System

Abstract

An object of a system according to an embodiment is to propose a training method optimal for an individual user.SOLUTION: A system according to an embodiment includes a user information input unit, a successful case analysis unit, and a training suggestion unit. The user information input unit inputs a sport, an age, a physique, a position, and a target figure of the user. The successful case analysis unit analyzes a successful case based on the information input by the user information input unit. The training proposal unit proposes an optimum training method based on the successful case analyzed by the successful case analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional techniques, it is difficult to provide an optimal training method for each individual user, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal training method for each individual user. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information input unit, a success story analysis unit, and a training suggestion unit. The user information input unit inputs the user's sport, age, physique, position, and desired appearance. The success story analysis unit analyzes success stories based on the information input by the user information input unit. The training suggestion unit suggests an optimal training method based on the success stories analyzed by the success story analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal training method for each individual user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A training suggestion system according to an embodiment of the present invention allows a user to input the sport they are currently playing, their age, physique, position, and desired performance, and then AI provides optimal training methods and advice based on success stories. This allows the training suggestion system to suggest optimal training methods based on the user's individual information, enabling efficient improvement of sports skills.

[0029] A training suggestion system according to an embodiment includes a user information input unit, a success story analysis unit, and a training suggestion unit. The user information input unit inputs the user's sport, age, physique, position, and desired appearance. For example, the user information input unit selects the sport the user is playing and provides an interface for inputting the user's age, physique, position, and desired appearance. The user information input unit also transmits the input information as a prompt to the AI. The success story analysis unit analyzes success stories based on the information input by the user information input unit. For example, the success story analysis unit references a database of past success stories to identify the success story that best suits the user's information. The success story analysis unit also uses AI to analyze the success stories and select the optimal training method for the user. The training suggestion unit suggests an optimal training method based on the success stories analyzed by the success story analysis unit. For example, the training suggestion unit suggests a training method based on the user's age and physique, and provides appropriate instruction while minimizing the risk of injury. The training suggestion unit also presents specific training methods and supports the user in practicing them. As a result, the training suggestion system according to the embodiment can suggest an optimal training method based on the user's individual information, thereby enabling efficient improvement of sports skills.

[0030] The user information input unit collects the user's past training data and performance data, and the AI ​​can optimize individual training plans based on that data. The user information input unit, for example, records the content and results of the user's past training, and the AI ​​analyzes that data. For example, past training logs and performance data are input, and the AI ​​proposes an optimal training plan based on that data. The user information input unit also collects the user's past training data, and the AI ​​evaluates the user's training progress based on that data. For example, the current training plan is adjusted based on past training results. The user information input unit also collects the user's performance data, and the AI ​​evaluates the effectiveness of the training based on that data. For example, a plan to maximize the effectiveness of training is proposed based on past performance data. This makes it possible to optimize training plans based on past data.

[0031] The user information input unit allows the user to input the user's psychological state and motivation level, and can adjust the training method based on that. For example, the user inputs the user's psychological state and motivation level before training, and the AI ​​adjusts the training plan based on that data. For example, if motivation is low, it suggests lighter training. The user information input unit also allows the user to periodically input the user's psychological state, and the AI ​​adjusts the difficulty and content of the training based on that data. For example, if stress is high, it suggests training that has a relaxing effect. The user information input unit also allows the user to input the user's motivation level, and the AI ​​adjusts the frequency and intensity of training based on that data. For example, if motivation is high, it suggests challenging training. This makes it possible to provide a training method that suits the user's psychological state and motivation.

[0032] The user information input unit can provide a more intuitive interface by introducing voice input and gesture input. The user information input unit, for example, allows the user to input information using voice input, providing a more intuitive interface. For example, the user inputs the type of sport or the desired form by voice. The user information input unit can also introduce gesture input, allowing the user to input information intuitively. For example, the user selects the type and intensity of training with hand movements. The user information input unit can also combine voice input and gesture input to provide an interface that allows the user to input information more intuitively. For example, the user gives instructions by voice and selects details with gestures. This makes it possible to provide an intuitive interface.

[0033] The user information input unit can integrate data from different sports and fitness activities to suggest cross-sport training. The user information input unit, for example, integrates data from different sports to suggest cross-sport training. For example, data from baseball and soccer can be integrated to suggest training methods that are effective for both sports. The user information input unit also integrates data from fitness activities to suggest cross-sport training. For example, data from running and strength training can be integrated to suggest a comprehensive training plan. The user information input unit also integrates data from different sports and fitness activities to suggest cross-sport training that is optimal for the user. For example, data from swimming and basketball can be integrated to suggest training methods that are effective for both sports. This makes it possible to suggest cross-sport training.

