System

Abstract

An object of the system according to the embodiment is to enable a sumo wrestler or a parent to efficiently plan strategies for the next approach.SOLUTION: A system includes a data collection unit, an analysis unit, an advice generation unit, and a video generation unit. The data collection part collects past work data. The analysis unit analyzes the data collected by the data collection unit. The advice generation unit generates specific advice on the basis of a result analyzed by the analysis unit. The video generation unit generates a video of the approach based on the advice generated by the advice 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 technology has made it difficult for wrestlers and their coaches to quickly come up with strategies for their next bout, placing a heavy mental and physical burden on them.

[0005] The system according to the embodiment aims to enable sumo wrestlers and their coaches to efficiently develop strategies for their next bout. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, an advice generation unit, and a video generation unit. The data collection unit collects past effort data. The analysis unit analyzes the data collected by the data collection unit. The advice generation unit generates specific advice based on the results of the analysis by the analysis unit. The video generation unit generates a video of the effort based on the advice generated by the advice generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows sumo wrestlers and their coaches to efficiently plan strategies for their next bout. [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 advice providing system according to an embodiment of the present invention uses AI to provide sumo wrestlers and their coaches with specific advice on how to win the next match, allowing the wrestlers and their coaches to efficiently prepare for the match.

[0029] The advice providing system according to the embodiment includes a data collection unit, an analysis unit, an advice generation unit, and a video generation unit. The data collection unit collects data on past bouts. For example, it collects match results, training data, physical condition data, etc. The data collection unit can also collect data on the psychological state of the wrestlers. The analysis unit analyzes the data collected by the data collection unit. For example, it analyzes the data using statistical analysis or a machine learning algorithm. The advice generation unit generates specific advice based on the results of the analysis by the analysis unit. For example, it generates tactical advice or training advice. The video generation unit generates video of the bout based on the advice generated by the advice generation unit. For example, it generates simulation video or video of an actual match. This allows the advice providing system to enable wrestlers and their coaches to efficiently prepare for bouts.

[0030] The data collection unit can also collect data on physical and psychological states and perform machine learning based on the data. The data collection unit, for example, collects data on the physical state of the wrestlers (e.g., heart rate and sleep time) and combines it with past bout data to perform machine learning. This allows for an analysis of the impact that physical state has on the results of the bout. The data collection unit also quantifies the wrestlers' psychological states (e.g., stress levels and motivation) and combines it with past bout data to perform machine learning. This allows for an analysis of the impact that psychological states have on winning or losing. Furthermore, the data collection unit collects data on the wrestlers' physical and psychological states in real time and combines it with past bout data to perform machine learning. This allows for an analysis of the impact that fluctuations in physical and psychological states have on the results of the bout. This allows for the provision of advice that takes into account the wrestlers' physical and psychological states.

[0031] The data collection unit can also incorporate dietary or training data to make comprehensive performance predictions. The data collection unit, for example, collects dietary data (e.g., calorie intake and nutritional balance) of sumo wrestlers and combines it with past bout data to perform machine learning. This allows for an analysis of the impact of diet on performance. The data collection unit also collects training data (e.g., training content and intensity) of sumo wrestlers and combines it with past bout data to perform machine learning. This allows for an analysis of the impact of training on performance. Furthermore, the data collection unit collects dietary and training data of sumo wrestlers in real time and combines it with past bout data to perform machine learning. This allows for an analysis of the impact of fluctuations in diet and training on performance. This makes it possible to make performance predictions that take into account the dietary and training of sumo wrestlers.

[0032] The analysis unit can also incorporate data from other martial arts or sports to learn commonalities in techniques and tactics between different sports. For example, the analysis unit collects data from other martial arts (e.g., judo or boxing) and combines it with past bout data to perform machine learning. This allows for learning of commonalities in techniques and tactics between different sports. The analysis unit also collects data from other sports (e.g., wrestling or rugby) and combines it with past bout data to perform machine learning. This allows for learning of commonalities in techniques and tactics between different sports. Furthermore, the analysis unit collects data from other martial arts or sports in real time and combines it with past bout data to perform machine learning. This allows for learning of commonalities in techniques and tactics between different sports.

[0033] The analysis unit can generate a personalized training plan based on the individual characteristics of each sumo wrestler. The analysis unit, for example, analyzes the individual characteristics of each sumo wrestler (for example, physique or skill level) and generates a personalized training plan based on the results. This provides training that is optimal for the wrestler's characteristics. The analysis unit also analyzes the wrestler's past training data and generates a personalized training plan based on the results. This provides training that is optimal for the wrestler's past training history. Furthermore, the analysis unit collects the individual characteristics and past training data of each wrestler in real time and generates a personalized training plan based on the results. This provides training that is optimal for the wrestler's characteristics and training history. This allows for the generation of a training plan based on the individual characteristics of each wrestler.

