optimal training menu based on the user's genetic information. For example, a menu that strengthens strength training is suggested for a user who has genetically strong muscles. In addition, the training menu creation unit analyzes the user's genetic information and suggests exercises based on the genetic characteristics. For example, a menu that strengthens endurance training is created for a user who has genetically high endurance. In this way, by analyzing the user's genetic information and suggesting a training menu based on the genetic characteristics, more effective training can be provided.

[0034] The training menu creation unit can analyze the user's lifestyle data and adjust the training menu based on that. In the training menu creation unit, for example, the generation AI analyzes the user's lifestyle data and suggests a training menu based on the user's work stress level. For example, on days when stress is high, it can incorporate exercises that have a relaxing effect. The training menu creation unit also adjusts the training menu based on the user's daily activity level. For example, it can suggest a lighter training menu to a user who exercises a lot on a daily basis. In addition, the training menu creation unit analyzes the user's lifestyle data and dynamically adjusts the training menu. For example, it can adjust the training intensity depending on how busy the user is at work. In this way, by analyzing the user's lifestyle data and adjusting the training menu based on that, it is possible to provide more effective training.

[0035] The training menu creation unit can analyze the user's health checkup data and suggest a training menu based on the health condition. In the training menu creation unit, for example, a generation AI analyzes the user's health checkup data and suggests a training menu based on the health condition. For example, it suggests exercises that take blood pressure and heart rate into consideration. The training menu creation unit also creates a training menu based on the user's health checkup data. For example, it suggests a menu that includes a lot of aerobic exercise for a user with high cholesterol levels. In addition, the training menu creation unit analyzes the user's health checkup data and suggests exercises based on the health condition. For example, it suggests exercises to manage blood sugar levels for a user at high risk of diabetes. In this way, by analyzing the user's health checkup data and suggesting a training menu based on the health condition, more effective training can be provided.

[0036] The training menu creation unit can analyze at least one of the user's hobbies and interests and suggest a fun training menu based on that. In the training menu creation unit, for example, a generation AI analyzes the user's hobbies and interests and suggests a fun training menu based on that. For example, for a user who likes dancing, the training menu creation unit creates a menu that includes many dance exercises. The training menu creation unit also creates a training menu based on the user's hobbies and interests. For example, for a user who likes the outdoors, the training menu creation unit suggests exercises to be done outdoors. In addition, the training menu creation unit analyzes the user's hobbies and interests and suggests a fun training menu using the generation AI. For example, for a user who likes music, the training menu creation unit suggests exercises that go along with music. In this way, by analyzing the user's hobbies and interests and suggesting a fun training menu based on them, it is possible to increase the continuity of training.

[0037] The data collection unit can collect the user's training data over a long period of time and analyze the long-term training effects. In the data collection unit, for example, the generation AI collects the user's training data over a long period of time and analyzes the long-term training effects. For example, it evaluates changes in muscle strength based on data over several months. The data collection unit also collects the user's training data over a long period of time and analyzes the training progress. For example, it adjusts the training menu based on past data. In addition, the data collection unit collects the user's training data over a long period of time and analyzes the training effects. For example, it suggests a training menu aimed at achieving long-term goals. In this way, by collecting the user's training data over a long period of time and analyzing the long-term training effects, it is possible to evaluate the training progress and provide the optimal training menu.

[0038] The data collection unit can compare the user's training data with that of other users and provide a benchmark. In the data collection unit, for example, the generation AI compares the user's training data with that of other users and provides a benchmark. For example, the progress is evaluated by comparing with users of the same age and gender. The data collection unit also compares the user's training data with that of other users and evaluates the effectiveness of the training. For example, the results are measured by comparing with users who have the same goals. In addition, the data collection unit also compares the user's training data with that of other users and provides a benchmark. For example, advice for achieving goals is provided by comparing with data of top athletes. In this way, by comparing the user's training data with that of other users and providing a benchmark, the progress of training can be evaluated and an optimal training menu can be provided.