[0034] The success case analysis unit can propose a more personalized training method by taking into account the user's genetic information and physiological data. For example, the success case analysis unit analyzes the user's genetic information, and the AI ​​proposes a personalized training method based on that data. For example, for a user who has genetically strong muscles, the AI ​​may propose a training method that focuses on strength training. The success case analysis unit also collects the user's physiological data, and the AI ​​proposes a personalized training method based on that data. For example, the AI ​​may propose an appropriate training intensity based on heart rate and blood pressure data. The success case analysis unit also integrates the genetic information and physiological data, and the AI ​​proposes a more personalized training method. For example, for a user who has genetically high endurance, the AI ​​may propose a training method that focuses on endurance training. This makes it possible to provide a personalized training method based on the genetic information and physiological data.

[0035] The success case analysis unit updates the database of success cases in real time, so that it can always reflect the latest training methods and techniques. The success case analysis unit, for example, builds a system that updates the database of success cases in real time to reflect the latest training methods and techniques. For example, it updates the database every time a new success case is added. The success case analysis unit also periodically updates the database of success cases to always reflect the latest training methods and techniques. For example, it adds new training methods to the database every month. The success case analysis unit also updates the database of success cases in real time to provide users with the latest training methods and techniques. For example, it updates the database every time a new training technique is announced. This makes it possible to always reflect the latest training methods and techniques.

[0036] The success case analysis unit can propose training methods from a global perspective, including examples from different cultural spheres and regions. The success case analysis unit, for example, adds success cases from different cultural spheres and regions to a database and proposes training methods from a global perspective. For example, it proposes training methods based on success cases from America and Europe. In addition, the success case analysis unit takes into account examples from different cultural spheres and regions in analyzing success cases and proposes the optimal training method for the user. For example, it proposes training methods based on success cases from Asia and Africa. In addition, the success case analysis unit analyzes success cases from different cultural spheres and regions in order to propose training methods from a global perspective. For example, it proposes training methods based on success cases from South America and Oceania. This makes it possible to provide training methods from a global perspective.

[0037] The success case analysis unit visualizes the analysis results of the success cases and provides them to the user, thereby presenting the training method in a form that is visually easy to understand. The success case analysis unit, for example, builds a system that visualizes the analysis results of the success cases and provides them to the user. For example, the training method is visually displayed using graphs or charts. The success case analysis unit also visualizes the analysis results of the success cases and presents the training method in a form that is visually easy to understand for the user. For example, the training method is explained using infographics. The success case analysis unit also provides the visualized analysis results of the success cases to the user and presents the training method in a form that is visually easy to understand. For example, the training method is explained using videos or animations. This makes it possible to provide the training method in a form that is visually easy to understand.

[0038] The training suggestion unit can feed back the user's real-time performance data and dynamically adjust the training content. The training suggestion unit, for example, collects the user's real-time performance data and builds a system that dynamically adjusts the training content based on that data. For example, it monitors the user's heart rate and exercise intensity in real time and adjusts the training content. The training suggestion unit also develops a system that feeds back the real-time performance data and dynamically adjusts the training content. For example, it adjusts the training intensity and frequency based on the user's performance data. The training suggestion unit also builds a system that feeds back the user's real-time performance data and dynamically adjusts the training content. For example, it updates the training plan in real time based on the user's exercise data. This allows the training content to be dynamically adjusted based on the real-time performance data.

[0039] The training suggestion unit can provide comprehensive health management by taking into account the user's diet and nutritional information. The training suggestion unit, for example, builds a system that collects the user's diet and nutritional information and proposes a training method based on that data. For example, it adjusts a training plan based on the user's dietary content and nutritional intake. The training suggestion unit also develops a system that considers dietary and nutritional information and proposes a training method for comprehensive health management. For example, it adjusts the intensity and content of training based on the user's nutritional status. The training suggestion unit also collects the user's dietary and nutritional information and proposes a training method for comprehensive health management based on that data. For example, it adjusts a training plan based on the user's dietary content and nutritional intake. This allows comprehensive health management to be provided based on dietary and nutritional information.

[0040] The training suggestion unit can provide a more practical training environment using virtual reality (VR) or augmented reality (AR). For example, the training suggestion unit uses VR as a concrete example of a training method, allowing the user to perform training in a similar environment to an actual sports environment. For example, VR goggles are used to simulate an actual match. The training suggestion unit also uses AR to provide a concrete example of a training method, allowing the user to perform practical training. For example, an AR app is used to check the training movements in real time. The training suggestion unit also combines VR and AR to provide a concrete example of a training method, allowing the user to perform more practical training. For example, a match simulation is performed using VR, and the training movements are checked using AR. In this way, a practical training environment can be provided using VR and AR.