[0034] The advice generation unit can provide advice that reflects the physical and psychological states of the wrestlers on the day in real time. For example, the advice generation unit collects data on the wrestlers' physical conditions on the day (e.g., heart rate and sleep hours) in real time and provides advice based on that data. This allows for the proposal of tactics that are optimal for their physical conditions. The advice generation unit also analyzes the wrestlers' psychological conditions on the day (e.g., stress level and motivation) in real time and provides advice based on that data. This allows for the proposal of tactics that are optimal for their psychological conditions. The advice generation unit also collects data on the wrestlers' physical and psychological conditions on the day in real time and provides advice based on that data. This allows for the proposal of tactics that are optimal for their physical and psychological conditions. This allows for the provision of advice that reflects the wrestlers' physical and psychological conditions on the day in real time.

[0035] The advice generation unit incorporates the opposing wrestler's latest training data or match data, allowing it to provide more accurate advice. The advice generation unit, for example, collects the opposing wrestler's latest training data (e.g., training content and intensity) and provides advice based on that data. This allows it to propose tactics that are optimal for the opponent's latest condition. The advice generation unit also collects the opposing wrestler's latest match data (e.g., recent wins and losses and frequency of technique use) and provides advice based on that data. This allows it to propose countermeasures that are optimal for the opponent's latest tactics. Furthermore, the advice generation unit collects the opposing wrestler's latest training data and match data in real time and provides advice based on that data. This allows it to propose countermeasures that are optimal for the opponent's latest condition and tactics. This allows it to provide highly accurate advice that incorporates the opposing wrestler's latest training data and match data.

[0036] The advice generation unit can provide advice in multiple scenarios, allowing the sumo wrestler to try out different tactics. The advice generation unit, for example, provides advice for tomorrow's bout in multiple scenarios, allowing the sumo wrestler to try out different tactics. For example, it proposes scenarios that change the timing of the initial attack or the selection of techniques. The advice generation unit also enables the sumo wrestler to try out different tactics based on the multiple scenarios. For example, it proposes multiple countermeasures against the opponent's specialty techniques, allowing the wrestler to select the optimal tactic. Furthermore, the advice generation unit provides advice for tomorrow's bout in multiple scenarios, allowing the sumo wrestler to try out different tactics. This helps the wrestler find the optimal tactic. This allows the wrestler to try out different tactics.

[0037] The advice generation unit can incorporate feedback from other wrestlers and coaches to improve the accuracy of the advice. The advice generation unit, for example, collects feedback from other wrestlers and coaches and provides advice based on that. This improves the accuracy of the advice. The advice generation unit also incorporates the opinions of other wrestlers and coaches to adjust the content of the advice. For example, the advice is improved based on past experience and knowledge. Furthermore, the advice generation unit collects feedback from other wrestlers and coaches in real time and provides advice based on that. This improves the accuracy of the advice. This improves the accuracy of the advice that incorporates feedback from other wrestlers and coaches.

[0038] The image generation unit also generates images from the perspective of the wrestlers, allowing for a more realistic simulation of an actual bout. The image generation unit, for example, generates images from the perspective of the wrestlers and simulates an actual bout. This allows the wrestlers to check the tactics from their own perspective. The image generation unit also generates images from the perspective of the wrestlers and simulates the bout. This makes it easier for the wrestlers to check the tactics from their own perspective. The image generation unit also generates images from the perspective of the wrestlers and simulates the bout. This makes it easier for the wrestlers to check the tactics from their own perspective and imagine the movements in an actual bout. This allows for a more realistic simulation of an actual bout by generating images from the perspective of the wrestlers.

[0039] The video generation unit can overlay the vital data of the wrestlers onto the video and provide real-time feedback. For example, the video generation unit can overlay the vital data of the wrestlers (heart rate, respiratory rate, etc.) onto the video and provide real-time feedback. This allows the wrestlers to check their tactics while checking their own physical condition. The video generation unit can also overlay the vital data of the wrestlers onto the video and provide real-time feedback. This makes it easier for the wrestlers to check their tactics while checking their own physical condition. The video generation unit can also overlay the vital data of the wrestlers onto the video and provide real-time feedback. This makes it easier for the wrestlers to check their tactics while checking their own physical condition, and to visualize their movements in an actual bout. This makes it possible to provide video with the vital data of the wrestlers overlaid on it and provide real-time feedback.

