System

Abstract

An object of a system according to an embodiment is to provide an optimum practice program according to an individual situation or a target to a swimming competitor.SOLUTION: A system according to an embodiment includes a generation AI, an input device, an analyzer, a generator, and a provider. The generation AI generates an optimal practice program on the basis of individual situations and targets of the competitive swimmers. The inputting unit inputs information such as athlete age, physical strength, a technical level, and a target race or event. The analysis unit analyzes the information input by the input unit. The generation unit generates a practice program on the basis of the information analyzed by the analysis unit. The provision unit provides the practice program generated by the generation 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] Conventional techniques have had the problem of making it difficult to provide competitive swimmers with optimal training programs tailored to their individual circumstances and goals.

[0005] The system according to the embodiment aims to provide competitive swimmers with optimal training programs suited to their individual circumstances and goals. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, an input unit, an analysis unit, a generation unit, and a provision unit. The generation AI generates an optimal training program based on the individual circumstances and goals of a competitive swimmer. The input unit inputs information such as the swimmer's age, physical strength, technical level, and target competition or event. The analysis unit analyzes the information input by the input unit. The generation unit generates a training program based on the information analyzed by the analysis unit. The provision unit provides the training program generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide competitive swimmers with optimal training programs tailored to their individual circumstances and goals. [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) The training program providing system according to an embodiment of the present invention is a system in which a generation AI provides training programs to students aiming to become competitive swimmers and their coaches. As a result, the training program providing system can provide appropriate training to all swimmers.

[0029] A training program providing system according to an embodiment includes a generation AI, an input unit, an analysis unit, a generation unit, and a provision unit. The generation AI generates an optimal training program based on the individual circumstances and goals of a competitive swimmer. For example, the generation AI generates a training program based on information such as the swimmer's age, physical strength, technical level, and target competition or event. The input unit inputs information such as the swimmer's age, physical strength, technical level, and target competition or event. For example, the input unit provides an interface for inputting the swimmer's profile information. The analysis unit analyzes the information input by the input unit. For example, the analysis unit evaluates the swimmer's physical strength and technical level and provides data necessary for generating a training program. The generation unit generates a training program based on the information analyzed by the analysis unit. For example, the generation unit creates a training menu tailored to the swimmer's goals. The provision unit provides the training program generated by the generation unit. For example, the provision unit provides the generated training program to the swimmer or coach via a digital platform or mobile app. This allows the training program providing system to provide an optimal training program based on the individual circumstances and goals of the competitive swimmer.

[0030] Generative AI can analyze a player's past training data and match results to generate a training program that maximizes performance improvement. For example, generative AI collects a player's past training data and analyzes the frequency, intensity, and technical progress of training. This allows it to understand the player's current performance level and generate an optimal training program. Generative AI can also identify a player's strengths and weaknesses based on match results and suggest training menus accordingly. For example, for a player with a weak starting sprint, it can generate a program that focuses on starting practice. Generative AI can also integrate past training data with match results and analyze trends to improve a player's performance. This allows it to provide a training program that promotes the player's growth. This makes it possible to provide an optimal training program based on a player's past data.

[0031] Generative AI can incorporate a player's dietary and sleep data and propose a training program that takes into account their overall health. For example, generative AI can analyze a player's dietary data and generate a training program that takes into account nutritional balance and calorie intake. For example, it can recommend a high-calorie meal before a training day when energy consumption is high. Generative AI can also evaluate a player's fatigue level and recovery state based on sleep data and propose a training menu accordingly. For example, it can recommend lighter training on days when the player is sleep-deprived. Generative AI can also integrate dietary and sleep data to evaluate a player's overall health. This allows it to provide a training program that is optimal for the player's physical condition. This makes it possible to provide a training program that takes into account a player's health.

[0032] Generative AI can refer to practice data from other sports and generate new training programs that can be applied to competitive swimming. For example, generative AI can analyze practice data from track and field to propose training methods that can be applied to competitive swimming. For example, it can generate a short-distance training menu that incorporates sprint training techniques. Generative AI can also refer to practice data from cycling to generate a training program to improve endurance. For example, it can propose a long-distance training menu that incorporates interval training. Generative AI can also integrate practice data from other sports to develop new training methods that can be applied to competitive swimming. This provides a training program aimed at improving athlete performance. This makes it possible to refer to practice data from other sports and provide new training programs that can be applied to competitive swimming.