[0039] The data collection unit can integrate the user's training data with other health data to analyze their overall health condition. In the data collection unit, for example, the generation AI integrates the user's training data with other health data to analyze their overall health condition. For example, it combines dietary data and training data to evaluate nutritional balance. The data collection unit also integrates the user's training data with sleep data to analyze their overall health condition. For example, it evaluates the relationship between sleep quality and training effectiveness. In the data collection unit, the generation AI also integrates the user's training data with other health data to analyze their overall health condition. For example, it combines heart rate data and training data to evaluate their heart health. In this way, by integrating the user's training data with other health data and analyzing their overall health condition, it is possible to evaluate their training progress and provide an optimal training menu.

[0040] The data collection unit can visualize the user's training data and provide an intuitively understandable report. In the data collection unit, for example, the generation AI visualizes the user's training data and provides an intuitively understandable report. For example, the training progress is displayed using graphs and charts. The data collection unit also visualizes the user's training data and provides an intuitively understandable report of the training effect. For example, the results for each exercise are displayed color-coded. In addition, the data collection unit uses the generation AI to visualize the user's training data and provide an intuitively understandable report. For example, the training results are displayed in chronological order. In this way, by visualizing the user's training data and providing an intuitively understandable report, the training progress can be evaluated and an optimal training menu can be provided.

[0041] The advice providing unit can analyze the user's training data and provide dietary or rest advice to maximize the effectiveness of training. For example, the generation AI analyzes the user's training data and provides dietary or rest advice to maximize the effectiveness of training. For example, specific advice is provided regarding meal menus and rest timing. The advice providing unit also analyzes the user's training data and provides dietary advice to maximize the effectiveness of training. For example, advice is provided regarding protein and vitamin intake. The advice providing unit also analyzes the user's training data and provides rest advice to maximize the effectiveness of training. For example, advice is provided regarding appropriate sleep duration and relaxation methods. In this way, the effectiveness of training can be improved by analyzing the user's training data and providing dietary and rest advice to maximize the effectiveness of training.

[0042] The advice providing unit can analyze the user's training data and provide advice regarding adjustment of training frequency or intensity. The advice providing unit, for example, has a generation AI analyze the user's training data and provide advice regarding adjustment of training frequency or intensity. For example, it provides specific advice regarding the optimal number of times per week to train and at what intensity the training should be performed. The advice providing unit also analyzes the user's training data and provides advice regarding adjustment of training frequency. For example, it provides advice regarding how long the interval between training sessions should be. The advice providing unit also analyzes the user's training data and provides advice regarding adjustment of training intensity. For example, it provides advice regarding how much the training load should be increased or decreased. In this way, the effectiveness of training can be improved by analyzing the user's training data and providing advice regarding adjustment of training frequency and intensity.

[0043] The advice providing unit can analyze the user's training data and provide advice to maintain motivation toward achieving goals. For example, the generation AI analyzes the user's training data and provides advice to maintain motivation toward achieving goals. For example, specific advice is provided regarding goal setting and encouraging messages. The advice providing unit also analyzes the user's training data and provides advice to maintain motivation. For example, feedback is provided according to the training progress. The advice providing unit also analyzes the user's training data and provides advice to maintain motivation toward achieving goals. For example, advice is provided to set short-term goals and increase a sense of accomplishment. In this way, the effectiveness of training can be improved by analyzing the user's training data and providing advice to maintain motivation toward achieving goals.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The training menu creation unit can analyze the user's hobbies and interests and suggest a training menu based on them. For example, for a user who likes dancing, a menu that includes many dance exercises can be created. Also, for a user who likes the outdoors, outdoor exercises can be suggested. Furthermore, for a user who likes music, exercises to go along with music can be suggested. In this way, by providing a training menu that takes into account the user's hobbies and interests, it is possible to increase the continuity of training.

[0046] The training menu creation unit can analyze the user's genetic information and propose a training menu based on the user's genetic characteristics. For example, it takes into account genetic characteristics related to muscle growth rate and endurance. It can also propose a menu that strengthens muscle strength training for a user who has genetically strong muscles. It can also create a menu that strengthens endurance training for a user who has genetically strong endurance. In this way, by providing a training menu based on the user's genetic information, more effective training can be achieved.