[0040] The image generation unit can provide the image in VR or AR, allowing the wrestlers to experience an actual bout. For example, the image generation unit can provide the image in VR, allowing the wrestlers to experience an actual bout. This makes it easier for the wrestlers to check tactics in a virtual space. The image generation unit can also provide the image in AR, allowing the wrestlers to experience an actual bout. This makes it easier for the wrestlers to check tactics in real space. The image generation unit can also provide the image in VR or AR, allowing the wrestlers to experience an actual bout. This makes it easier for the wrestlers to check tactics in a virtual space or real space. This makes it easier for the wrestlers to experience an actual bout using VR or AR.

[0041] The video generation unit can add comments from other wrestlers and coaches to the video and provide feedback from multiple perspectives. For example, the video generation unit can add comments from other wrestlers and coaches to the video and provide feedback from multiple perspectives. This allows wrestlers to check their tactics from multiple perspectives. The video generation unit can also add comments from other wrestlers and coaches to the video and provide feedback from multiple perspectives. This makes it easier for wrestlers to check their tactics from multiple perspectives. The video generation unit can also add comments from other wrestlers and coaches to the video and provide feedback from multiple perspectives. This makes it easier for wrestlers to check their tactics from multiple perspectives and imagine their movements in an actual bout. This makes it easier for wrestlers to check their tactics from multiple perspectives and imagine their movements in an actual bout. This makes it easier for wrestlers to add comments from other wrestlers and coaches to provide feedback from multiple perspectives.

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

[0043] The advice providing system can further include a voice recognition unit. The voice recognition unit can recognize voice instructions from the wrestlers and their coaches and operate the system based on them. For example, if a wrestler instructs, "Show me the data of my next opponent," the voice recognition unit recognizes the instruction, obtains the necessary information from the data collection unit, and displays it. Also, if a coach instructs, "Change the training plan," the voice recognition unit recognizes the instruction, sends instructions to the analysis unit, and regenerates the training plan. Furthermore, the voice recognition unit can recognize voice instructions from the wrestlers and their coaches in real time, allowing the system to be operated quickly. This allows the wrestlers and their coaches to operate the system without using their hands and efficiently prepare for bouts.

[0044] The advice providing system may further include an environmental data collection unit. The environmental data collection unit may collect environmental data (e.g., temperature, humidity, and air pressure) of the location where the bout is taking place. For example, if the temperature of the location where the bout is taking place is high, the analysis unit may take that data into consideration and provide the wrestler with advice on hydration. Also, if the humidity is high, the analysis unit may use that data to suggest an appropriate warm-up method for the wrestler. Furthermore, if there are fluctuations in air pressure, the analysis unit may use that data to provide advice on managing the wrestler's physical condition. This allows the wrestler to make optimal preparations for the environmental conditions.

[0045] The advice providing system can further include a health management unit. The health management unit can comprehensively manage the health status of sumo wrestlers and provide advice for maintaining their health. For example, the health management unit can collect dietary and sleep data of sumo wrestlers and provide it to the analysis unit. The health management unit can also suggest nutritionally balanced meal plans and appropriate rest methods based on data on the wrestlers' health status. Furthermore, the health management unit can monitor the wrestlers' health status in real time and issue an alert if an abnormality is detected. This allows wrestlers to train while maintaining their health.

[0046] The advice providing system can further include a competition analysis unit. The competition analysis unit can analyze tactics and techniques in other sports and provide advice that can be applied to sumo wrestlers. For example, it can analyze judo or wrestling techniques and identify techniques that can be applied to sumo. The competition analysis unit can also analyze training methods in other sports and incorporate them into sumo wrestlers' training. Furthermore, the competition analysis unit can analyze psychological tactics in other sports and apply them to sumo wrestlers' mental training. This allows sumo wrestlers to utilize knowledge from other sports to improve their tactics and training.

[0047] The advice providing system may further include a virtual reality unit. The virtual reality unit allows sumo wrestlers to simulate bouts in a virtual space. For example, a sumo wrestler wears a VR headset and simulates a bout with an opponent in the virtual space. The virtual reality unit also allows sumo wrestlers to train in the virtual space. For example, a training program is provided in the virtual space, allowing the wrestlers to train in an environment similar to an actual bout. Furthermore, the virtual reality unit allows the wrestlers to review their bouts in the virtual space and check their tactics. This allows the wrestlers to improve their tactics through simulations in the virtual space.

