System

Abstract

An object of a system according to an embodiment is to provide effective individual coaching to an athlete or a trainee.SOLUTION: A system according to an embodiment includes a data collection unit, an analysis unit, a feedback unit, and a chatbot unit. The data collection unit collects data of an athlete or a trainee. The analysis unit analyzes the data collected by the data collection unit. The feedback unit provides real-time feedback based on the result analyzed by the analysis unit. The chatbot unit provides the latest training plan and nutrition advice.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology requires athletes and trainees to receive individual instruction at high cost and with time constraints, leaving room for improvement.

[0005] The system according to the embodiment aims to provide effective individual instruction to athletes and trainees. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a feedback unit, and a chatbot unit. The data collection unit collects data on the athlete or trainee. The analysis unit analyzes the data collected by the data collection unit. The feedback unit provides real-time feedback based on the results of the analysis by the analysis unit. The chatbot unit provides the latest training plans and nutritional advice. [Effects of the Invention]

[0007] The system according to the embodiment can provide effective individual instruction to athletes and trainees. [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 AI ​​sports trainer system according to an embodiment of the present invention provides personalized instruction to athletes and trainees, with a generative AI using data analysis and machine learning to provide real-time feedback and up-to-date training plans and nutritional advice via a chatbot. This eliminates the cost and time constraints associated with using personal trainers and experts, enabling more people to receive effective training.

[0029] An AI sports trainer system according to an embodiment includes a data collection unit, an analysis unit, a feedback unit, and a chatbot unit. The data collection unit collects data on athletes or trainees. For example, the data collection unit can collect athletic data and physical data on athletes. The data collection unit can also collect psychological data. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the athletic data using a data analysis algorithm. The analysis unit can also analyze the physical data using a machine learning algorithm. The analysis unit can also analyze the psychological data. The feedback unit provides real-time feedback based on the results of the analysis by the analysis unit. For example, the feedback unit can provide audio feedback. The feedback unit can also provide visual feedback. The feedback unit can also provide text feedback. The chatbot unit provides updated training plans and nutritional advice. For example, the chatbot unit can provide personalized training plans. The chatbot unit can also provide general training guidelines. The chatbot unit can also provide personalized nutritional advice. This allows the AI ​​sports trainer system to provide individualized instruction to athletes and trainees, eliminating cost and time constraints.

[0030] The data collection unit collects the athletic data and physical data of the athlete, and the analysis unit analyzes the athletic data and physical data collected by the data collection unit and can propose an optimal training menu based on the results. The data collection unit, for example, collects the athletic data of the athlete. For example, the data collection unit can collect the athlete's heart rate and number of steps. The data collection unit also collects the athlete's physical data. For example, the data collection unit can collect the athlete's weight and body fat percentage. The analysis unit analyzes the athletic data and physical data collected by the data collection unit. For example, the analysis unit can analyze the athletic data using a data analysis algorithm. The analysis unit can also analyze the physical data using a machine learning algorithm. The analysis unit proposes an optimal training menu based on the analysis results. For example, the analysis unit proposes a training menu for building muscle strength based on the athlete's athletic data and physical data. The analysis unit can also propose a training menu for improving endurance. The analysis unit can also propose a training menu for weight loss. In this way, an optimal training menu can be proposed based on the athlete's athletic data and physical data.

[0031] The chatbot unit can use the generation AI to provide training plans and nutritional advice in response to questions and requests from users. For example, the chatbot unit can use the generation AI to provide training plans in response to questions and requests from users. For example, in response to a question such as, "What training plan should I use to increase muscle strength?", the generation AI can suggest an optimal plan. In response to a question such as, "What nutrients should I take while losing weight?", the generation AI can provide appropriate advice. The generation AI can generate training plans and nutritional advice using, for example, natural language generation technology. The generation AI can also analyze user data using machine learning algorithms to provide personalized plans. The generation AI can also integrate past training data and competitive results to provide plans aimed at long-term performance improvement. This allows the generation AI to provide training plans and nutritional advice in response to user questions and requests.

[0032] The feedback unit can analyze the form and movements during training and point out areas for improvement. The feedback unit analyzes, for example, the form and movements during training. For example, the feedback unit analyzes the form using video analysis technology. The feedback unit can also analyze movements using motion capture technology. The feedback unit points out areas for improvement based on the analysis results. For example, the feedback unit points out the position of the knees and the angle of the back when squatting. The feedback unit can also point out the landing position of the feet and the way the arms are swung when running. The feedback unit can also point out how to hold the barbell and the timing of the lift. In this way, it is possible to point out areas for improvement in the form and movements during training.