[0047] The training menu creation unit can analyze the user's lifestyle data and adjust the training menu based on that. For example, it can suggest a training menu based on the user's stress level at work. On days when stress is high, it can incorporate exercises that have a relaxing effect. It can also suggest a lighter training menu to users who exercise a lot on a daily basis. It can also adjust the intensity of training depending on how busy the user is at work. In this way, more effective training can be achieved by providing a training menu based on the user's lifestyle data.

[0048] The training menu creation unit can analyze the user's health checkup data and suggest a training menu based on the user's health condition. For example, it can suggest exercises that take blood pressure and heart rate into consideration. For a user with high cholesterol levels, it can suggest a menu that includes a lot of aerobic exercise. Furthermore, it can suggest exercises to manage blood sugar levels to a user at high risk of diabetes. In this way, by providing a training menu based on the user's health checkup data, more effective training can be achieved.

[0049] The training menu creation unit can analyze the user's sleep data and suggest optimal training times. For example, it can adjust the menu so that the user trains during the time when they are most energetic. It can also suggest training after a deep sleep. It can also suggest lighter exercises on days when the user has not had enough sleep. In this way, the effectiveness of training can be maximized by providing a training menu based on the user's sleep data.

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

[0051] Step 1: The training menu creation unit creates a training menu based on at least one of the user's physical data, goals, and sport. For example, the training menu creation unit receives input information such as the user's age, gender, weight, height, exercise experience, and goals (e.g., improving muscle strength or endurance), and the generation AI analyzes this information to create the optimal training menu for each individual user. Step 2: The instructor provides instruction based on the training menu created by the training menu creation department. For example, the instructor explains the correct form for exercises and important points to note using audio and video. The instructor also provides real-time feedback during training, correcting form and adjusting load. Step 3: The data collection unit collects training data, such as the training status, heart rate, calories burned, and changes in muscle strength. Step 4: The advice providing unit analyzes the training data collected by the data collecting unit and provides advice to the user. For example, the advice providing unit may provide advice on diet and rest to maximize the effect of training, or advice on adjusting the frequency and intensity of training.

[0052] (Example 2) The personal trainer system according to an embodiment of the present invention uses a generation AI to aggregate training know-how and knowledge for various sports, and creates training menus, provides instruction, collects data, and provides advice to users. This allows the personal trainer system to provide users with individually optimized training menus and achieve effective training.

[0053] A personal trainer system according to an embodiment includes a training menu creation unit, an instruction unit, a data collection unit, and an advice provision unit. The training menu creation unit creates a training menu based on at least one of the user's physical data, goals, and sport. For example, the training menu creation unit receives input information such as the user's age, gender, weight, height, exercise experience, and goals (e.g., improving muscle strength or endurance), and a generation AI analyzes this information to create a training menu optimized for each individual user. The instruction unit provides instruction based on the training menu created by the training menu creation unit. For example, the instruction unit explains correct exercise form and important points using audio and video. The instruction unit also provides real-time feedback during training to correct form and adjust load. The data collection unit collects training data. For example, the data collection unit collects data such as training progress, heart rate, calories burned, and changes in muscle strength. The advice provision unit analyzes the training data collected by the data collection unit and provides advice to the user. For example, the advice providing unit may provide advice on diet and rest to maximize the effectiveness of training, as well as advice on adjusting the frequency and intensity of training, etc. This allows the personal trainer system according to the embodiment to provide an optimal training menu for each individual user, enabling effective training.

[0054] The training menu creation unit can analyze the user's past training history and dynamically adjust the training menu based on past results. In the training menu creation unit, for example, the generation AI analyzes the user's past training history and dynamically adjusts the training menu based on past results and failures. For example, it prioritizes incorporating exercises that were effective in the past. The training menu creation unit also analyzes the user's training history and adjusts the training menu based on results over a specific period. For example, it optimizes the load and number of repetitions based on past data. In addition, the generation AI analyzes the user's past training data, eliminates exercises that did not produce results, and suggests new exercises. For example, it replaces exercises that were less effective with other exercises. In this way, by dynamically adjusting the training menu based on past training history, it is possible to provide more effective training.