[0048] The advice providing system may further include a data visualization unit. The data visualization unit visually displays collected data to enable wrestlers and their coaches to easily understand it. For example, the data visualization unit may display the wrestlers' training data and match data in graphs and charts. The data visualization unit may also visually display the wrestlers' physical condition data and psychological state data, allowing the wrestlers and their coaches to adjust their tactics based on the data. Furthermore, the data visualization unit may display the collected data in real time, allowing the wrestlers and their coaches to immediately grasp the situation. This allows the wrestlers and their coaches to efficiently prepare for bouts based on the data.

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

[0050] Step 1: The data collection unit collects data on past matches, such as match results, training data, physical condition data, and data on the wrestlers' psychological state. Step 2: The analysis unit analyzes the data collected by the data collection unit, for example, by using statistical analysis or machine learning algorithms. Step 3: The advice generator generates specific advice based on the results of the analysis by the analyzer, such as tactical advice or training advice. Step 4: The video generation unit generates a video of the bout based on the advice generated by the advice generation unit. For example, the video is a simulation video or an actual game video.

[0051] (Example 2) The advice providing system according to an embodiment of the present invention uses AI to provide sumo wrestlers and their coaches with specific advice on how to win the next match, allowing the wrestlers and their coaches to efficiently prepare for the match.

[0052] The advice providing system according to the embodiment includes a data collection unit, an analysis unit, an advice generation unit, and a video generation unit. The data collection unit collects data on past bouts. For example, it collects match results, training data, physical condition data, etc. The data collection unit can also collect data on the psychological state of the wrestlers. The analysis unit analyzes the data collected by the data collection unit. For example, it analyzes the data using statistical analysis or a machine learning algorithm. The advice generation unit generates specific advice based on the results of the analysis by the analysis unit. For example, it generates tactical advice or training advice. The video generation unit generates video of the bout based on the advice generated by the advice generation unit. For example, it generates simulation video or video of an actual match. This allows the advice providing system to enable wrestlers and their coaches to efficiently prepare for bouts.

[0053] The data collection unit can also collect data on physical and psychological states and perform machine learning based on the data. The data collection unit, for example, collects data on the physical state of the wrestlers (e.g., heart rate and sleep time) and combines it with past bout data to perform machine learning. This allows for an analysis of the impact that physical state has on the results of the bout. The data collection unit also quantifies the wrestlers' psychological states (e.g., stress levels and motivation) and combines it with past bout data to perform machine learning. This allows for an analysis of the impact that psychological states have on winning or losing. Furthermore, the data collection unit collects data on the wrestlers' physical and psychological states in real time and combines it with past bout data to perform machine learning. This allows for an analysis of the impact that fluctuations in physical and psychological states have on the results of the bout. This allows for the provision of advice that takes into account the wrestlers' physical and psychological states.

[0054] The data collection unit can also incorporate dietary or training data to make comprehensive performance predictions. The data collection unit, for example, collects dietary data (e.g., calorie intake and nutritional balance) of sumo wrestlers and combines it with past bout data to perform machine learning. This allows for an analysis of the impact of diet on performance. The data collection unit also collects training data (e.g., training content and intensity) of sumo wrestlers and combines it with past bout data to perform machine learning. This allows for an analysis of the impact of training on performance. Furthermore, the data collection unit collects dietary and training data of sumo wrestlers in real time and combines it with past bout data to perform machine learning. This allows for an analysis of the impact of fluctuations in diet and training on performance. This makes it possible to make performance predictions that take into account the dietary and training of sumo wrestlers.

[0055] The data collection unit can use the emotion estimation function to analyze emotions during past bouts and learn the impact of emotional fluctuations on victory or defeat. The data collection unit, for example, collects facial expression data of wrestlers during past bouts and analyzes their emotions using the emotion estimation function. This allows learning of the impact of emotional fluctuations on victory or defeat. The data collection unit also collects audio data of wrestlers during past bouts and analyzes their emotions using the emotion estimation function. This allows learning of the impact of emotional fluctuations on victory or defeat. Furthermore, the data collection unit collects biological data of wrestlers during past bouts (for example, heart rate and electrodermal activity) and analyzes their emotions using the emotion estimation function. This allows learning of the impact of emotional fluctuations on victory or defeat.

[0056] The analysis unit can also incorporate data from other martial arts or sports to learn commonalities in techniques and tactics between different sports. For example, the analysis unit collects data from other martial arts (e.g., judo or boxing) and combines it with past bout data to perform machine learning. This allows for learning of commonalities in techniques and tactics between different sports. The analysis unit also collects data from other sports (e.g., wrestling or rugby) and combines it with past bout data to perform machine learning. This allows for learning of commonalities in techniques and tactics between different sports. Furthermore, the analysis unit collects data from other martial arts or sports in real time and combines it with past bout data to perform machine learning. This allows for learning of commonalities in techniques and tactics between different sports.