[0033] The data collection unit can integrate past training data and competitive results to provide a plan aimed at improving long-term performance. The data collection unit, for example, integrates past training data and competitive results. For example, the data collection unit analyzes past training history and suggests a training menu to improve performance. The data collection unit can also identify an athlete's strengths and weaknesses based on competitive performance data and provide a training plan accordingly. The data collection unit can also analyze past training data and competitive results to provide a step-by-step plan for achieving long-term goals. For example, the data collection unit creates an annual plan and sets goals for each month. In this way, past training data and competitive results can be integrated to provide a plan aimed at improving long-term performance.

[0034] The data collection unit can analyze the lifestyle habit data of the athlete and propose a comprehensive health management plan. The data collection unit, for example, analyzes the lifestyle habit data of the athlete. For example, the data collection unit can analyze the athlete's sleep data and provide advice on optimal sleep duration and how to improve sleep quality. The data collection unit can also analyze the athlete's dietary data and propose a meal plan to optimize nutritional balance. The data collection unit can also monitor the athlete's stress level and propose relaxation techniques and mental training to reduce stress. For example, the data collection unit can introduce meditation and deep breathing techniques. In this way, the athlete's lifestyle habit data can be analyzed and a comprehensive health management plan can be proposed.

[0035] The feedback unit can provide a plan that incorporates mental training and relaxation techniques in addition to the training plan. The feedback unit, for example, incorporates mental training into the training plan. For example, the feedback unit incorporates image training and positive thinking techniques to improve the athlete's concentration and mental toughness. The feedback unit can also incorporate relaxation techniques into the training plan to reduce the athlete's stress. For example, the feedback unit adds yoga or meditation sessions to the training menu. The feedback unit can also provide a comprehensive plan that integrates mental training and relaxation techniques to balance the athlete's mind and body. For example, the feedback unit conducts a relaxation session as a cool-down after training. In this way, a training plan that incorporates mental training and relaxation techniques can be provided.

[0036] The chatbot unit can analyze the user's dietary history and propose a specific meal plan to optimize nutritional balance. The chatbot unit, for example, analyzes the user's dietary history and proposes a specific meal plan to optimize nutritional balance. For example, the chatbot unit provides a meal menu to supplement necessary nutrients based on past dietary data. The chatbot unit can also analyze the dietary history and customize a meal plan according to the user's health condition and training goals. For example, the chatbot unit can recommend a high-protein diet to a user aiming to increase muscle strength. The chatbot unit can also propose a supplement and vitamin intake plan based on the user's dietary history to optimize nutritional balance. For example, the chatbot unit can recommend appropriate supplements if a specific nutrient is lacking. In this way, the chatbot unit can analyze the user's dietary history and propose a specific meal plan to optimize nutritional balance.

[0037] The chatbot unit can provide personalized nutrition advice that takes into account the user's allergy information and food preferences. The chatbot unit can provide personalized nutrition advice that takes into account the user's allergy information and food preferences. For example, the chatbot unit can suggest a meal plan that avoids foods that the user is allergic to. The chatbot unit can also provide nutrition advice that takes into account food preferences so that the user can continue to enjoy their meals. For example, the chatbot unit can suggest recipes using favorite ingredients. The chatbot unit can also customize a meal plan to maintain optimal nutritional balance for the user based on the allergy information and food preferences. For example, the chatbot unit can suggest substitutions for specific ingredients. This makes it possible to provide personalized nutrition advice that takes into account the user's allergy information and food preferences.

[0038] The chatbot unit can provide supplement and vitamin intake plans in addition to nutritional advice. For example, the chatbot unit can provide supplement and vitamin intake plans based on the user's health condition and training goals in addition to nutritional advice. For example, the chatbot unit can recommend protein supplements to a user who is aiming to increase muscle strength. The chatbot unit can also analyze the user's dietary history and suggest supplement and vitamin intake plans to make up for missing nutrients. For example, the chatbot unit can recommend appropriate supplements if the user is deficient in vitamin D. The chatbot unit can also provide supplement and vitamin intake plans to optimize the user's nutritional balance. For example, the chatbot unit can suggest the timing of supplement intake based on the user's training goals. This makes it possible to provide supplement and vitamin intake plans in addition to nutritional advice.

[0039] The chatbot unit can provide nutritional advice based on the eating habits of different cultures and regions, thereby providing global support. The chatbot unit, for example, provides nutritional advice based on the eating habits of different cultures and regions. For example, the chatbot unit provides advice taking into account the eating habits of each region, such as Asia, Europe, and America. The chatbot unit can also provide nutritional advice that incorporates ingredients and dishes from each region and propose meal plans that are familiar to the user. For example, the chatbot unit can introduce recipes using local ingredients. The chatbot unit can also customize meal plans to maintain optimal nutritional balance for the user based on the eating habits of different cultures and regions. For example, the chatbot unit can propose menus that take into account regional nutrients. This allows nutritional advice based on the eating habits of different cultures and regions to be provided globally.