[0055] The training menu creation unit can monitor the user's psychological state in real time and suggest a training menu to maintain motivation. In the training menu creation unit, for example, a generation AI monitors the user's psychological state in real time and suggests a training menu to maintain motivation. For example, the training menu creation unit analyzes the user's stress level and incorporates exercises that have a relaxing effect. The training menu creation unit also monitors the user's psychological state and, if motivation is low, suggests a training menu that will give the user a sense of accomplishment in a short period of time. For example, a menu that includes many easy exercises is created. In addition, the training menu creation unit analyzes the user's psychological state and suggests exercises to increase motivation. For example, a menu that includes many exercises that the user likes. In this way, the user's psychological state is monitored and a training menu to maintain motivation is suggested, thereby increasing the continuity of training.

[0056] The training menu creation unit uses the emotion estimation function to generate a training menu according to the user's emotional state and can suggest exercises that elicit positive emotions. The training menu creation unit, for example, uses the emotion estimation function to generate a training menu according to the user's emotional state. For example, if the user is tired, it suggests exercises that have a relaxing effect. The training menu creation unit also analyzes the user's emotional state and suggests exercises that elicit positive emotions. For example, it creates a menu that includes many exercises that the user finds enjoyable. The training menu creation unit also uses the emotion estimation function to dynamically adjust the training menu according to the user's emotional state. For example, if the user is feeling stressed, it suggests exercises that have a stress-relieving effect. In this way, the training effectiveness can be improved by generating a training menu according to the user's emotional state and suggesting exercises that elicit positive emotions.

[0057] The training menu creation unit can analyze the user's dietary data and propose a training menu based on nutritional balance. In the training menu creation unit, for example, a generation AI analyzes the user's dietary data and proposes a training menu based on nutritional balance. For example, if protein intake is insufficient, a menu is created that strengthens strength training. The training menu creation unit also analyzes the user's dietary data and proposes a training menu that takes nutritional balance into consideration. For example, it incorporates exercises to compensate for vitamin deficiencies. In addition, the training menu creation unit uses the generation AI to propose a training menu that optimizes nutritional balance based on the user's dietary data. For example, it creates a menu that includes many exercises that take calorie consumption into consideration. In this way, by analyzing the user's dietary data and proposing a training menu based on nutritional balance, more effective training can be provided.

[0058] The training menu creation unit can analyze the user's sleep data and suggest the optimal training time slot. In the training menu creation unit, for example, a generation AI analyzes the user's sleep data and suggests the optimal training time slot. For example, the menu is adjusted so that the user trains during the time slot when they are most energetic. The training menu creation unit also suggests a time slot for maximizing the effectiveness of training based on the user's sleep data. For example, training can be performed during the time slot after deep sleep. In addition, the training menu creation unit uses a generation AI to analyze the user's sleep data and suggest a training time slot that takes fatigue recovery into consideration. For example, it suggests light exercises on days when the user has insufficient sleep. In this way, the effectiveness of training can be maximized by analyzing the user's sleep data and suggesting the optimal training time slot.

[0059] The training menu creation unit can use the emotion estimation function to monitor the emotions of the user when performing a training menu in real time and provide feedback to elicit positive emotions. The training menu creation unit, for example, uses the emotion estimation function to monitor the emotions of the user when performing a training menu in real time. For example, if the user is tired, it displays an encouraging message. The training menu creation unit also analyzes the user's emotional state in real time and provides feedback to elicit positive emotions. For example, it suggests exercises that the user finds enjoyable. The training menu creation unit also uses the emotion estimation function to provide feedback in real time according to the user's emotional state. For example, if the user is feeling stressed, it suggests exercises that have a relaxing effect. In this way, the effectiveness of training can be improved by monitoring the emotions of the user when performing a training menu in real time and providing feedback to elicit positive emotions.

[0060] The training menu creation unit can analyze the user's genetic information and suggest a training menu based on the genetic characteristics. In the training menu creation unit, for example, a generation AI analyzes the user's genetic information and suggests a training menu based on the genetic characteristics. For example, genetic characteristics related to muscle growth rate and endurance are taken into consideration. The training menu creation unit also creates an optimal training menu based on the user's genetic information. For example, a menu that strengthens strength training is suggested for a user who has genetically strong muscles. In addition, the training menu creation unit analyzes the user's genetic information and suggests exercises based on the genetic characteristics. For example, a menu that strengthens endurance training is created for a user who has genetically high endurance. In this way, by analyzing the user's genetic information and suggesting a training menu based on the genetic characteristics, more effective training can be provided.