[0033] Generative AI can provide optimal training programs by taking into account the athlete's training environment. For example, generative AI collects data on the athlete's training environment and generates training programs that take into account the water temperature and length of the pool. For example, if the water temperature is low, it will suggest a menu that emphasizes warm-ups. Generative AI also provides training menus according to the length of the pool. For example, it will suggest different training content for a 25m pool and a 50m pool. Generative AI also generates training programs to maximize athlete performance based on training environment data. For example, it will suggest training methods that are optimal for the training environment. This makes it possible to provide optimal training programs that take into account the athlete's training environment.

[0034] The generation AI can analyze the technical details of each swimming style and generate a practice menu aimed at improving technique. For example, the generation AI can analyze the stroke efficiency of each swimming style and generate a practice menu aimed at improving technique. For example, it can suggest exercises to improve stroke timing and form. The generation AI can also analyze turning technique and provide a turn practice menu that is optimal for each swimming style. For example, it can suggest exercises to improve backstroke turning technique. The generation AI can also analyze the technical details of each swimming style and generate a practice program aimed at improving the athlete's technique. For example, it can suggest exercises to improve butterfly stroke stroke efficiency. In this way, the generation AI can analyze the technical details of each swimming style and provide a practice menu aimed at improving technique.

[0035] The generation AI can propose the optimal training content for each swimming style based on the athlete's body type and muscle strength data. For example, the generation AI analyzes the athlete's body type data and proposes the optimal training content for each swimming style. For example, it provides methods for improving strokes according to body type. The generation AI also proposes strength training required for each swimming style based on muscle strength data. For example, it provides training to strengthen the upper body muscles required for butterfly. The generation AI also integrates body type and muscle strength data to propose training content optimal for the athlete's characteristics. For example, it provides training to improve stroke efficiency in freestyle. This makes it possible to provide the optimal training content for each swimming style based on the athlete's body type and muscle strength data.

[0036] The generating AI can propose a training menu for cooperative training by taking into account the data of the player's training partners and teammates. The generating AI can, for example, analyze the data of the player's training partners and propose a training menu for cooperative training. For example, it can provide relay training or pair training. The generating AI can also generate a training menu for the entire team based on the data of teammates. For example, it can propose interval training for the entire team. The generating AI can also integrate the data of training partners and teammates and propose a training menu for cooperative training. This provides a training program that strengthens team cooperation. This can propose a training menu for cooperative training by taking into account the data of the player's training partners and teammates.

[0037] Generative AI can monitor an athlete's heart rate and oxygen consumption in real time and generate a training menu that suggests optimal pacing. For example, generative AI can monitor an athlete's heart rate data in real time and generate a training menu that suggests optimal pacing. For example, it can adjust the pace so that the heart rate stays within a certain range. Generative AI can also suggest a training menu to maximize the athlete's endurance based on oxygen consumption. For example, it can avoid exercises that require high oxygen consumption and aim for efficient energy consumption. Generative AI can also integrate heart rate and oxygen consumption data to provide a training program that optimizes athlete performance. For example, it can suggest a pacing that takes into account the balance between heart rate and oxygen consumption. This makes it possible to monitor an athlete's heart rate and oxygen consumption in real time and provide a training menu that suggests optimal pacing.

[0038] The generating AI can analyze a player's muscle fatigue level and provide a training menu to promote recovery. For example, the generating AI can monitor a player's muscle fatigue level in real time and generate a training menu to promote recovery. For example, if fatigue level is high, it can suggest lighter training. The generating AI can also suggest stretching and recovery training to promote a player's recovery based on muscle fatigue level data. For example, it can provide a stretching menu to relieve muscle tension. The generating AI can also analyze muscle fatigue level and provide a training program to maximize a player's recovery. For example, it can adjust the program so that high-intensity training is performed on days when fatigue level is low. This makes it possible to analyze a player's muscle fatigue level and provide a training menu to promote recovery.

[0039] The generative AI can provide a training menu that takes into account the athlete's training location and is suited to the environment. For example, the generative AI collects data on the athlete's training location and generates a training menu that is suited to indoor or outdoor pools. For example, for an outdoor pool, it will suggest training content that takes the weather into account. The generative AI also provides training methods that are suited to the training location. For example, for an indoor pool, it will suggest a training menu that emphasizes turning techniques. The generative AI also provides a training program to maximize the athlete's performance based on the training location data. For example, it will suggest the training method that is best suited to the training location. This makes it possible to provide a training menu that takes into account the athlete's training location and is suited to the environment.