[0041] The training suggestion unit can combine training elements of different sports to enhance the effectiveness of cross-training. For example, the training suggestion unit combines training elements of different sports to propose a training method that enhances the effectiveness of cross-training. For example, it combines training elements of soccer and basketball. The training suggestion unit also combines training elements of different sports to propose an optimal cross-training method for the user. For example, it combines training elements of swimming and running. The training suggestion unit also proposes a training method that combines training elements of different sports to enhance the effectiveness of cross-training. For example, it combines training elements of tennis and volleyball. In this way, the effectiveness of cross-training can be enhanced by combining training elements of different sports.

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

[0043] The user information input unit can also input the user's health condition and medical history, and adjust the training method based on that. For example, the user can input the parts of the body that have previously been injured or any chronic illnesses, and the AI ​​can adjust the training plan based on that information. For example, for a user with knee problems, it can suggest a training method that does not put strain on the knee. The user information input unit also requires the user to periodically input their health condition, and the AI ​​can adjust the difficulty and content of the training based on that data. For example, for a user with heart problems, it can suggest safe training while monitoring their heart rate. The user information input unit also requires the user to input their health condition, and the AI ​​can adjust the frequency and intensity of training based on that data. For example, if the user is not feeling well, it can suggest lighter training. This makes it possible to provide training methods that suit the user's health condition.

[0044] The user information input unit can also input the user's lifestyle habits and daily activity level, and adjust the training method based on that. For example, the user can input how much exercise they do daily, and the AI ​​can adjust the training plan based on that information. For example, for users who do a lot of desk work, it can suggest stretching or light exercise. The user information input unit also periodically inputs the user's lifestyle habits, and the AI ​​can adjust the difficulty and content of the training based on that data. For example, for users who often work night shifts, it can suggest a training method that takes fatigue into consideration. The user information input unit also inputs the user's activity level, and the AI ​​can adjust the frequency and intensity of the training based on that data. For example, it can suggest challenging training for users who are usually active. This makes it possible to provide training methods that are suited to the user's lifestyle habits and activity level.

[0045] The user information input unit can also prompt the user to input their hobbies and interests, and suggest training methods based on that. For example, the user inputs their favorite sports and activities, and the AI ​​suggests training plans based on that information. For example, for a user who likes dancing, it suggests training methods that incorporate dance. The user information input unit also prompts the user to periodically input their hobbies and interests, and the AI ​​adjusts the content of the training based on that data. For example, for a user who likes the outdoors, it suggests training methods that can be done outdoors. The user information input unit also prompts the user to input their interests, and the AI ​​adjusts the frequency and intensity of the training based on that data. For example, for a user who likes music, it suggests training methods that incorporate music. This makes it possible to provide training methods that suit users' hobbies and interests.

[0046] The training suggestion unit can also adjust the training method taking into account the user's sleep data. For example, the AI ​​can monitor the user's sleep time and sleep quality and adjust the training plan based on that data. For example, it can suggest lighter training to a user who is sleep-deprived. The training suggestion unit also periodically collects the user's sleep data and adjusts the difficulty and content of the training based on that data. For example, it can suggest a more challenging training method to a user who sleeps well. The training suggestion unit can also adjust the frequency and intensity of training taking into account the user's sleep data. For example, it can suggest a training method that has a relaxing effect to a user who sleeps poorly. This makes it possible to provide training methods based on sleep data.

[0047] The training suggestion unit can also adjust the training method taking into account the user's stress level. For example, the AI ​​monitors the user's stress level and adjusts the training plan based on that data. For example, for a user with high stress, it suggests a training method that has a relaxing effect. The training suggestion unit also periodically collects the user's stress level and adjusts the difficulty and content of the training based on that data. For example, it suggests a challenging training method for a user with low stress. The training suggestion unit also adjusts the frequency and intensity of the training taking into account the user's stress level. For example, it suggests a balanced training method for a user with moderate stress. This makes it possible to provide training methods based on stress levels.