[0061] The training menu creation unit can analyze the user's lifestyle data and adjust the training menu based on that. In the training menu creation unit, for example, the generation AI analyzes the user's lifestyle data and suggests a training menu based on the user's work stress level. For example, on days when stress is high, it can incorporate exercises that have a relaxing effect. The training menu creation unit also adjusts the training menu based on the user's daily activity level. For example, it can suggest a lighter training menu to a user who exercises a lot on a daily basis. In addition, the training menu creation unit analyzes the user's lifestyle data and dynamically adjusts the training menu. For example, it can adjust the training intensity depending on how busy the user is at work. In this way, by analyzing the user's lifestyle data and adjusting the training menu based on that, it is possible to provide more effective training.

[0062] The training menu creation unit can use the emotion estimation function to set training goals according to the user's emotional state and suggest a training menu to enhance the user's sense of accomplishment. The training menu creation unit, for example, uses the emotion estimation function to set training goals according to the user's emotional state. For example, if the user has positive emotions, it sets a high goal. The training menu creation unit also analyzes the user's emotional state and suggests a training menu to enhance the user's sense of accomplishment. For example, it sets a goal that can be achieved in a short period of time. The training menu creation unit also uses the emotion estimation function to dynamically adjust training goals according to the user's emotional state. For example, if the user is feeling stressed, it sets a lower goal. In this way, the effectiveness of training can be improved by setting training goals according to the user's emotional state and suggesting a menu to enhance the user's sense of accomplishment.

[0063] The training menu creation unit can analyze the user's health checkup data and suggest a training menu based on the health condition. In the training menu creation unit, for example, a generation AI analyzes the user's health checkup data and suggests a training menu based on the health condition. For example, it suggests exercises that take blood pressure and heart rate into consideration. The training menu creation unit also creates a training menu based on the user's health checkup data. For example, it suggests a menu that includes a lot of aerobic exercise for a user with high cholesterol levels. In addition, the training menu creation unit analyzes the user's health checkup data and suggests exercises based on the health condition. For example, it suggests exercises to manage blood sugar levels for a user at high risk of diabetes. In this way, by analyzing the user's health checkup data and suggesting a training menu based on the health condition, more effective training can be provided.

[0064] The training menu creation unit can analyze at least one of the user's hobbies and interests and suggest a fun training menu based on that. In the training menu creation unit, for example, a generation AI analyzes the user's hobbies and interests and suggests a fun training menu based on that. For example, for a user who likes dancing, the training menu creation unit creates a menu that includes many dance exercises. The training menu creation unit also creates a training menu based on the user's hobbies and interests. For example, for a user who likes the outdoors, the training menu creation unit suggests exercises to be done outdoors. In addition, the training menu creation unit analyzes the user's hobbies and interests and suggests a fun training menu using the generation AI. For example, for a user who likes music, the training menu creation unit suggests exercises that go along with music. In this way, by analyzing the user's hobbies and interests and suggesting a fun training menu based on them, it is possible to increase the continuity of training.

[0065] The training menu creation unit can use the emotion estimation function to monitor the emotions of the user when performing a training menu in real time and provide feedback to elicit positive emotions. The training menu creation unit, for example, uses the emotion estimation function to monitor the emotions of the user when performing a training menu in real time. For example, if the user is tired, it displays an encouraging message. The training menu creation unit also analyzes the user's emotional state in real time and provides feedback to elicit positive emotions. For example, it suggests exercises that the user finds enjoyable. The training menu creation unit also uses the emotion estimation function to provide feedback in real time according to the user's emotional state. For example, if the user is feeling stressed, it suggests exercises that have a relaxing effect. In this way, the effectiveness of training can be improved by monitoring the emotions of the user when performing a training menu in real time and providing feedback to elicit positive emotions.