[0040] The feedback unit can analyze the heart rate and breathing rate during training in real time and suggest an optimal exercise intensity. The feedback unit, for example, analyzes the heart rate and breathing rate during training in real time. For example, the feedback unit can monitor the heart rate using a wearable device. The feedback unit can also monitor the breathing rate using a biometric sensor. The feedback unit suggests an optimal exercise intensity based on the analysis results. For example, the feedback unit monitors whether the heart rate is within a target range and adjusts the exercise intensity. The feedback unit can also evaluate the progress of training in real time based on the heart rate and breathing rate data and provide appropriate feedback. For example, the feedback unit recommends taking a break if the heart rate is too high. The feedback unit can also analyze the heart rate and breathing rate in real time and suggest an exercise intensity to maximize the effect of the training. For example, the feedback unit indicates an optimal intensity during interval training. In this way, the heart rate and breathing rate during training can be analyzed in real time and an optimal exercise intensity can be suggested.

[0041] The feedback unit can analyze training progress data and dynamically adjust the plan depending on the degree of goal achievement. The feedback unit, for example, analyzes training progress data. For example, the feedback unit analyzes a training log and evaluates the degree of goal achievement. The feedback unit can also dynamically adjust the plan depending on the degree of goal achievement. For example, the feedback unit increases training intensity when progress toward the goal is lagging. The feedback unit can also adjust the training plan in real time based on the progress data and provide an optimal plan for goal achievement. For example, the feedback unit sets a new goal when good progress toward a specific goal is being made. The feedback unit can also monitor the training progress and provide feedback depending on the degree of goal achievement. For example, the feedback unit displays an encouraging message when the user is close to achieving the goal. This makes it possible to analyze training progress data and dynamically adjust the plan depending on the degree of goal achievement.

[0042] The feedback unit can analyze environmental data (temperature, humidity, weather) in addition to the training data and suggest an optimal training environment. The feedback unit, for example, analyzes environmental data (temperature, humidity, weather) in addition to the training data. For example, the feedback unit collects weather data and evaluates the training environment. The feedback unit can also monitor temperature and humidity using sensor information. The feedback unit suggests an optimal training environment based on the analysis results. For example, the feedback unit recommends indoor training when the temperature is high. The feedback unit can also suggest increasing the frequency of hydration when the humidity is high. The feedback unit can also change the training content when the weather is bad. In this way, environmental data can be analyzed in addition to the training data and an optimal training environment can be suggested.

[0043] The feedback unit can analyze audio data during training and provide audio feedback on areas for improvement in form and movement. The feedback unit, for example, analyzes audio data during training. For example, the feedback unit can analyze the audio data using voice recognition technology. The feedback unit can also identify areas for improvement in movement using a voice analysis algorithm. The feedback unit provides audio feedback based on the analysis results. For example, the feedback unit can analyze breathing sounds while running and provide guidance on appropriate breathing techniques. The feedback unit can also instruct the correct posture when squatting. The feedback unit can also provide audio guidance on how to hold the barbell and the timing of lifts. In this way, the audio data during training can be analyzed and audio feedback on areas for improvement in form and movement can be provided.

[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 AI ​​sports trainer system may further include a virtual reality (VR) unit. The VR unit allows the user to train in a virtual environment. For example, the VR unit may provide a simulation in which the user exercises in a virtual training room. The VR unit may also provide the user with the experience of competing in a virtual stadium. Furthermore, the VR unit may allow the user to train while interacting with a virtual trainer. This allows the user to have a diverse training experience beyond the constraints of reality.

[0046] The data collection unit can further collect environmental data. For example, the data collection unit can collect environmental data such as temperature, humidity, and air pressure at the training location. The analysis unit can analyze this environmental data and evaluate its impact on the effectiveness of training. For example, the analysis unit can adjust the intensity of training when the temperature is high. The analysis unit can also suggest increasing the frequency of hydration when the humidity is high. This makes it possible to provide an optimal training plan that takes environmental data into consideration.

[0047] The chatbot unit can further analyze the user's sleep data and suggest an optimal sleep schedule. For example, the chatbot unit can monitor the user's sleep duration and sleep quality and point out areas for improvement. The chatbot unit can also suggest optimal sleep durations that match the user's training schedule. Furthermore, the chatbot unit can suggest relaxation techniques and ways to improve the sleep environment based on the user's sleep data. This can improve the user's sleep quality and maximize the training effect.