[0048] The training suggestion unit can also suggest a training method taking into account the user's sociability and communication style. For example, if the user is sociable, it will suggest group training. The training suggestion unit also periodically collects the user's communication style and adjusts the content of the training based on that data. For example, if the user prefers training alone, it will suggest individual training. The training suggestion unit also adjusts the frequency and intensity of training taking into account the user's sociability. For example, if the user is sociable, it will suggest a training method to do with other users. This makes it possible to provide a training method that suits the user's sociability and communication style.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The user information input unit inputs the user's sport, age, physique, position, and desired appearance. For example, the user information input unit provides an interface for selecting the sport the user is playing and inputting their age, physique, position, and desired appearance. The user information input unit also sends the input information as a prompt to the AI. Step 2: The success story analysis unit analyzes success stories based on the information entered by the user information input unit. For example, the success story analysis unit refers to a database of past success stories and identifies the success story that best suits the user's information. The success story analysis unit also uses AI to analyze the success stories and select the optimal training method for the user. Step 3: The training suggestion unit proposes the optimal training method based on the success cases analyzed by the success case analysis unit. For example, the training suggestion unit proposes a training method based on the user's age and physique, and provides appropriate instruction while minimizing the risk of injury. The training suggestion unit also presents specific training methods and supports the user in putting them into practice.

[0051] (Example 2) A training suggestion system according to an embodiment of the present invention allows a user to input the sport they are currently playing, their age, physique, position, and desired performance, and then AI provides optimal training methods and advice based on success stories. This allows the training suggestion system to suggest optimal training methods based on the user's individual information, enabling efficient improvement of sports skills.

[0052] A training suggestion system according to an embodiment includes a user information input unit, a success story analysis unit, and a training suggestion unit. The user information input unit inputs the user's sport, age, physique, position, and desired appearance. For example, the user information input unit selects the sport the user is playing and provides an interface for inputting the user's age, physique, position, and desired appearance. The user information input unit also transmits the input information as a prompt to the AI. The success story analysis unit analyzes success stories based on the information input by the user information input unit. For example, the success story analysis unit references a database of past success stories to identify the success story that best suits the user's information. The success story analysis unit also uses AI to analyze the success stories and select the optimal training method for the user. The training suggestion unit suggests an optimal training method based on the success stories analyzed by the success story analysis unit. For example, the training suggestion unit suggests a training method based on the user's age and physique, and provides appropriate instruction while minimizing the risk of injury. The training suggestion unit also presents specific training methods and supports the user in practicing them. As a result, the training suggestion system according to the embodiment can suggest an optimal training method based on the user's individual information, thereby enabling efficient improvement of sports skills.

[0053] The user information input unit collects the user's past training data and performance data, and the AI ​​can optimize individual training plans based on that data. The user information input unit, for example, records the content and results of the user's past training, and the AI ​​analyzes that data. For example, past training logs and performance data are input, and the AI ​​proposes an optimal training plan based on that data. The user information input unit also collects the user's past training data, and the AI ​​evaluates the user's training progress based on that data. For example, the current training plan is adjusted based on past training results. The user information input unit also collects the user's performance data, and the AI ​​evaluates the effectiveness of the training based on that data. For example, a plan to maximize the effectiveness of training is proposed based on past performance data. This makes it possible to optimize training plans based on past data.

[0054] The user information input unit allows the user to input the user's psychological state and motivation level, and can adjust the training method based on that. For example, the user inputs the user's psychological state and motivation level before training, and the AI ​​adjusts the training plan based on that data. For example, if motivation is low, it suggests lighter training. The user information input unit also allows the user to periodically input the user's psychological state, and the AI ​​adjusts the difficulty and content of the training based on that data. For example, if stress is high, it suggests training that has a relaxing effect. The user information input unit also allows the user to input the user's motivation level, and the AI ​​adjusts the frequency and intensity of training based on that data. For example, if motivation is high, it suggests challenging training. This makes it possible to provide a training method that suits the user's psychological state and motivation.

[0055] The user information input unit can use the emotion estimation function to analyze the emotion of the user when entering information in real time and provide an interface for eliciting positive emotions. For example, the user information input unit can use the emotion estimation function to analyze the emotion of the user when entering information in real time and display a message for eliciting positive emotions. For example, if the user is feeling anxious, an encouraging message can be displayed. The user information input unit can also use the emotion estimation function to analyze the emotion of the user when entering information and provide an interface for eliciting positive emotions. For example, an interface design that makes the user feel joy can be adopted. The user information input unit can also use the emotion estimation function to analyze the emotion of the user when entering information in real time and provide feedback for eliciting positive emotions. For example, the user's motivation can be increased by receiving positive feedback on the content of their input. This makes it possible to provide an interface that corresponds to the user's emotions.

[0056] The user information input unit can provide a more intuitive interface by introducing voice input and gesture input. The user information input unit, for example, allows the user to input information using voice input, providing a more intuitive interface. For example, the user inputs the type of sport or the desired form by voice. The user information input unit can also introduce gesture input, allowing the user to input information intuitively. For example, the user selects the type and intensity of training with hand movements. The user information input unit can also combine voice input and gesture input to provide an interface that allows the user to input information more intuitively. For example, the user gives instructions by voice and selects details with gestures. This makes it possible to provide an intuitive interface.