[0040] The generation AI can take into account the player's practice time and propose the optimal practice menu for that time. For example, the generation AI collects data on the player's practice time and generates a practice menu suitable for morning practice or evening practice. For example, it proposes a menu that emphasizes warm-up for morning practice. The generation AI also provides training methods according to the practice time. For example, it proposes a relaxing practice menu for evening practice. The generation AI also provides a practice program to maximize the player's performance based on the practice time data. For example, it proposes the optimal training method for the practice time. This makes it possible to take into account the player's practice time and provide the optimal practice menu for that time.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The training program provision system can also provide long-term training plans based on the player's training history. For example, it can analyze the player's past training data and create a year-round training schedule. It can also flexibly adjust the training plan according to the player's growth and progress. Furthermore, it can provide medium- to long-term training plans tailored to the player's goals and support the player's performance improvement. This makes it possible to provide long-term training plans based on the player's training history.

[0043] The training program provision system can also be equipped with functions to support athletes' nutritional management. For example, it can collect athletes' dietary data and propose meal plans that take nutritional balance into consideration. It can also advise athletes on the timing and amount of nutrition to replenish based on their training. It can also analyze athletes' dietary data and provide feedback to prevent nutritional deficiencies and over-intake. This makes it possible to provide functions to support athletes' nutritional management.

[0044] The training program provision system can also suggest optimal training times based on the player's sleep data. For example, it can analyze the player's sleep patterns and suggest the most effective training times. If the player continues to suffer from sleep deprivation, it can also adjust the intensity of training and prioritize the player's recovery. Furthermore, it can provide advice on improving the quality of sleep based on the player's sleep data. This makes it possible to suggest optimal training times based on the player's sleep data.

[0045] The training program provision system can also simulate the athlete's training environment and provide an optimal training menu. For example, it can simulate the water temperature, water quality, and lighting conditions of the pool where the athlete practices, and propose a training menu accordingly. It can also provide training methods to help athletes adapt to different environments when they practice in different environments. Furthermore, it can provide a training program that responds to changes in the environment based on the athlete's training environment data. This makes it possible to provide an optimal training menu that simulates the athlete's training environment.

[0046] The training program providing system can further include a function for strengthening cooperation between players and their training partners. For example, it can analyze data on a player's training partners and suggest a training menu for joint practice. It can also provide a function for promoting communication with training partners. Furthermore, it can provide feedback to strengthen cooperation with training partners and support the improvement of the team's overall performance. This makes it possible to provide a function for strengthening cooperation between players and their training partners.

[0047] The training program provision system can also provide advice to improve performance based on the player's training data. For example, it can analyze the player's training data and identify areas for technical improvement. It can also suggest specific training methods to improve the player's performance. Furthermore, it can provide feedback to improve performance based on the player's training data and support the player's growth. This makes it possible to provide advice to improve performance based on the player's training data.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The input unit inputs information such as the athlete's age, physical strength, skill level, target competition or event, etc. For example, the input unit provides an interface for inputting the athlete's profile information. Step 2: The analysis unit analyzes the information input by the input unit. For example, the analysis unit evaluates the physical strength and technical level of the player and provides data necessary for generating a training program. Step 3: The generator generates a practice program based on the information analyzed by the analyzer. For example, the generator creates a training menu tailored to the player's goals. Step 4: The providing unit provides the training program generated by the generating unit. For example, the providing unit provides the generated training program to players and coaches via a digital platform or a mobile app. Step 5: The AI ​​generates an optimal training program based on the swimmer's individual circumstances and goals. For example, the AI ​​generates a training program based on information such as the swimmer's age, physical strength, technical level, and target competitions or events.

[0050] (Example 2) The training program providing system according to an embodiment of the present invention is a system in which a generation AI provides training programs to students aiming to become competitive swimmers and their coaches. As a result, the training program providing system can provide appropriate training to all swimmers.