[0048] The feedback unit can further analyze the user's nutritional intake data and propose a meal plan to maximize the training effect. For example, the feedback unit can monitor the user's diet and provide a meal plan to supplement necessary nutrients. The feedback unit can also propose the timing of nutritional intake according to the type and intensity of training. Furthermore, the feedback unit can also provide a supplement and vitamin intake plan based on the user's nutritional intake data. This can optimize the user's nutritional balance and maximize the training effect.

[0049] The data collection unit further collects the user's social activity data, and the analysis unit can adjust the user's training plan based on this data. For example, the data collection unit collects data on the user's interactions with friends and family, and the analysis unit can provide a training plan to increase the user's motivation based on this data. For example, the analysis unit can recommend that the user train with friends. The analysis unit can also customize the training plan based on the user's social activity data. This makes it possible to provide an optimal training plan that takes the user's social activities into consideration.

[0050] The feedback unit can further analyze the user's training history and provide a step-by-step plan for achieving long-term goals. For example, the feedback unit can analyze the user's past training data and suggest a training menu for improving performance. The feedback unit can also create an annual plan and set monthly goals based on the user's training history. Furthermore, the feedback unit can monitor the user's training progress based on the user's training history and adjust the plan as necessary. This can support the user in achieving their long-term goals.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The data collection unit collects data on the athlete or trainee. For example, the data collection unit can collect exercise data, physical data, and psychological data on the athlete. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the exercise data, physical data, and psychological data using a data analysis algorithm or a machine learning algorithm. Step 3: The feedback unit provides real-time feedback based on the results analyzed by the analysis unit. For example, the feedback unit can provide audio feedback, visual feedback, or text feedback. Step 4: The chatbot provides updated training plans and nutrition advice. For example, the chatbot can provide personalized training plans, general training guidelines, and personalized nutrition advice.

[0053] (Example 2) The AI ​​sports trainer system according to an embodiment of the present invention provides personalized instruction to athletes and trainees, with a generative AI using data analysis and machine learning to provide real-time feedback and up-to-date training plans and nutritional advice via a chatbot. This eliminates the cost and time constraints associated with using personal trainers and experts, enabling more people to receive effective training.

[0054] An AI sports trainer system according to an embodiment includes a data collection unit, an analysis unit, a feedback unit, and a chatbot unit. The data collection unit collects data on athletes or trainees. For example, the data collection unit can collect athletic data and physical data on athletes. The data collection unit can also collect psychological data. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the athletic data using a data analysis algorithm. The analysis unit can also analyze the physical data using a machine learning algorithm. The analysis unit can also analyze the psychological data. The feedback unit provides real-time feedback based on the results of the analysis by the analysis unit. For example, the feedback unit can provide audio feedback. The feedback unit can also provide visual feedback. The feedback unit can also provide text feedback. The chatbot unit provides updated training plans and nutritional advice. For example, the chatbot unit can provide personalized training plans. The chatbot unit can also provide general training guidelines. The chatbot unit can also provide personalized nutritional advice. This allows the AI ​​sports trainer system to provide individualized instruction to athletes and trainees, eliminating cost and time constraints.

[0055] The data collection unit collects the athletic data and physical data of the athlete, and the analysis unit analyzes the athletic data and physical data collected by the data collection unit and can propose an optimal training menu based on the results. The data collection unit, for example, collects the athletic data of the athlete. For example, the data collection unit can collect the athlete's heart rate and number of steps. The data collection unit also collects the athlete's physical data. For example, the data collection unit can collect the athlete's weight and body fat percentage. The analysis unit analyzes the athletic data and physical data collected by the data collection unit. For example, the analysis unit can analyze the athletic data using a data analysis algorithm. The analysis unit can also analyze the physical data using a machine learning algorithm. The analysis unit proposes an optimal training menu based on the analysis results. For example, the analysis unit proposes a training menu for building muscle strength based on the athlete's athletic data and physical data. The analysis unit can also propose a training menu for improving endurance. The analysis unit can also propose a training menu for weight loss. In this way, an optimal training menu can be proposed based on the athlete's athletic data and physical data.

[0056] The chatbot unit can use the generation AI to provide training plans and nutritional advice in response to questions and requests from users. For example, the chatbot unit can use the generation AI to provide training plans in response to questions and requests from users. For example, in response to a question such as, "What training plan should I use to increase muscle strength?", the generation AI can suggest an optimal plan. In response to a question such as, "What nutrients should I take while losing weight?", the generation AI can provide appropriate advice. The generation AI can generate training plans and nutritional advice using, for example, natural language generation technology. The generation AI can also analyze user data using machine learning algorithms to provide personalized plans. The generation AI can also integrate past training data and competitive results to provide plans aimed at long-term performance improvement. This allows the generation AI to provide training plans and nutritional advice in response to user questions and requests.