[0066] The data collection unit can collect the user's training data over a long period of time and analyze the long-term training effects. In the data collection unit, for example, the generation AI collects the user's training data over a long period of time and analyzes the long-term training effects. For example, it evaluates changes in muscle strength based on data over several months. The data collection unit also collects the user's training data over a long period of time and analyzes the training progress. For example, it adjusts the training menu based on past data. In addition, the data collection unit collects the user's training data over a long period of time and analyzes the training effects. For example, it suggests a training menu aimed at achieving long-term goals. In this way, by collecting the user's training data over a long period of time and analyzing the long-term training effects, it is possible to evaluate the training progress and provide the optimal training menu.

[0067] The data collection unit can compare the user's training data with that of other users and provide a benchmark. In the data collection unit, for example, the generation AI compares the user's training data with that of other users and provides a benchmark. For example, the progress is evaluated by comparing with users of the same age and gender. The data collection unit also compares the user's training data with that of other users and evaluates the effectiveness of the training. For example, the results are measured by comparing with users who have the same goals. In addition, the data collection unit also compares the user's training data with that of other users and provides a benchmark. For example, advice for achieving goals is provided by comparing with data of top athletes. In this way, by comparing the user's training data with that of other users and providing a benchmark, the progress of training can be evaluated and an optimal training menu can be provided.

[0068] The data collection unit can use the emotion estimation function to integrate the user's training data and emotion data and analyze the training effect based on changes in emotion. For example, the data collection unit uses the emotion estimation function to integrate the user's training data and emotion data and analyze the training effect based on changes in emotion. For example, the training effect during periods of strong positive emotion is evaluated. The data collection unit also integrates the user's emotion data with the training data and analyzes the training effect based on changes in emotion. For example, the data collection unit analyzes the training results during periods of low stress. The data collection unit also uses the emotion estimation function to integrate the user's emotion data with the training data and analyze the training effect based on changes in emotion. For example, the data collection unit evaluates the impact of emotional fluctuations on training results. In this way, by using the emotion estimation function to integrate the user's training data and emotion data and analyze the training effect based on changes in emotion, it is possible to evaluate the progress of training and provide an optimal training menu.

[0069] The data collection unit can integrate the user's training data with other health data to analyze their overall health condition. In the data collection unit, for example, the generation AI integrates the user's training data with other health data to analyze their overall health condition. For example, it combines dietary data and training data to evaluate nutritional balance. The data collection unit also integrates the user's training data with sleep data to analyze their overall health condition. For example, it evaluates the relationship between sleep quality and training effectiveness. In the data collection unit, the generation AI also integrates the user's training data with other health data to analyze their overall health condition. For example, it combines heart rate data and training data to evaluate their heart health. In this way, by integrating the user's training data with other health data and analyzing their overall health condition, it is possible to evaluate their training progress and provide an optimal training menu.

[0070] The data collection unit can visualize the user's training data and provide an intuitively understandable report. In the data collection unit, for example, the generation AI visualizes the user's training data and provides an intuitively understandable report. For example, the training progress is displayed using graphs and charts. The data collection unit also visualizes the user's training data and provides an intuitively understandable report of the training effect. For example, the results for each exercise are displayed color-coded. In addition, the data collection unit uses the generation AI to visualize the user's training data and provide an intuitively understandable report. For example, the training results are displayed in chronological order. In this way, by visualizing the user's training data and providing an intuitively understandable report, the training progress can be evaluated and an optimal training menu can be provided.

[0071] The data collection unit can use the emotion estimation function to integrate the user's training data and emotion data and analyze the training effect based on changes in emotion. For example, the data collection unit uses the emotion estimation function to integrate the user's training data and emotion data and analyze the training effect based on changes in emotion. For example, the training effect during periods of strong positive emotion is evaluated. The data collection unit also integrates the user's emotion data with the training data and analyzes the training effect based on changes in emotion. For example, the data collection unit analyzes the training results during periods of low stress. The data collection unit also uses the emotion estimation function to integrate the user's emotion data with the training data and analyze the training effect based on changes in emotion. For example, the data collection unit evaluates the impact of emotional fluctuations on training results. In this way, by using the emotion estimation function to integrate the user's training data and emotion data and analyze the training effect based on changes in emotion, it is possible to evaluate the progress of training and provide an optimal training menu.