[0051] A training program providing system according to an embodiment includes a generation AI, an input unit, an analysis unit, a generation unit, and a provision unit. The generation AI generates an optimal training program based on the individual circumstances and goals of a competitive swimmer. For example, the generation AI generates a training program based on information such as the swimmer's age, physical strength, technical level, and target competition or event. The input unit inputs information such as the swimmer's age, physical strength, technical level, and target competition or event. For example, the input unit provides an interface for inputting the swimmer's profile information. The analysis unit analyzes the information input by the input unit. For example, the analysis unit evaluates the swimmer's physical strength and technical level and provides data necessary for generating a training program. The generation unit generates a training program based on the information analyzed by the analysis unit. For example, the generation unit creates a training menu tailored to the swimmer's goals. The provision unit provides the training program generated by the generation unit. For example, the provision unit provides the generated training program to the swimmer or coach via a digital platform or mobile app. This allows the training program providing system to provide an optimal training program based on the individual circumstances and goals of the competitive swimmer.

[0052] Generative AI can analyze a player's past training data and match results to generate a training program that maximizes performance improvement. For example, generative AI collects a player's past training data and analyzes the frequency, intensity, and technical progress of training. This allows it to understand the player's current performance level and generate an optimal training program. Generative AI can also identify a player's strengths and weaknesses based on match results and suggest training menus accordingly. For example, for a player with a weak starting sprint, it can generate a program that focuses on starting practice. Generative AI can also integrate past training data with match results and analyze trends to improve a player's performance. This allows it to provide a training program that promotes the player's growth. This makes it possible to provide an optimal training program based on a player's past data.

[0053] Generative AI can incorporate a player's dietary and sleep data and propose a training program that takes into account their overall health. For example, generative AI can analyze a player's dietary data and generate a training program that takes into account nutritional balance and calorie intake. For example, it can recommend a high-calorie meal before a training day when energy consumption is high. Generative AI can also evaluate a player's fatigue level and recovery state based on sleep data and propose a training menu accordingly. For example, it can recommend lighter training on days when the player is sleep-deprived. Generative AI can also integrate dietary and sleep data to evaluate a player's overall health. This allows it to provide a training program that is optimal for the player's physical condition. This makes it possible to provide a training program that takes into account a player's health.

[0054] The generation AI can use the emotion estimation function to monitor a player's emotions during practice in real time and generate a training program to maintain motivation. For example, the generation AI uses the emotion estimation function to monitor a player's emotions during practice in real time. For example, if a player is feeling fatigued or stressed, the generation AI can suggest a training menu that will help them relax. The generation AI can also analyze emotional data during practice and generate a training program to maintain the player's motivation. For example, it can prioritize suggesting training menus that encourage positive emotions. The generation AI can also provide feedback based on the player's emotional state based on the emotion estimation data. For example, if a player feels negative emotions during practice, it can display an encouraging message. This makes it possible to monitor a player's emotions and provide a training program to maintain their motivation.

[0055] Generative AI can refer to practice data from other sports and generate new training programs that can be applied to competitive swimming. For example, generative AI can analyze practice data from track and field to propose training methods that can be applied to competitive swimming. For example, it can generate a short-distance training menu that incorporates sprint training techniques. Generative AI can also refer to practice data from cycling to generate a training program to improve endurance. For example, it can propose a long-distance training menu that incorporates interval training. Generative AI can also integrate practice data from other sports to develop new training methods that can be applied to competitive swimming. This provides a training program aimed at improving athlete performance. This makes it possible to refer to practice data from other sports and provide new training programs that can be applied to competitive swimming.

[0056] Generative AI can provide optimal training programs by taking into account the athlete's training environment. For example, generative AI collects data on the athlete's training environment and generates training programs that take into account the water temperature and length of the pool. For example, if the water temperature is low, it will suggest a menu that emphasizes warm-ups. Generative AI also provides training menus according to the length of the pool. For example, it will suggest different training content for a 25m pool and a 50m pool. Generative AI also generates training programs to maximize athlete performance based on training environment data. For example, it will suggest training methods that are optimal for the training environment. This makes it possible to provide optimal training programs that take into account the athlete's training environment.

[0057] The generative AI can use the emotion estimation function to analyze a player's emotions before and after practice and evaluate the effectiveness of a training program. For example, the generative AI can use the emotion estimation function to monitor a player's emotions before and after practice in real time. For example, it can compare emotion scores before and after practice to evaluate the effectiveness of a training program. The generative AI can also analyze emotion data before and after practice to identify areas for improvement in the training program. For example, it can modify training menus that are associated with strong negative emotions. The generative AI can also build a system to evaluate the effectiveness of a training program based on the emotion estimation data. For example, it can prioritize suggesting training menus that are associated with strong positive emotions. This makes it possible to analyze a player's emotions before and after practice and evaluate the effectiveness of a training program.