[0057] The feedback unit can analyze the form and movements during training and point out areas for improvement. The feedback unit analyzes, for example, the form and movements during training. For example, the feedback unit analyzes the form using video analysis technology. The feedback unit can also analyze movements using motion capture technology. The feedback unit points out areas for improvement based on the analysis results. For example, the feedback unit points out the position of the knees and the angle of the back when squatting. The feedback unit can also point out the landing position of the feet and the way the arms are swung when running. The feedback unit can also point out how to hold the barbell and the timing of the lift. In this way, it is possible to point out areas for improvement in the form and movements during training.

[0058] The data collection unit can analyze the athlete's psychological state using an emotion estimation function and generate a training plan to increase motivation. The data collection unit, for example, analyzes the athlete's psychological state using the emotion estimation function. For example, the data collection unit analyzes the athlete's facial expressions and voice to estimate stress and fatigue levels. The emotion estimation function can analyze emotions using facial expression recognition technology, for example. The emotion estimation function can also analyze emotions using voice analysis technology. The data collection unit generates a training plan to increase motivation based on the analysis results. For example, the data collection unit adjusts the intensity and content of training based on the athlete's emotional data. The data collection unit can also monitor the athlete's psychological state in real time and provide feedback to increase motivation. The data collection unit can also customize the training plan based on the athlete's emotional data. In this way, the athlete's psychological state can be analyzed and a training plan to increase motivation can be generated.

[0059] The data collection unit can integrate past training data and competitive results to provide a plan aimed at improving long-term performance. The data collection unit, for example, integrates past training data and competitive results. For example, the data collection unit analyzes past training history and suggests a training menu to improve performance. The data collection unit can also identify an athlete's strengths and weaknesses based on competitive performance data and provide a training plan accordingly. The data collection unit can also analyze past training data and competitive results to provide a step-by-step plan for achieving long-term goals. For example, the data collection unit creates an annual plan and sets goals for each month. In this way, past training data and competitive results can be integrated to provide a plan aimed at improving long-term performance.

[0060] The data collection unit can analyze the lifestyle habit data of the athlete and propose a comprehensive health management plan. The data collection unit, for example, analyzes the lifestyle habit data of the athlete. For example, the data collection unit can analyze the athlete's sleep data and provide advice on optimal sleep duration and how to improve sleep quality. The data collection unit can also analyze the athlete's dietary data and propose a meal plan to optimize nutritional balance. The data collection unit can also monitor the athlete's stress level and propose relaxation techniques and mental training to reduce stress. For example, the data collection unit can introduce meditation and deep breathing techniques. In this way, the athlete's lifestyle habit data can be analyzed and a comprehensive health management plan can be proposed.

[0061] The feedback unit can provide a plan that incorporates mental training and relaxation techniques in addition to the training plan. The feedback unit, for example, incorporates mental training into the training plan. For example, the feedback unit incorporates image training and positive thinking techniques to improve the athlete's concentration and mental toughness. The feedback unit can also incorporate relaxation techniques into the training plan to reduce the athlete's stress. For example, the feedback unit adds yoga or meditation sessions to the training menu. The feedback unit can also provide a comprehensive plan that integrates mental training and relaxation techniques to balance the athlete's mind and body. For example, the feedback unit conducts a relaxation session as a cool-down after training. In this way, a training plan that incorporates mental training and relaxation techniques can be provided.

[0062] The feedback unit can use the emotion estimation function to monitor emotional changes during training in real time and provide appropriate feedback. The feedback unit, for example, uses the emotion estimation function to monitor emotional changes during training in real time. For example, the feedback unit analyzes the athlete's facial expressions and voice to detect emotional changes. The feedback unit can also monitor emotional changes during training and provide feedback to maintain motivation. For example, the feedback unit displays an encouraging message if the athlete is tired. The feedback unit can also dynamically adjust the training plan based on the emotion estimation data to provide optimal training according to the athlete's psychological state. For example, the feedback unit adds a relaxation session if stress is high. This makes it possible to monitor emotional changes during training in real time and provide appropriate feedback.

[0063] The chatbot unit can analyze the user's dietary history and propose a specific meal plan to optimize nutritional balance. The chatbot unit, for example, analyzes the user's dietary history and proposes a specific meal plan to optimize nutritional balance. For example, the chatbot unit provides a meal menu to supplement necessary nutrients based on past dietary data. The chatbot unit can also analyze the dietary history and customize a meal plan according to the user's health condition and training goals. For example, the chatbot unit can recommend a high-protein diet to a user aiming to increase muscle strength. The chatbot unit can also propose a supplement and vitamin intake plan based on the user's dietary history to optimize nutritional balance. For example, the chatbot unit can recommend appropriate supplements if a specific nutrient is lacking. In this way, the chatbot unit can analyze the user's dietary history and propose a specific meal plan to optimize nutritional balance.