[0057] The analysis unit can generate a personalized training plan based on the individual characteristics of each sumo wrestler. The analysis unit, for example, analyzes the individual characteristics of each sumo wrestler (for example, physique or skill level) and generates a personalized training plan based on the results. This provides training that is optimal for the wrestler's characteristics. The analysis unit also analyzes the wrestler's past training data and generates a personalized training plan based on the results. This provides training that is optimal for the wrestler's past training history. Furthermore, the analysis unit collects the individual characteristics and past training data of each wrestler in real time and generates a personalized training plan based on the results. This provides training that is optimal for the wrestler's characteristics and training history. This allows for the generation of a training plan based on the individual characteristics of each wrestler.

[0058] The analysis unit can use the emotion estimation function to analyze the emotions of sumo wrestlers when they look back on past bouts and provide feedback to elicit positive emotions. The analysis unit, for example, collects facial expression data of sumo wrestlers when they look back on past bouts and analyzes their emotions using the emotion estimation function. This allows for providing feedback to elicit positive emotions. The analysis unit also collects audio data of sumo wrestlers when they look back on past bouts and analyzes their emotions using the emotion estimation function. This allows for providing feedback to elicit positive emotions. The analysis unit also collects biometric data (for example, heart rate and electrodermal activity) of wrestlers when they look back on past bouts and analyzes their emotions using the emotion estimation function. This allows for providing feedback to elicit positive emotions. This allows for providing feedback to elicit positive emotions of wrestlers when they look back on past bouts.

[0059] The advice generation unit can provide advice that reflects the physical and psychological states of the wrestlers on the day in real time. For example, the advice generation unit collects data on the wrestlers' physical conditions on the day (e.g., heart rate and sleep hours) in real time and provides advice based on that data. This allows for the proposal of tactics that are optimal for their physical conditions. The advice generation unit also analyzes the wrestlers' psychological conditions on the day (e.g., stress level and motivation) in real time and provides advice based on that data. This allows for the proposal of tactics that are optimal for their psychological conditions. The advice generation unit also collects data on the wrestlers' physical and psychological conditions on the day in real time and provides advice based on that data. This allows for the proposal of tactics that are optimal for their physical and psychological conditions. This allows for the provision of advice that reflects the wrestlers' physical and psychological conditions on the day in real time.

[0060] The advice generation unit incorporates the opposing wrestler's latest training data or match data, allowing it to provide more accurate advice. The advice generation unit, for example, collects the opposing wrestler's latest training data (e.g., training content and intensity) and provides advice based on that data. This allows it to propose tactics that are optimal for the opponent's latest condition. The advice generation unit also collects the opposing wrestler's latest match data (e.g., recent wins and losses and frequency of technique use) and provides advice based on that data. This allows it to propose countermeasures that are optimal for the opponent's latest tactics. Furthermore, the advice generation unit collects the opposing wrestler's latest training data and match data in real time and provides advice based on that data. This allows it to propose countermeasures that are optimal for the opponent's latest condition and tactics. This allows it to provide highly accurate advice that incorporates the opposing wrestler's latest training data and match data.

[0061] The advice generation unit uses the emotion estimation function to provide advice according to the emotional state of the wrestler and also provide mental support. For example, the advice generation unit analyzes the emotional state of the wrestler (e.g., stress level or motivation) in real time and provides advice based on that. This provides mental support. The advice generation unit also analyzes the emotional state of the wrestler and provides advice to bring out positive emotions. For example, it suggests encouraging messages or relaxation methods. Furthermore, the advice generation unit analyzes the emotional state of the wrestler in real time and provides advice based on that. This provides mental support and improves the wrestler's performance. This makes it possible to provide advice and mental support according to the emotional state of the wrestler.

[0062] The advice generation unit can provide advice in multiple scenarios, allowing the sumo wrestler to try out different tactics. The advice generation unit, for example, provides advice for tomorrow's bout in multiple scenarios, allowing the sumo wrestler to try out different tactics. For example, it proposes scenarios that change the timing of the initial attack or the selection of techniques. The advice generation unit also enables the sumo wrestler to try out different tactics based on the multiple scenarios. For example, it proposes multiple countermeasures against the opponent's specialty techniques, allowing the wrestler to select the optimal tactic. Furthermore, the advice generation unit provides advice for tomorrow's bout in multiple scenarios, allowing the sumo wrestler to try out different tactics. This helps the wrestler find the optimal tactic. This allows the wrestler to try out different tactics.