[0058] The generation AI can analyze the technical details of each swimming style and generate a practice menu aimed at improving technique. For example, the generation AI can analyze the stroke efficiency of each swimming style and generate a practice menu aimed at improving technique. For example, it can suggest exercises to improve stroke timing and form. The generation AI can also analyze turning technique and provide a turn practice menu that is optimal for each swimming style. For example, it can suggest exercises to improve backstroke turning technique. The generation AI can also analyze the technical details of each swimming style and generate a practice program aimed at improving the athlete's technique. For example, it can suggest exercises to improve butterfly stroke stroke efficiency. In this way, the generation AI can analyze the technical details of each swimming style and provide a practice menu aimed at improving technique.

[0059] The generation AI can propose the optimal training content for each swimming style based on the athlete's body type and muscle strength data. For example, the generation AI analyzes the athlete's body type data and proposes the optimal training content for each swimming style. For example, it provides methods for improving strokes according to body type. The generation AI also proposes strength training required for each swimming style based on muscle strength data. For example, it provides training to strengthen the upper body muscles required for butterfly. The generation AI also integrates body type and muscle strength data to propose training content optimal for the athlete's characteristics. For example, it provides training to improve stroke efficiency in freestyle. This makes it possible to provide the optimal training content for each swimming style based on the athlete's body type and muscle strength data.

[0060] The generation AI can use the emotion estimation function to generate a training menu to reduce the stress and anxiety that an athlete feels about a particular swimming style. For example, the generation AI uses the emotion estimation function to monitor in real time the stress that an athlete feels about a particular swimming style. For example, if stress is high, the generation AI can suggest a training menu that will help them relax. The generation AI can also adjust the training menu based on the emotion data for a swimming style that causes anxiety. For example, it can provide a step-by-step training program to reduce anxiety. The generation AI can also generate a training menu to reduce the athlete's stress and anxiety based on the emotion estimation data. For example, it can suggest training to elicit positive emotions. This makes it possible to provide a training menu to reduce the stress and anxiety that an athlete feels about a particular swimming style.

[0061] The generating AI can propose a training menu for cooperative training by taking into account the data of the player's training partners and teammates. The generating AI can, for example, analyze the data of the player's training partners and propose a training menu for cooperative training. For example, it can provide relay training or pair training. The generating AI can also generate a training menu for the entire team based on the data of teammates. For example, it can propose interval training for the entire team. The generating AI can also integrate the data of training partners and teammates and propose a training menu for cooperative training. This provides a training program that strengthens team cooperation. This can propose a training menu for cooperative training by taking into account the data of the player's training partners and teammates.

[0062] The generation AI can use the emotion estimation function to analyze the emotions of athletes when practicing a specific swimming style and provide a training menu that brings out positive emotions. For example, the generation AI can use the emotion estimation function to monitor the emotions of athletes when practicing a specific swimming style in real time. For example, it can prioritize and suggest training menus that evoke strong positive emotions. The generation AI can also provide training menus that bring out positive emotions in athletes based on the emotion data. For example, it can suggest training content that the athletes can enjoy. The generation AI can also analyze the emotion estimation data and provide feedback according to the athletes' emotional state. For example, it can display encouraging messages to bring out positive emotions. This makes it possible to analyze the emotions of athletes when practicing a specific swimming style and provide a training menu that brings out positive emotions.

[0063] Generative AI can monitor an athlete's heart rate and oxygen consumption in real time and generate a training menu that suggests optimal pacing. For example, generative AI can monitor an athlete's heart rate data in real time and generate a training menu that suggests optimal pacing. For example, it can adjust the pace so that the heart rate stays within a certain range. Generative AI can also suggest a training menu to maximize the athlete's endurance based on oxygen consumption. For example, it can avoid exercises that require high oxygen consumption and aim for efficient energy consumption. Generative AI can also integrate heart rate and oxygen consumption data to provide a training program that optimizes athlete performance. For example, it can suggest a pacing that takes into account the balance between heart rate and oxygen consumption. This makes it possible to monitor an athlete's heart rate and oxygen consumption in real time and provide a training menu that suggests optimal pacing.