[0072] The advice providing unit can analyze the user's training data and provide dietary or rest advice to maximize the effectiveness of training. For example, the generation AI analyzes the user's training data and provides dietary or rest advice to maximize the effectiveness of training. For example, specific advice is provided regarding meal menus and rest timing. The advice providing unit also analyzes the user's training data and provides dietary advice to maximize the effectiveness of training. For example, advice is provided regarding protein and vitamin intake. The advice providing unit also analyzes the user's training data and provides rest advice to maximize the effectiveness of training. For example, advice is provided regarding appropriate sleep duration and relaxation methods. In this way, the effectiveness of training can be improved by analyzing the user's training data and providing dietary and rest advice to maximize the effectiveness of training.

[0073] The advice providing unit can analyze the user's training data and provide advice regarding adjustment of training frequency or intensity. The advice providing unit, for example, has a generation AI analyze the user's training data and provide advice regarding adjustment of training frequency or intensity. For example, it provides specific advice regarding the optimal number of times per week to train and at what intensity the training should be performed. The advice providing unit also analyzes the user's training data and provides advice regarding adjustment of training frequency. For example, it provides advice regarding how long the interval between training sessions should be. The advice providing unit also analyzes the user's training data and provides advice regarding adjustment of training intensity. For example, it provides advice regarding how much the training load should be increased or decreased. In this way, the effectiveness of training can be improved by analyzing the user's training data and providing advice regarding adjustment of training frequency and intensity.

[0074] The advice providing unit can analyze the user's training data and provide advice to maintain motivation toward achieving goals. For example, the generation AI analyzes the user's training data and provides advice to maintain motivation toward achieving goals. For example, specific advice is provided regarding goal setting and encouraging messages. The advice providing unit also analyzes the user's training data and provides advice to maintain motivation. For example, feedback is provided according to the training progress. The advice providing unit also analyzes the user's training data and provides advice to maintain motivation toward achieving goals. For example, advice is provided to set short-term goals and increase a sense of accomplishment. In this way, the effectiveness of training can be improved by analyzing the user's training data and providing advice to maintain motivation toward achieving goals.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The training menu creation unit can analyze the user's hobbies and interests and suggest a training menu based on them. For example, for a user who likes dancing, a menu that includes many dance exercises can be created. Also, for a user who likes the outdoors, outdoor exercises can be suggested. Furthermore, for a user who likes music, exercises to go along with music can be suggested. In this way, by providing a training menu that takes into account the user's hobbies and interests, it is possible to increase the continuity of training.

[0077] The training menu creation unit can analyze the user's genetic information and propose a training menu based on the user's genetic characteristics. For example, it takes into account genetic characteristics related to muscle growth rate and endurance. It can also propose a menu that strengthens muscle strength training for a user who has genetically strong muscles. It can also create a menu that strengthens endurance training for a user who has genetically strong endurance. In this way, by providing a training menu based on the user's genetic information, more effective training can be achieved.

[0078] The training menu creation unit can analyze the user's lifestyle data and adjust the training menu based on that. For example, it can suggest a training menu based on the user's stress level at work. On days when stress is high, it can incorporate exercises that have a relaxing effect. It can also suggest a lighter training menu to users who exercise a lot on a daily basis. It can also adjust the intensity of training depending on how busy the user is at work. In this way, more effective training can be achieved by providing a training menu based on the user's lifestyle data.

[0079] The training menu creation unit can analyze the user's health checkup data and suggest a training menu based on the user's health condition. For example, it can suggest exercises that take blood pressure and heart rate into consideration. For a user with high cholesterol levels, it can suggest a menu that includes a lot of aerobic exercise. Furthermore, it can suggest exercises to manage blood sugar levels to a user at high risk of diabetes. In this way, by providing a training menu based on the user's health checkup data, more effective training can be achieved.

[0080] The training menu creation unit can analyze the user's sleep data and suggest optimal training times. For example, it can adjust the menu so that the user trains during the time when they are most energetic. It can also suggest training after a deep sleep. It can also suggest lighter exercises on days when the user has not had enough sleep. In this way, the effectiveness of training can be maximized by providing a training menu based on the user's sleep data.