[0057] The user information input unit can integrate data from different sports and fitness activities to suggest cross-sport training. The user information input unit, for example, integrates data from different sports to suggest cross-sport training. For example, data from baseball and soccer can be integrated to suggest training methods that are effective for both sports. The user information input unit also integrates data from fitness activities to suggest cross-sport training. For example, data from running and strength training can be integrated to suggest a comprehensive training plan. The user information input unit also integrates data from different sports and fitness activities to suggest cross-sport training that is optimal for the user. For example, data from swimming and basketball can be integrated to suggest training methods that are effective for both sports. This makes it possible to suggest cross-sport training.

[0058] The user information input unit can use the emotion estimation function to analyze the emotion of the user when entering information and display a message for improving motivation according to the content of the entry. For example, when the user enters information, the user information input unit can use the emotion estimation function to analyze the emotion and display a message for improving motivation. For example, if the user is tired, an encouraging message can be displayed. The user information input unit can also use the emotion estimation function to analyze the emotion of the user when entering information and provide feedback for improving motivation according to the content of the entry. For example, if the user is feeling anxious, a message that gives a sense of security can be displayed. The user information input unit can also use the emotion estimation function to analyze the emotion in real time when the user enters information and provide advice for improving motivation. For example, the user can be motivated by receiving positive feedback on the content of the entry. This makes it possible to provide a message for improving motivation according to the user's emotion.

[0059] The success case analysis unit can propose a more personalized training method by taking into account the user's genetic information and physiological data. For example, the success case analysis unit analyzes the user's genetic information, and the AI ​​proposes a personalized training method based on that data. For example, for a user who has genetically strong muscles, the AI ​​may propose a training method that focuses on strength training. The success case analysis unit also collects the user's physiological data, and the AI ​​proposes a personalized training method based on that data. For example, the AI ​​may propose an appropriate training intensity based on heart rate and blood pressure data. The success case analysis unit also integrates the genetic information and physiological data, and the AI ​​proposes a more personalized training method. For example, for a user who has genetically high endurance, the AI ​​may propose a training method that focuses on endurance training. This makes it possible to provide a personalized training method based on the genetic information and physiological data.

[0060] The success case analysis unit updates the database of success cases in real time, so that it can always reflect the latest training methods and techniques. The success case analysis unit, for example, builds a system that updates the database of success cases in real time to reflect the latest training methods and techniques. For example, it updates the database every time a new success case is added. The success case analysis unit also periodically updates the database of success cases to always reflect the latest training methods and techniques. For example, it adds new training methods to the database every month. The success case analysis unit also updates the database of success cases in real time to provide users with the latest training methods and techniques. For example, it updates the database every time a new training technique is announced. This makes it possible to always reflect the latest training methods and techniques.

[0061] The success case analysis unit can use the emotion estimation function to preferentially suggest a case that the user can most easily empathize with from among the success cases. For example, the success case analysis unit uses the emotion estimation function to identify a case that the user can most easily empathize with from among the success cases and preferentially suggest it. For example, it proposes a training method based on a success case that the user can most easily empathize with. Furthermore, the success case analysis unit analyzes the user's emotional response and builds a system that preferentially suggests a success case that the user can most easily empathize with. For example, it proposes a success case that the user feels positive emotions about. Furthermore, the success case analysis unit uses the emotion estimation function to identify a success case that the user can most easily empathize with and proposes a training method based on that case. For example, it creates a training plan based on a success case that the user emotionally empathizes with. This makes it possible to preferentially suggest success cases that the user can most easily empathize with.

[0062] The success case analysis unit can propose training methods from a global perspective, including examples from different cultural spheres and regions. The success case analysis unit, for example, adds success cases from different cultural spheres and regions to a database and proposes training methods from a global perspective. For example, it proposes training methods based on success cases from America and Europe. In addition, the success case analysis unit takes into account examples from different cultural spheres and regions in analyzing success cases and proposes the optimal training method for the user. For example, it proposes training methods based on success cases from Asia and Africa. In addition, the success case analysis unit analyzes success cases from different cultural spheres and regions in order to propose training methods from a global perspective. For example, it proposes training methods based on success cases from South America and Oceania. This makes it possible to provide training methods from a global perspective.