[0064] The generating AI can analyze a player's muscle fatigue level and provide a training menu to promote recovery. For example, the generating AI can monitor a player's muscle fatigue level in real time and generate a training menu to promote recovery. For example, if fatigue level is high, it can suggest lighter training. The generating AI can also suggest stretching and recovery training to promote a player's recovery based on muscle fatigue level data. For example, it can provide a stretching menu to relieve muscle tension. The generating AI can also analyze muscle fatigue level and provide a training program to maximize a player's recovery. For example, it can adjust the program so that high-intensity training is performed on days when fatigue level is low. This makes it possible to analyze a player's muscle fatigue level and provide a training menu to promote recovery.

[0065] The generative AI can use its emotion estimation function to generate training menus to reduce the fatigue and stress that athletes feel during long-distance training. For example, the generative AI uses its emotion estimation function to monitor the fatigue and stress that athletes feel during long-distance training in real time. For example, if fatigue or stress is high, it can suggest a training menu that helps athletes relax. The generative AI can also analyze emotional data during long-distance training and provide training programs to reduce athletes' fatigue and stress. For example, it can suggest training content that elicits positive emotions. The generative AI can also provide feedback to athletes to reduce their fatigue and stress based on the emotion estimation data. For example, it can display encouraging messages during training. This makes it possible to provide training menus to reduce athletes' fatigue and stress during long-distance training.

[0066] The generative AI can provide a training menu that takes into account the athlete's training location and is suited to the environment. For example, the generative AI collects data on the athlete's training location and generates a training menu that is suited to indoor or outdoor pools. For example, for an outdoor pool, it will suggest training content that takes the weather into account. The generative AI also provides training methods that are suited to the training location. For example, for an indoor pool, it will suggest a training menu that emphasizes turning techniques. The generative AI also provides a training program to maximize the athlete's performance based on the training location data. For example, it will suggest the training method that is best suited to the training location. This makes it possible to provide a training menu that takes into account the athlete's training location and is suited to the environment.

[0067] The generation AI can take into account the player's practice time and propose the optimal practice menu for that time. For example, the generation AI collects data on the player's practice time and generates a practice menu suitable for morning practice or evening practice. For example, it proposes a menu that emphasizes warm-up for morning practice. The generation AI also provides training methods according to the practice time. For example, it proposes a relaxing practice menu for evening practice. The generation AI also provides a practice program to maximize the player's performance based on the practice time data. For example, it proposes the optimal training method for the practice time. This makes it possible to take into account the player's practice time and provide the optimal practice menu for that time.

[0068] The generation AI can use the emotion estimation function to analyze the emotions of players when practicing a specific distance and provide a training menu to maintain motivation. For example, the generation AI can use the emotion estimation function to monitor the emotions of players when practicing a specific distance in real time. For example, it can prioritize and suggest training menus that evoke strong positive emotions. The generation AI can also provide training menus to maintain the player's motivation based on the emotion data. For example, it can suggest training content that the player will enjoy. The generation AI can also analyze the emotion estimation data and provide feedback according to the player's emotional state. For example, it can display encouraging messages to elicit positive emotions. This makes it possible to analyze the emotions of players when practicing a specific distance and provide a training menu to maintain motivation.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The training program provision system can also provide training menus that take into account the player's psychological state. For example, if a player is nervous before a game, it can suggest mental training to help them relax. Also, if a player is losing motivation, it can provide goal setting and positive feedback to boost motivation. Furthermore, it can monitor the player's psychological state in real time and adjust the training menu as needed. This makes it possible to provide training programs that take into account the player's psychological state.

[0071] The training program provision system can also provide long-term training plans based on the player's training history. For example, it can analyze the player's past training data and create a year-round training schedule. It can also flexibly adjust the training plan according to the player's growth and progress. Furthermore, it can provide medium- to long-term training plans tailored to the player's goals and support the player's performance improvement. This makes it possible to provide long-term training plans based on the player's training history.

[0072] The training program provision system can also be equipped with functions to support athletes' nutritional management. For example, it can collect athletes' dietary data and propose meal plans that take nutritional balance into consideration. It can also advise athletes on the timing and amount of nutrition to replenish based on their training. It can also analyze athletes' dietary data and provide feedback to prevent nutritional deficiencies and over-intake. This makes it possible to provide functions to support athletes' nutritional management.