[0081] The training menu creation unit can use the emotion estimation function to generate a training menu according to the user's emotional state and suggest exercises that will elicit positive emotions. For example, if the user is tired, it can suggest exercises that have a relaxing effect. It can also create a menu that includes many exercises that the user finds enjoyable. Furthermore, if the user is feeling stressed, it can suggest exercises that have a stress-relieving effect. In this way, by providing a training menu according to the user's emotional state, it is possible to improve the effectiveness of training.

[0082] The training menu creation unit can use the emotion estimation function to monitor the emotions of the user in real time when performing a training menu and provide feedback to elicit positive emotions. For example, if the user is tired, an encouraging message can be displayed. Exercises that the user finds enjoyable can also be suggested. Furthermore, if the user is feeling stressed, exercises that have a relaxing effect can be suggested. In this way, the effectiveness of training can be improved by monitoring the emotions of the user in real time when performing a training menu and providing feedback to elicit positive emotions.

[0083] The training menu creation unit can use the emotion estimation function to set training goals according to the user's emotional state and suggest training menus that will enhance the sense of accomplishment. For example, if the user is feeling positive, a high goal can be set. A goal that can be achieved in a short period of time can also be set. Furthermore, if the user is feeling stressed, a lower goal can also be set. In this way, by setting training goals according to the user's emotional state and providing a menu that will enhance the sense of accomplishment, the effectiveness of training can be improved.

[0084] The data collection unit can use the emotion estimation function to integrate the user's training data and emotional data and analyze the training effect based on emotional changes. For example, it can evaluate the training effect during periods of strong positive emotions. It can also analyze training results during periods of low stress. It can also evaluate the impact of emotional fluctuations on training results. In this way, by integrating the user's training data and emotional data and analyzing the training effect based on emotional changes using the emotion estimation function, it is possible to evaluate the progress of training and provide an optimal training menu.

[0085] The advice providing unit can use the emotion estimation function to analyze the user's emotional state and provide dietary and rest advice to maximize the effectiveness of training. For example, if the user is feeling stressed, it can suggest dietary and resting methods that have a relaxing effect. If the user is feeling positive, it can also suggest dietary and resting methods that will increase energy. Furthermore, it can provide specific dietary and resting advice according to the user's emotional state. In this way, it is possible to maximize the effectiveness of training by providing dietary and resting advice based on the user's emotional state.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The training menu creation unit creates a training menu based on at least one of the user's physical data, goals, and sport. For example, the training menu creation unit receives input information such as the user's age, gender, weight, height, exercise experience, and goals (e.g., improving muscle strength or endurance), and the generation AI analyzes this information to create the optimal training menu for each individual user. Step 2: The instructor provides instruction based on the training menu created by the training menu creation department. For example, the instructor explains the correct form for exercises and important points to note using audio and video. The instructor also provides real-time feedback during training, correcting form and adjusting load. Step 3: The data collection unit collects training data, such as the training status, heart rate, calories burned, and changes in muscle strength. Step 4: The advice providing unit analyzes the training data collected by the data collecting unit and provides advice to the user. For example, the advice providing unit may provide advice on diet and rest to maximize the effect of training, or advice on adjusting the frequency and intensity of training.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0101] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0102] 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.

[0103] 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.

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

[0105] 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.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 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.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The 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.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 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.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 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.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 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.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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]

[0155] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a training menu creation unit that creates a training menu based on at least one piece of information among the user's physical data, goals, and sport; a training unit that provides training based on the training menu created by the training menu creation unit; a data collection unit that collects training data; an advice providing unit that analyzes the training data collected by the data collecting unit and provides advice to the user; A system characterized by:

2. The training menu creation unit Analyzes the user's past training history and dynamically adjusts training menus based on past performance 2. The system of claim 1.

3. The training menu creation unit Analyzes the user's dietary data and suggests training menus based on nutritional balance 2. The system of claim 1.

4. The data collection unit Collecting user training data over a long period of time and analyzing long-term training effects 2. The system of claim 1.

5. The advice providing unit Analyzes user training data and provides dietary or rest advice to maximize training effectiveness 2. The system of claim 1.

6. The training menu creation unit Monitors the user's psychological state in real time and suggests training menus to maintain motivation 2. The system of claim 1.

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