[0063] The success case analysis unit visualizes the analysis results of the success cases and provides them to the user, thereby presenting the training method in a form that is visually easy to understand. The success case analysis unit, for example, builds a system that visualizes the analysis results of the success cases and provides them to the user. For example, the training method is visually displayed using graphs or charts. The success case analysis unit also visualizes the analysis results of the success cases and presents the training method in a form that is visually easy to understand for the user. For example, the training method is explained using infographics. The success case analysis unit also provides the visualized analysis results of the success cases to the user and presents the training method in a form that is visually easy to understand. For example, the training method is explained using videos or animations. This makes it possible to provide the training method in a form that is visually easy to understand.

[0064] The success case analysis unit can use the emotion estimation function to select from the success cases the case that evokes the most positive emotion in the user, and propose a training method based on that case. For example, the success case analysis unit can use the emotion estimation function to identify from the success cases the case that evokes the most positive emotion in the user, and propose a training method based on that case. For example, a training plan can be created based on success cases that evoke a positive emotional response from the user. The success case analysis unit can also analyze the user's emotional response and build a system that preferentially proposes success cases that evoke the most positive emotion in the user. For example, a training method can be proposed based on success cases that evoke a positive emotional response from the user. The success case analysis unit can also use the emotion estimation function to identify the success case that evokes the most positive emotion in the user, and propose a training method based on that case. For example, a training plan can be created based on success cases that evoke a positive emotional response from the user. This makes it possible to provide a training method based on success cases that evoke positive emotions in the user.

[0065] The training suggestion unit can feed back the user's real-time performance data and dynamically adjust the training content. The training suggestion unit, for example, collects the user's real-time performance data and builds a system that dynamically adjusts the training content based on that data. For example, it monitors the user's heart rate and exercise intensity in real time and adjusts the training content. The training suggestion unit also develops a system that feeds back the real-time performance data and dynamically adjusts the training content. For example, it adjusts the training intensity and frequency based on the user's performance data. The training suggestion unit also builds a system that feeds back the user's real-time performance data and dynamically adjusts the training content. For example, it updates the training plan in real time based on the user's exercise data. This allows the training content to be dynamically adjusted based on the real-time performance data.

[0066] The training suggestion unit can provide comprehensive health management by taking into account the user's diet and nutritional information. The training suggestion unit, for example, builds a system that collects the user's diet and nutritional information and proposes a training method based on that data. For example, it adjusts a training plan based on the user's dietary content and nutritional intake. The training suggestion unit also develops a system that considers dietary and nutritional information and proposes a training method for comprehensive health management. For example, it adjusts the intensity and content of training based on the user's nutritional status. The training suggestion unit also collects the user's dietary and nutritional information and proposes a training method for comprehensive health management based on that data. For example, it adjusts a training plan based on the user's dietary content and nutritional intake. This allows comprehensive health management to be provided based on dietary and nutritional information.

[0067] The training suggestion unit can use the emotion estimation function to monitor the user's emotions during training and provide advice to maintain motivation in real time. The training suggestion unit, for example, uses the emotion estimation function to build a system that monitors the user's emotions during training in real time and provides advice to maintain motivation. For example, if the user is tired, an encouraging message is displayed. The training suggestion unit also develops a system that monitors the user's emotions during training and provides advice to maintain motivation in real time. For example, if the user is feeling anxious, a message that gives a sense of security is displayed. The training suggestion unit also uses the emotion estimation function to build a system that monitors the user's emotions during training in real time and provides advice to maintain motivation. For example, advice that makes the user feel positive emotions is provided. This makes it possible to monitor the user's emotions during training and provide advice to maintain motivation.

[0068] The training suggestion unit can provide a more practical training environment using virtual reality (VR) or augmented reality (AR). For example, the training suggestion unit uses VR as a concrete example of a training method, allowing the user to perform training in a similar environment to an actual sports environment. For example, VR goggles are used to simulate an actual match. The training suggestion unit also uses AR to provide a concrete example of a training method, allowing the user to perform practical training. For example, an AR app is used to check the training movements in real time. The training suggestion unit also combines VR and AR to provide a concrete example of a training method, allowing the user to perform more practical training. For example, a match simulation is performed using VR, and the training movements are checked using AR. In this way, a practical training environment can be provided using VR and AR.

[0069] The training suggestion unit can combine training elements of different sports to enhance the effectiveness of cross-training. For example, the training suggestion unit combines training elements of different sports to propose a training method that enhances the effectiveness of cross-training. For example, it combines training elements of soccer and basketball. The training suggestion unit also combines training elements of different sports to propose an optimal cross-training method for the user. For example, it combines training elements of swimming and running. The training suggestion unit also proposes a training method that combines training elements of different sports to enhance the effectiveness of cross-training. For example, it combines training elements of tennis and volleyball. In this way, the effectiveness of cross-training can be enhanced by combining training elements of different sports.