[0073] The training program provision system can also suggest optimal training times based on the player's sleep data. For example, it can analyze the player's sleep patterns and suggest the most effective training times. If the player continues to suffer from sleep deprivation, it can also adjust the intensity of training and prioritize the player's recovery. Furthermore, it can provide advice on improving the quality of sleep based on the player's sleep data. This makes it possible to suggest optimal training times based on the player's sleep data.

[0074] The training program provision system can also estimate the player's emotions and provide a training menu based on those emotions. For example, it can monitor the stress and anxiety a player feels during training in real time and suggest a training menu that will help them relax. It can also provide a challenging training menu if the player is feeling positive emotions. Furthermore, it can provide feedback after training based on the player's emotional data and give advice to maintain the player's motivation. This makes it possible to provide a training menu based on the player's emotions.

[0075] The training program provision system can also simulate the athlete's training environment and provide an optimal training menu. For example, it can simulate the water temperature, water quality, and lighting conditions of the pool where the athlete practices, and propose a training menu accordingly. It can also provide training methods to help athletes adapt to different environments when they practice in different environments. Furthermore, it can provide a training program that responds to changes in the environment based on the athlete's training environment data. This makes it possible to provide an optimal training menu that simulates the athlete's training environment.

[0076] The training program providing system can further estimate the player's emotions and provide feedback based on the emotions. For example, it can display encouraging messages to reinforce the positive emotions the player feels during practice. It can also provide advice to help the player relax if the player is feeling negative emotions. Furthermore, it can provide feedback after practice based on the player's emotional data and give advice to improve the player's emotional state. This makes it possible to provide feedback based on the player's emotions.

[0077] The training program providing system can further include a function for strengthening cooperation between players and their training partners. For example, it can analyze data on a player's training partners and suggest a training menu for joint practice. It can also provide a function for promoting communication with training partners. Furthermore, it can provide feedback to strengthen cooperation with training partners and support the improvement of the team's overall performance. This makes it possible to provide a function for strengthening cooperation between players and their training partners.

[0078] The training program provision system can also estimate the player's emotions and provide training plans based on those emotions. For example, it can monitor the player's stress and anxiety during training in real time and suggest a training plan that helps them relax. It can also provide a challenging training plan if the player is feeling positive emotions. Furthermore, it can provide feedback after training based on the player's emotional data and give advice to maintain the player's motivation. This makes it possible to provide training plans based on the player's emotions.

[0079] The training program provision system can also provide advice to improve performance based on the player's training data. For example, it can analyze the player's training data and identify areas for technical improvement. It can also suggest specific training methods to improve the player's performance. Furthermore, it can provide feedback to improve performance based on the player's training data and support the player's growth. This makes it possible to provide advice to improve performance based on the player's training data.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The input unit inputs information such as the athlete's age, physical strength, skill level, target competition or event, etc. For example, the input unit provides an interface for inputting the athlete's profile information. Step 2: The analysis unit analyzes the information input by the input unit. For example, the analysis unit evaluates the physical strength and technical level of the player and provides data necessary for generating a training program. Step 3: The generator generates a practice program based on the information analyzed by the analyzer. For example, the generator creates a training menu tailored to the player's goals. Step 4: The providing unit provides the training program generated by the generating unit. For example, the providing unit provides the generated training program to players and coaches via a digital platform or a mobile app. Step 5: The AI ​​generates an optimal training program based on the swimmer's individual circumstances and goals. For example, the AI ​​generates a training program based on information such as the swimmer's age, physical strength, technical level, and target competitions or events.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 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. Equipped with generative AI, Generative AI that generates optimal training programs based on the individual circumstances and goals of competitive swimmers, and An input section for inputting information such as the athlete's age, physical strength, skill level, and target competitions and events; an analysis unit that analyzes the information input by the input unit; a generation unit that generates a practice program based on the information analyzed by the analysis unit; a providing unit that provides the practice program generated by the generating unit. A system characterized by:

2. The generated AI is Analyzes players' past training data and match results to generate training programs to maximize performance improvement 2. The system of claim 1.

3. The generated AI is By incorporating the player's dietary and sleep data, we propose training programs that take into account their overall health.

2. The system of claim 1.

4. The generated AI is Monitor players' emotions in real time during practice and generate training programs to maintain motivation 2. The system of claim 1.

5. The generated AI is Using training data from other sports to generate new training programs that can be applied to competitive swimming 2. The system of claim 1.

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