[0063] The advice generation unit can incorporate feedback from other wrestlers and coaches to improve the accuracy of the advice. The advice generation unit, for example, collects feedback from other wrestlers and coaches and provides advice based on that. This improves the accuracy of the advice. The advice generation unit also incorporates the opinions of other wrestlers and coaches to adjust the content of the advice. For example, the advice is improved based on past experience and knowledge. Furthermore, the advice generation unit collects feedback from other wrestlers and coaches in real time and provides advice based on that. This improves the accuracy of the advice. This improves the accuracy of the advice that incorporates feedback from other wrestlers and coaches.

[0064] The advice generation unit can use the emotion estimation function to analyze the emotional reaction of the wrestler when receiving advice and provide advice that will elicit the most positive reaction. The advice generation unit, for example, analyzes the emotional reaction of the wrestler when receiving advice in real time and provides advice that will elicit the most positive reaction. For example, the advice generation unit adjusts the advice based on the emotion score. The advice generation unit also analyzes the emotional reaction of the wrestler and provides advice to elicit positive emotions. For example, it suggests encouraging messages or relaxation methods. Furthermore, the advice generation unit monitors the emotional reaction of the wrestler when receiving advice in real time and provides advice that will elicit the most positive reaction. This provides psychological support to the wrestler. This makes it possible to analyze the emotional reaction of the wrestler and provide advice that will elicit the most positive reaction.

[0065] The image generation unit also generates images from the perspective of the wrestlers, allowing for a more realistic simulation of an actual bout. The image generation unit, for example, generates images from the perspective of the wrestlers and simulates an actual bout. This allows the wrestlers to check the tactics from their own perspective. The image generation unit also generates images from the perspective of the wrestlers and simulates the bout. This makes it easier for the wrestlers to check the tactics from their own perspective. The image generation unit also generates images from the perspective of the wrestlers and simulates the bout. This makes it easier for the wrestlers to check the tactics from their own perspective and imagine the movements in an actual bout. This allows for a more realistic simulation of an actual bout by generating images from the perspective of the wrestlers.

[0066] The video generation unit can overlay the vital data of the wrestlers onto the video and provide real-time feedback. For example, the video generation unit can overlay the vital data of the wrestlers (heart rate, respiratory rate, etc.) onto the video and provide real-time feedback. This allows the wrestlers to check their tactics while checking their own physical condition. The video generation unit can also overlay the vital data of the wrestlers onto the video and provide real-time feedback. This makes it easier for the wrestlers to check their tactics while checking their own physical condition. The video generation unit can also overlay the vital data of the wrestlers onto the video and provide real-time feedback. This makes it easier for the wrestlers to check their tactics while checking their own physical condition, and to visualize their movements in an actual bout. This makes it possible to provide video with the vital data of the wrestlers overlaid on it and provide real-time feedback.

[0067] The video generation unit can use the emotion estimation function to analyze the emotions felt by sumo wrestlers when they watch the video and generate videos that evoke positive emotions. For example, the video generation unit analyzes the emotions felt by sumo wrestlers when they watch the video and generates videos that evoke positive emotions. This allows the wrestlers to approach the bout with confidence. The video generation unit also analyzes the emotions felt by sumo wrestlers when they watch the video and generates videos that evoke positive emotions. This allows the wrestlers to approach the bout in a relaxed manner. The video generation unit also analyzes the emotions felt by sumo wrestlers when they watch the video and generates videos that evoke positive emotions. This allows the wrestlers to approach the bout with concentration. This allows the wrestlers to generate videos that evoke positive emotions.

[0068] The image generation unit can provide the image in VR or AR, allowing the wrestlers to experience an actual bout. For example, the image generation unit can provide the image in VR, allowing the wrestlers to experience an actual bout. This makes it easier for the wrestlers to check tactics in a virtual space. The image generation unit can also provide the image in AR, allowing the wrestlers to experience an actual bout. This makes it easier for the wrestlers to check tactics in real space. The image generation unit can also provide the image in VR or AR, allowing the wrestlers to experience an actual bout. This makes it easier for the wrestlers to check tactics in a virtual space or real space. This makes it easier for the wrestlers to experience an actual bout using VR or AR.