[0070] The training suggestion unit uses the emotion estimation function to analyze the user's emotional response to specific training methods and continuously suggest optimal training methods. The training suggestion unit, for example, uses the emotion estimation function to analyze the user's emotional response to specific training methods in real time and build a system that suggests optimal training methods. For example, the training suggestion unit preferentially suggests training methods that evoke positive emotions in the user. The training suggestion unit also develops a system that analyzes the user's emotional response and continuously suggests optimal training methods. For example, a new training plan is created based on training methods that evoke positive emotions in the user. The training suggestion unit also uses the emotion estimation function to analyze the user's emotional response to specific training methods and build a system that continuously suggests optimal training methods. For example, a new training plan is created based on training methods that evoke positive emotions in the user. This allows optimal training methods to be continuously suggested based on the user's emotional response.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The user information input unit can also input the user's health condition and medical history, and adjust the training method based on that. For example, the user can input the parts of the body that have previously been injured or any chronic illnesses, and the AI ​​can adjust the training plan based on that information. For example, for a user with knee problems, it can suggest a training method that does not put strain on the knee. The user information input unit also requires the user to periodically input their health condition, and the AI ​​can adjust the difficulty and content of the training based on that data. For example, for a user with heart problems, it can suggest safe training while monitoring their heart rate. The user information input unit also requires the user to input their health condition, and the AI ​​can adjust the frequency and intensity of training based on that data. For example, if the user is not feeling well, it can suggest lighter training. This makes it possible to provide training methods that suit the user's health condition.

[0073] The user information input unit can also input the user's lifestyle habits and daily activity level, and adjust the training method based on that. For example, the user can input how much exercise they do daily, and the AI ​​can adjust the training plan based on that information. For example, for users who do a lot of desk work, it can suggest stretching or light exercise. The user information input unit also periodically inputs the user's lifestyle habits, and the AI ​​can adjust the difficulty and content of the training based on that data. For example, for users who often work night shifts, it can suggest a training method that takes fatigue into consideration. The user information input unit also inputs the user's activity level, and the AI ​​can adjust the frequency and intensity of the training based on that data. For example, it can suggest challenging training for users who are usually active. This makes it possible to provide training methods that are suited to the user's lifestyle habits and activity level.

[0074] The user information input unit can also prompt the user to input their hobbies and interests, and suggest training methods based on that. For example, the user inputs their favorite sports and activities, and the AI ​​suggests training plans based on that information. For example, for a user who likes dancing, it suggests training methods that incorporate dance. The user information input unit also prompts the user to periodically input their hobbies and interests, and the AI ​​adjusts the content of the training based on that data. For example, for a user who likes the outdoors, it suggests training methods that can be done outdoors. The user information input unit also prompts the user to input their interests, and the AI ​​adjusts the frequency and intensity of the training based on that data. For example, for a user who likes music, it suggests training methods that incorporate music. This makes it possible to provide training methods that suit users' hobbies and interests.

[0075] The user information input unit can also use the emotion estimation function to analyze the emotion the user feels when entering data and suggest a training method that matches the input content. For example, if the user feels stressed when entering data, a training method that has a relaxing effect is suggested. The user information input unit also uses the emotion estimation function to analyze the emotion the user feels when entering data and adjust the frequency and intensity of training according to the input content. For example, if the user feels positive, a challenging training method is suggested. The user information input unit also uses the emotion estimation function to analyze the emotion the user feels when entering data and adjust the training content according to the input content. For example, if the user feels anxious, a training method that gives a sense of security is suggested. In this way, a training method that matches the emotion can be provided.

[0076] The user information input unit can also use the emotion estimation function to analyze the emotion the user feels when entering information and evaluate the progress of training according to the input content. For example, if the user feels positive when entering information, it evaluates that the progress of training is good. The user information input unit also uses the emotion estimation function to analyze the emotion the user feels when entering information and evaluates the effectiveness of training according to the input content. For example, if the user feels negative when entering information, it evaluates that the effectiveness of training is low. The user information input unit also uses the emotion estimation function to analyze the emotion the user feels when entering information and adjusts the content of training according to the input content. For example, if the user feels stressed when entering information, it suggests a training method that has a relaxing effect. This makes it possible to evaluate the progress and effectiveness of training according to emotions.