[0064] The chatbot unit can provide personalized nutrition advice that takes into account the user's allergy information and food preferences. The chatbot unit can provide personalized nutrition advice that takes into account the user's allergy information and food preferences. For example, the chatbot unit can suggest a meal plan that avoids foods that the user is allergic to. The chatbot unit can also provide nutrition advice that takes into account food preferences so that the user can continue to enjoy their meals. For example, the chatbot unit can suggest recipes using favorite ingredients. The chatbot unit can also customize a meal plan to maintain optimal nutritional balance for the user based on the allergy information and food preferences. For example, the chatbot unit can suggest substitutions for specific ingredients. This makes it possible to provide personalized nutrition advice that takes into account the user's allergy information and food preferences.

[0065] The chatbot unit can use the emotion estimation function to analyze the user's emotions regarding the meal and make suggestions to increase meal satisfaction. The chatbot unit, for example, uses the emotion estimation function to analyze the user's emotions regarding the meal. For example, the chatbot unit analyzes facial expressions and voices during the meal to evaluate satisfaction. The chatbot unit can also customize a meal plan to increase satisfaction based on the user's emotion data regarding the meal. For example, the chatbot unit can suggest a menu that includes many ingredients that evoke strong positive emotions. The chatbot unit can also use the emotion estimation data to monitor changes in the user's emotions regarding the meal and provide feedback in real time. For example, the chatbot unit can provide advice to elicit positive emotions during the meal. This makes it possible to analyze the user's emotions regarding the meal and make suggestions to increase meal satisfaction.

[0066] The chatbot unit can provide supplement and vitamin intake plans in addition to nutritional advice. For example, the chatbot unit can provide supplement and vitamin intake plans based on the user's health condition and training goals in addition to nutritional advice. For example, the chatbot unit can recommend protein supplements to a user who is aiming to increase muscle strength. The chatbot unit can also analyze the user's dietary history and suggest supplement and vitamin intake plans to make up for missing nutrients. For example, the chatbot unit can recommend appropriate supplements if the user is deficient in vitamin D. The chatbot unit can also provide supplement and vitamin intake plans to optimize the user's nutritional balance. For example, the chatbot unit can suggest the timing of supplement intake based on the user's training goals. This makes it possible to provide supplement and vitamin intake plans in addition to nutritional advice.

[0067] The chatbot unit can provide nutritional advice based on the eating habits of different cultures and regions, thereby providing global support. The chatbot unit, for example, provides nutritional advice based on the eating habits of different cultures and regions. For example, the chatbot unit provides advice taking into account the eating habits of each region, such as Asia, Europe, and America. The chatbot unit can also provide nutritional advice that incorporates ingredients and dishes from each region and propose meal plans that are familiar to the user. For example, the chatbot unit can introduce recipes using local ingredients. The chatbot unit can also customize meal plans to maintain optimal nutritional balance for the user based on the eating habits of different cultures and regions. For example, the chatbot unit can propose menus that take into account regional nutrients. This allows nutritional advice based on the eating habits of different cultures and regions to be provided globally.

[0068] The chatbot unit can use the emotion estimation function to analyze the user's emotions regarding training and provide advice to maintain motivation. The chatbot unit, for example, uses the emotion estimation function to analyze the user's emotions regarding training. For example, the chatbot unit analyzes facial expressions and voice during training to detect changes in emotions. The chatbot unit can also provide feedback to maintain motivation based on the user's emotion data regarding training. For example, the chatbot unit recommends training menus that evoke strong positive emotions. The chatbot unit can also use the emotion estimation data to monitor changes in the user's emotions regarding training and provide feedback in real time. For example, the chatbot unit displays encouraging messages during training. This allows the chatbot unit to analyze the user's emotions regarding training and provide advice to maintain motivation.

[0069] The feedback unit can analyze the heart rate and breathing rate during training in real time and suggest an optimal exercise intensity. The feedback unit, for example, analyzes the heart rate and breathing rate during training in real time. For example, the feedback unit can monitor the heart rate using a wearable device. The feedback unit can also monitor the breathing rate using a biometric sensor. The feedback unit suggests an optimal exercise intensity based on the analysis results. For example, the feedback unit monitors whether the heart rate is within a target range and adjusts the exercise intensity. The feedback unit can also evaluate the progress of training in real time based on the heart rate and breathing rate data and provide appropriate feedback. For example, the feedback unit recommends taking a break if the heart rate is too high. The feedback unit can also analyze the heart rate and breathing rate in real time and suggest an exercise intensity to maximize the effect of the training. For example, the feedback unit indicates an optimal intensity during interval training. In this way, the heart rate and breathing rate during training can be analyzed in real time and an optimal exercise intensity can be suggested.