[0069] The video generation unit can add comments from other wrestlers and coaches to the video and provide feedback from multiple perspectives. For example, the video generation unit can add comments from other wrestlers and coaches to the video and provide feedback from multiple perspectives. This allows wrestlers to check their tactics from multiple perspectives. The video generation unit can also add comments from other wrestlers and coaches to the video and provide feedback from multiple perspectives. This makes it easier for wrestlers to check their tactics from multiple perspectives. The video generation unit can also add comments from other wrestlers and coaches to the video and provide feedback from multiple perspectives. This makes it easier for wrestlers to check their tactics from multiple perspectives and imagine their movements in an actual bout. This makes it easier for wrestlers to check their tactics from multiple perspectives and imagine their movements in an actual bout. This makes it easier for wrestlers to add comments from other wrestlers and coaches to provide feedback from multiple perspectives.

[0070] The video generation unit uses the emotion estimation function to monitor the emotional reactions of the wrestlers when watching the video in real time, and continuously generate optimal videos. For example, the video generation unit monitors the emotional reactions of the wrestlers when watching the video in real time, and continuously generates optimal videos. This allows the wrestlers to approach the bout with positive emotions. The video generation unit also monitors the emotional reactions of the wrestlers when watching the video in real time, and continuously generates optimal videos. This allows the wrestlers to approach the bout in a relaxed manner. The video generation unit also monitors the emotional reactions of the wrestlers when watching the video in real time, and continuously generates optimal videos. This allows the wrestlers to approach the bout with concentration. This allows the wrestlers to monitor the emotional reactions of the wrestlers in real time, and continuously generate optimal videos.

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

[0072] The advice providing system can further include a voice recognition unit. The voice recognition unit can recognize voice instructions from the wrestlers and their coaches and operate the system based on them. For example, if a wrestler instructs, "Show me the data of my next opponent," the voice recognition unit recognizes the instruction, obtains the necessary information from the data collection unit, and displays it. Also, if a coach instructs, "Change the training plan," the voice recognition unit recognizes the instruction, sends instructions to the analysis unit, and regenerates the training plan. Furthermore, the voice recognition unit can recognize voice instructions from the wrestlers and their coaches in real time, allowing the system to be operated quickly. This allows the wrestlers and their coaches to operate the system without using their hands and efficiently prepare for bouts.

[0073] The advice providing system may further include an environmental data collection unit. The environmental data collection unit may collect environmental data (e.g., temperature, humidity, and air pressure) of the location where the bout is taking place. For example, if the temperature of the location where the bout is taking place is high, the analysis unit may take that data into consideration and provide the wrestler with advice on hydration. Also, if the humidity is high, the analysis unit may use that data to suggest an appropriate warm-up method for the wrestler. Furthermore, if there are fluctuations in air pressure, the analysis unit may use that data to provide advice on managing the wrestler's physical condition. This allows the wrestler to make optimal preparations for the environmental conditions.

[0074] The advice providing system may further include a social media analysis unit. The social media analysis unit may collect and analyze information on social media related to wrestlers and opposing wrestlers. For example, the social media analysis unit may collect supportive messages from fans for wrestlers and the latest information related to opposing wrestlers, and provide this to the analysis unit. The social media analysis unit may also identify information that may affect the psychological state of wrestlers and provide this to the analysis unit. Furthermore, the social media analysis unit may collect positive information to increase the motivation of wrestlers and provide this to the advice generation unit. This allows wrestlers to adjust their tactics based on the latest information and increase their motivation.

[0075] The advice providing system can further include a virtual coaching unit. The virtual coaching unit can provide coaching in real time when a wrestler trains at home or at a training facility. For example, the virtual coaching unit can analyze video of the wrestler training and provide advice on correcting form and improving technique. The virtual coaching unit can also collect training data of the wrestler in real time and provide it to the analysis unit. Furthermore, the virtual coaching unit can analyze the emotional state of the wrestler during training and provide feedback to maintain motivation. This allows the wrestler to train efficiently and improve their technique.

[0076] The advice providing system can further include a health management unit. The health management unit can comprehensively manage the health status of sumo wrestlers and provide advice for maintaining their health. For example, the health management unit can collect dietary and sleep data of sumo wrestlers and provide it to the analysis unit. The health management unit can also suggest nutritionally balanced meal plans and appropriate rest methods based on data on the wrestlers' health status. Furthermore, the health management unit can monitor the wrestlers' health status in real time and issue an alert if an abnormality is detected. This allows wrestlers to train while maintaining their health.

[0077] The advice providing system can further include a competition analysis unit. The competition analysis unit can analyze tactics and techniques in other sports and provide advice that can be applied to sumo wrestlers. For example, it can analyze judo or wrestling techniques and identify techniques that can be applied to sumo. The competition analysis unit can also analyze training methods in other sports and incorporate them into sumo wrestlers' training. Furthermore, the competition analysis unit can analyze psychological tactics in other sports and apply them to sumo wrestlers' mental training. This allows sumo wrestlers to utilize knowledge from other sports to improve their tactics and training.