[0077] The training suggestion unit can also adjust the training method taking into account the user's sleep data. For example, the AI ​​can monitor the user's sleep time and sleep quality and adjust the training plan based on that data. For example, it can suggest lighter training to a user who is sleep-deprived. The training suggestion unit also periodically collects the user's sleep data and adjusts the difficulty and content of the training based on that data. For example, it can suggest a more challenging training method to a user who sleeps well. The training suggestion unit can also adjust the frequency and intensity of training taking into account the user's sleep data. For example, it can suggest a training method that has a relaxing effect to a user who sleeps poorly. This makes it possible to provide training methods based on sleep data.

[0078] The training suggestion unit can also adjust the training method taking into account the user's stress level. For example, the AI ​​monitors the user's stress level and adjusts the training plan based on that data. For example, for a user with high stress, it suggests a training method that has a relaxing effect. The training suggestion unit also periodically collects the user's stress level and adjusts the difficulty and content of the training based on that data. For example, it suggests a challenging training method for a user with low stress. The training suggestion unit also adjusts the frequency and intensity of the training taking into account the user's stress level. For example, it suggests a balanced training method for a user with moderate stress. This makes it possible to provide training methods based on stress levels.

[0079] The training suggestion unit can also suggest a training method taking into account the user's sociability and communication style. For example, if the user is sociable, it will suggest group training. The training suggestion unit also periodically collects the user's communication style and adjusts the content of the training based on that data. For example, if the user prefers training alone, it will suggest individual training. The training suggestion unit also adjusts the frequency and intensity of training taking into account the user's sociability. For example, if the user is sociable, it will suggest a training method to do with other users. This makes it possible to provide a training method that suits the user's sociability and communication style.

[0080] The training suggestion unit can also use the emotion estimation function to monitor the user's emotions during training and evaluate the training progress in real time. For example, if the user feels positive emotions during training, it evaluates that the training progress is good. The training suggestion unit also uses the emotion estimation function to monitor the user's emotions during training and evaluates the effectiveness of the training in real time. For example, if the user feels negative emotions during training, it evaluates that the training effectiveness is low. The training suggestion unit also uses the emotion estimation function to monitor the user's emotions during training and adjust the training content in real time. For example, if the user feels stressed during training, it suggests a training method that has a relaxing effect. This makes it possible to evaluate the progress and effectiveness of training in real time according to emotions.

[0081] The training suggestion unit can also use the emotion estimation function to analyze the user's emotional response to specific training methods and continuously suggest optimal training methods. For example, it can preferentially suggest training methods that evoke positive emotions in the user. The training suggestion unit can also develop a system that analyzes the user's emotional response and continuously suggest optimal training methods. For example, it can create a new training plan based on training methods that evoke positive emotions in the user. The training suggestion unit can also use the emotion estimation function to build a system that analyzes the user's emotional response to specific training methods and continuously suggest optimal training methods. For example, it can create a new training plan based on training methods that evoke positive emotions in the user. This makes it possible to continuously suggest optimal training methods based on the user's emotional response.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The user information input unit inputs the user's sport, age, physique, position, and desired appearance. For example, the user information input unit provides an interface for selecting the sport the user is playing and inputting their age, physique, position, and desired appearance. The user information input unit also sends the input information as a prompt to the AI. Step 2: The success story analysis unit analyzes success stories based on the information entered by the user information input unit. For example, the success story analysis unit refers to a database of past success stories and identifies the success story that best suits the user's information. The success story analysis unit also uses AI to analyze the success stories and select the optimal training method for the user. Step 3: The training suggestion unit proposes the optimal training method based on the success cases analyzed by the success case analysis unit. For example, the training suggestion unit proposes a training method based on the user's age and physique, and provides appropriate instruction while minimizing the risk of injury. The training suggestion unit also presents specific training methods and supports the user in putting them into practice.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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 user information input unit for inputting the user's sport, age, physique, position, and desired appearance; a success case analysis unit that analyzes success cases based on the information input by the user information input unit; a training suggestion unit that suggests an optimal training method based on the success cases analyzed by the success case analysis unit. A system characterized by:

2. The user information input unit It collects the user's past training data and performance data, and uses that data to optimize individual training plans using AI.

2. The system of claim 1.

3. The user information input unit Allows users to input their psychological state and motivation level, and adjusts training methods based on that.

2. The system of claim 1.

4. The user information input unit Analyzes emotions in real time as users input, providing an interface that elicits positive emotions 2. The system of claim 1.

5. The user information input unit Introducing voice input and gesture input to provide a more intuitive interface 2. The system of claim 1.

6. The user information input unit Integrate data from different sports and fitness activities to provide cross-sport training suggestions 2. The system of claim 1.

7. The user information input unit Analyzes the user's emotions as they type and displays motivational messages based on their input.

2. The system of claim 1.

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