[0070] The feedback unit can analyze training progress data and dynamically adjust the plan depending on the degree of goal achievement. The feedback unit, for example, analyzes training progress data. For example, the feedback unit analyzes a training log and evaluates the degree of goal achievement. The feedback unit can also dynamically adjust the plan depending on the degree of goal achievement. For example, the feedback unit increases training intensity when progress toward the goal is lagging. The feedback unit can also adjust the training plan in real time based on the progress data and provide an optimal plan for goal achievement. For example, the feedback unit sets a new goal when good progress toward a specific goal is being made. The feedback unit can also monitor the training progress and provide feedback depending on the degree of goal achievement. For example, the feedback unit displays an encouraging message when the user is close to achieving the goal. This makes it possible to analyze training progress data and dynamically adjust the plan depending on the degree of goal achievement.

[0071] The feedback unit can analyze environmental data (temperature, humidity, weather) in addition to the training data and suggest an optimal training environment. The feedback unit, for example, analyzes environmental data (temperature, humidity, weather) in addition to the training data. For example, the feedback unit collects weather data and evaluates the training environment. The feedback unit can also monitor temperature and humidity using sensor information. The feedback unit suggests an optimal training environment based on the analysis results. For example, the feedback unit recommends indoor training when the temperature is high. The feedback unit can also suggest increasing the frequency of hydration when the humidity is high. The feedback unit can also change the training content when the weather is bad. In this way, environmental data can be analyzed in addition to the training data and an optimal training environment can be suggested.

[0072] The feedback unit can analyze audio data during training and provide audio feedback on areas for improvement in form and movement. The feedback unit, for example, analyzes audio data during training. For example, the feedback unit can analyze the audio data using voice recognition technology. The feedback unit can also identify areas for improvement in movement using a voice analysis algorithm. The feedback unit provides audio feedback based on the analysis results. For example, the feedback unit can analyze breathing sounds while running and provide guidance on appropriate breathing techniques. The feedback unit can also instruct the correct posture when squatting. The feedback unit can also provide audio guidance on how to hold the barbell and the timing of lifts. In this way, the audio data during training can be analyzed and audio feedback on areas for improvement in form and movement can be provided.

[0073] The feedback unit can use the emotion estimation function to analyze changes in emotions after training and reflect them in the next training plan. The feedback unit, for example, uses the emotion estimation function to analyze changes in emotions after training. For example, the feedback unit analyzes facial expressions and voice after training and evaluates changes in emotions. The feedback unit can also customize the next training plan based on the emotion data after training. For example, the feedback unit prioritizes incorporating training menus that evoke strong positive emotions. The feedback unit can also use the emotion estimation data to monitor changes in emotions after training and adjust the next training plan. For example, the feedback unit changes the training content if strong negative emotions are felt. In this way, changes in emotions after training can be analyzed and reflected in the next training plan.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] The AI ​​sports trainer system may further include a virtual reality (VR) unit. The VR unit allows the user to train in a virtual environment. For example, the VR unit may provide a simulation in which the user exercises in a virtual training room. The VR unit may also provide the user with the experience of competing in a virtual stadium. Furthermore, the VR unit may allow the user to train while interacting with a virtual trainer. This allows the user to have a diverse training experience beyond the constraints of reality.

[0076] The data collection unit can further collect environmental data. For example, the data collection unit can collect environmental data such as temperature, humidity, and air pressure at the training location. The analysis unit can analyze this environmental data and evaluate its impact on the effectiveness of training. For example, the analysis unit can adjust the intensity of training when the temperature is high. The analysis unit can also suggest increasing the frequency of hydration when the humidity is high. This makes it possible to provide an optimal training plan that takes environmental data into consideration.

[0077] The chatbot unit can further analyze the user's sleep data and suggest an optimal sleep schedule. For example, the chatbot unit can monitor the user's sleep duration and sleep quality and point out areas for improvement. The chatbot unit can also suggest optimal sleep durations that match the user's training schedule. Furthermore, the chatbot unit can suggest relaxation techniques and ways to improve the sleep environment based on the user's sleep data. This can improve the user's sleep quality and maximize the training effect.

[0078] The feedback unit can further analyze the user's nutritional intake data and propose a meal plan to maximize the training effect. For example, the feedback unit can monitor the user's diet and provide a meal plan to supplement necessary nutrients. The feedback unit can also propose the timing of nutritional intake according to the type and intensity of training. Furthermore, the feedback unit can also provide a supplement and vitamin intake plan based on the user's nutritional intake data. This can optimize the user's nutritional balance and maximize the training effect.