[0078] The advice providing system can further include an emotion feedback unit. The emotion feedback unit can analyze the emotional reaction of a sumo wrestler when he or she receives advice and provide optimal feedback. For example, the emotion feedback unit collects facial expression data when the wrestler receives advice and analyzes the emotion using an emotion estimation function. This allows for providing feedback that makes the wrestler feel positive. The emotion feedback unit also collects audio data when the wrestler receives advice and analyzes the emotion using the emotion estimation function. This allows for providing feedback that increases the wrestler's motivation. Furthermore, the emotion feedback unit collects biometric data (e.g., heart rate and electrodermal activity) when the wrestler receives advice and analyzes the emotion using the emotion estimation function. This allows for providing feedback that helps the wrestler to relax and prepare for the bout. This allows for providing feedback that elicits the optimal emotional reaction when the wrestler receives advice.

[0079] The advice providing system may further include a virtual reality unit. The virtual reality unit allows sumo wrestlers to simulate bouts in a virtual space. For example, a sumo wrestler wears a VR headset and simulates a bout with an opponent in the virtual space. The virtual reality unit also allows sumo wrestlers to train in the virtual space. For example, a training program is provided in the virtual space, allowing the wrestlers to train in an environment similar to an actual bout. Furthermore, the virtual reality unit allows the wrestlers to review their bouts in the virtual space and check their tactics. This allows the wrestlers to improve their tactics through simulations in the virtual space.

[0080] The advice providing system can further include an emotion monitoring unit. The emotion monitoring unit can monitor the emotional state of the wrestler in real time and provide appropriate advice. For example, the emotion monitoring unit can collect facial expression data of the wrestler in real time and analyze the emotion using an emotion estimation function. As a result, if the wrestler is feeling stressed, it can suggest relaxation methods. The emotion monitoring unit can also collect audio data of the wrestler in real time and analyze the emotion using the emotion estimation function. As a result, it can provide advice to increase the wrestler's motivation. Furthermore, the emotion monitoring unit can collect biological data of the wrestler (for example, heart rate and electrodermal activity) in real time and analyze the emotion using the emotion estimation function. As a result, it can provide advice to help the wrestler concentrate during the bout. As a result, the emotional state of the wrestler can be monitored in real time and optimal advice can be provided.

[0081] The advice providing system may further include a data visualization unit. The data visualization unit visually displays collected data to enable wrestlers and their coaches to easily understand it. For example, the data visualization unit may display the wrestlers' training data and match data in graphs and charts. The data visualization unit may also visually display the wrestlers' physical condition data and psychological state data, allowing the wrestlers and their coaches to adjust their tactics based on the data. Furthermore, the data visualization unit may display the collected data in real time, allowing the wrestlers and their coaches to immediately grasp the situation. This allows the wrestlers and their coaches to efficiently prepare for bouts based on the data.

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

[0083] Step 1: The data collection unit collects data on past matches, such as match results, training data, physical condition data, and data on the wrestlers' psychological state. Step 2: The analysis unit analyzes the data collected by the data collection unit, for example, by using statistical analysis or machine learning algorithms. Step 3: The advice generator generates specific advice based on the results of the analysis by the analyzer, such as tactical advice or training advice. Step 4: The video generation unit generates a video of the bout based on the advice generated by the advice generation unit. For example, the video is a simulation video or an actual game video.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0143] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A data collection department that collects data on past initiatives; an analysis unit that analyzes the data collected by the data collection unit; an advice generation unit that generates specific advice based on the results of the analysis by the analysis unit; a video generation unit that generates a video of the effort based on the advice generated by the advice generation unit; A system characterized by:

2. The data collection unit It also collects data on physical and psychological states and performs machine learning based on that data.

2. The system of claim 1.

3. The data collection unit Incorporates nutrition and training data to provide comprehensive performance predictions 2. The system of claim 1.

4. The data collection unit Analyze emotions during past matches and learn how fluctuations in emotions affect winning and losing.

2. The system of claim 1.

5. The analysis unit Incorporating data from other martial arts and sports to learn commonalities in techniques and tactics between different disciplines 2. The system of claim 1.

6. The analysis unit Generate personalized training plans based on each wrestler's individual characteristics 2. The system of claim 1.

7. The analysis unit Analyzing the emotions wrestlers feel when reflecting on past matches and providing feedback to elicit positive emotions 2. The system of claim 1.

8. The advice generation unit Providing advice that reflects the wrestler's physical and psychological state on the day in real time 2. The system of claim 1.

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