[0079] The data collection unit further collects emotional data of the user, and the analysis unit can provide feedback to increase the user's motivation based on this data. For example, the data collection unit can analyze the user's facial expressions and voice to detect changes in emotions. The analysis unit can provide feedback to the user based on the emotional data to encourage them to have positive feelings about training. For example, the analysis unit can display an encouraging message when the user is tired. The analysis unit can also dynamically adjust the training plan based on the emotional data of the user. This makes it possible to provide an optimal training plan that takes the user's emotions into consideration.

[0080] The data collection unit can further monitor the user's stress level, and the analysis unit can provide a training plan to reduce stress based on the data. For example, the data collection unit can monitor the user's heart rate and electrodermal activity to evaluate the user's stress level. If the stress level is high, the analysis unit can suggest a training plan that incorporates relaxation techniques. For example, the analysis unit can incorporate yoga or meditation sessions into the training plan. The analysis unit can also monitor the user's stress level in real time and adjust the training plan as needed. This can reduce the user's stress and maximize the training effect.

[0081] The data collection unit further collects the user's social activity data, and the analysis unit can adjust the user's training plan based on this data. For example, the data collection unit collects data on the user's interactions with friends and family, and the analysis unit can provide a training plan to increase the user's motivation based on this data. For example, the analysis unit can recommend that the user train with friends. The analysis unit can also customize the training plan based on the user's social activity data. This makes it possible to provide an optimal training plan that takes the user's social activities into consideration.

[0082] The feedback unit can further analyze the user's training history and provide a step-by-step plan for achieving long-term goals. For example, the feedback unit can analyze the user's past training data and suggest a training menu for improving performance. The feedback unit can also create an annual plan and set monthly goals based on the user's training history. Furthermore, the feedback unit can monitor the user's training progress based on the user's training history and adjust the plan as necessary. This can support the user in achieving their long-term goals.

[0083] The feedback unit can also dynamically adjust the music during training based on the user's emotional data. For example, the feedback unit can analyze the user's emotional data and select music to increase motivation. For example, if the user is tired, energetic music can be played. Alternatively, if the user wants to relax, calm music can be played. Furthermore, the feedback unit can dynamically adjust the music according to the progress of training based on the user's emotional data. This makes it possible to provide optimal music that takes the user's emotions into consideration and maximize the training effect.

[0084] The feedback unit can further provide a post-training recovery plan based on the user's emotional data. For example, the feedback unit can analyze the user's emotional data and suggest optimal methods for recovery. For example, if the user feels fatigued, the feedback unit can suggest stretching or massage techniques. Also, if the user feels stressed, the feedback unit can provide a recovery plan that incorporates relaxation techniques. Furthermore, the feedback unit can monitor the progress of recovery based on the user's emotional data and adjust the plan as necessary. This makes it possible to provide an optimal recovery plan that takes the user's emotions into consideration.

[0085] The processing flow of the second embodiment will be briefly explained below.

[0086] Step 1: The data collection unit collects data on the athlete or trainee. For example, the data collection unit can collect exercise data, physical data, and psychological data on the athlete. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the exercise data, physical data, and psychological data using a data analysis algorithm or a machine learning algorithm. Step 3: The feedback unit provides real-time feedback based on the results analyzed by the analysis unit. For example, the feedback unit can provide audio feedback, visual feedback, or text feedback. Step 4: The chatbot provides updated training plans and nutrition advice. For example, the chatbot can provide personalized training plans, general training guidelines, and personalized nutrition advice.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0121] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the 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.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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."

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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]

[0154] 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 data collection unit that collects data on athletes or trainees; an analysis unit that analyzes the data collected by the data collection unit; a feedback unit that provides real-time feedback based on the results of the analysis by the analysis unit; It also features a chatbot that provides the latest training plans and nutrition advice. A system characterized by:

2. The chatbot unit Using generative AI to provide the training plan and nutrition advice in response to user questions and requests.

2. The system of claim 1.

3. The feedback unit In addition to the training plan, we also provide plans that incorporate mental training and relaxation techniques.

2. The system of claim 1.

4. The chatbot unit Analyzes the user's dietary history and proposes specific meal plans to optimize nutritional balance 2. The system of claim 1.

5. The feedback unit Analyzes heart rate and breathing rate in real time during training and suggests optimal exercise intensity 2. The system of claim 1.

6. The data collection unit Analyzing the athlete's mental state using an emotion estimation function and generating the training plan to increase motivation 2. The system of claim 1.

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