system

Abstract

The system according to the embodiment aims to enable sumo wrestlers and their coaches to efficiently obtain specific strategies for the next day's bouts. [Solution] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects data on past bouts of sumo wrestlers and data on their opponents. The analysis unit analyzes the data collected by the collection unit and proposes specific strategies for the next day's bout. The proposal unit provides the proposals obtained by the analysis unit to the sumo wrestlers and their coaches.

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 each match, 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 obtain specific strategies for the next day's bouts. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects data on past bouts of sumo wrestlers and data on their opponents. The analysis unit analyzes the data collected by the collection unit and proposes specific strategies for the next day's bout. The proposal unit provides the proposals obtained by the analysis unit to the sumo wrestlers and their coaches. [Effects of the Invention]

[0007] The system according to the embodiment allows sumo wrestlers and their coaches to efficiently obtain specific strategies for the next day's bouts. [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) An AI system according to an embodiment of the present invention is a system that enables sumo wrestlers and their coaches to efficiently develop strategies for their daily matches. This AI system collects data on the wrestlers' past matches and data on their opponents, analyzes this data, and proposes an optimal strategy for the next day's match. For example, data such as the wrestlers' weight, height, types of techniques, and past wins and losses is collected, and the AI ​​analyzes this data. Based on the analysis results, specific strategies are proposed to the wrestlers and their coaches. This allows the wrestlers and their coaches to efficiently prepare for the next day's match and reduce their mental and physical burden. This allows the AI ​​system to efficiently develop strategies for the wrestlers and their coaches' daily matches and reduce their mental and physical burden.

[0029] The AI ​​system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects data on the wrestler's past matches and data on his / her opponents. For example, the collection unit collects data such as the wrestler's weight, height, type of technique, and past wins and losses. The collection unit measures the wrestler's weight and height and stores the data in a database. The collection unit can also analyze video data of past matches and record the type of technique and success rate. The collection unit also collects data on the wrestler's opponents to understand the wrestler's match record and technique tendencies. The analysis unit analyzes the data collected by the collection unit and proposes an optimal strategy for the next day's match. For example, the analysis unit considers the wrestler's physical condition and past match record to propose which technique should be used and what position the wrestler should be in during the match. The analysis unit uses AI to analyze the data and identify the wrestler's strengths and weaknesses. For example, the analysis unit calculates the success rate of a specific technique based on the wrestler's past match data and determines whether to use that technique. The proposal unit provides the proposal obtained by the analysis unit to the wrestler and / or his / her coach. For example, the suggestion unit presents specific strategies to sumo wrestlers and supports their preparations for bouts. The suggestion unit generates suggestions using AI and provides appropriate advice to sumo wrestlers and their coaches. For example, the suggestion unit provides specific advice to a sumo wrestler, such as, "Sumo wrestler B's thrusts with his right hand are strong, so he should strengthen his pushes with his left hand." This allows the wrestlers to develop specific strategies for the next day's bout, thereby reducing their mental burden. As a result, the AI ​​system according to the embodiment allows sumo wrestlers and their coaches to efficiently develop strategies for each daily bout, thereby reducing their mental and physical burden.

[0030] The collection unit can collect data such as the weight, height, type of technique, past wins and losses, and success rates of techniques of the wrestlers. For example, the collection unit periodically measures the weight of the wrestlers and stores the data in a database. For example, the collection unit measures the weight of the wrestlers every morning and records the data. The collection unit can also measure the height of the wrestlers and store the data in a database. For example, the collection unit periodically measures the height of the wrestlers and records the data. The collection unit can also collect the type and success rate of the wrestlers' techniques. For example, the collection unit analyzes video data of past matches and records the type and success rate of the techniques. The collection unit can also collect data on the wrestlers' past wins and losses and store the data in a database. For example, the collection unit records the results of past matches of the wrestlers and analyzes the data. By collecting detailed data on the wrestlers, the collection unit can perform more accurate analysis. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection department can input sumo wrestlers' weight data into AI and analyze weight fluctuations.

[0031] The analysis unit can propose specific strategies based on the collected data, taking into account the wrestler's physical condition and past match results. The analysis unit, for example, proposes an optimal strategy for the next day's bout based on the wrestler's physical condition data. For example, the analysis unit analyzes data such as the wrestler's body temperature, heart rate, and fatigue level, and proposes an aggressive strategy if the wrestler is in good physical condition and a defensive strategy if the wrestler is in poor physical condition. The analysis unit can also propose an optimal strategy against an opponent based on the wrestler's past match results. For example, the analysis unit can analyze the wrestler's past match results and propose which techniques are effective against a specific opponent. Furthermore, the analysis unit can propose an optimal strategy for the next day's bout based on the success rate of the wrestler's techniques. For example, the analysis unit can analyze the success rate of the wrestler's techniques and determine whether to use a technique with a high success rate. This allows the analysis unit to propose a more effective strategy by taking into account the wrestler's physical condition and past match results. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the physical condition of sumo wrestlers into the AI ​​and suggest strategies based on their physical condition.

[0032] The proposal unit can provide the proposed strategies to the wrestlers and their masters. For example, the proposal unit can present specific strategies to the wrestlers and support their preparations for the bout. For example, the proposal unit can provide specific advice to the wrestlers, such as, "Wrestler B's thrusts with his right hand are strong, so he should strengthen his pushes with his left hand." The proposal unit can also present specific strategies to the masters and provide appropriate advice to the wrestlers. For example, the proposal unit can provide advice to the masters, such as, "Wrestler A is in good physical condition, so he should adopt an aggressive strategy." The proposal unit can also present the proposed strategies using a digital device. For example, the proposal unit can present strategies to the wrestlers and their masters using a smartphone or tablet. This allows the wrestlers and their masters to prepare for the bout based on the proposed strategies. Some or all of the above-described processing by the proposal unit may be performed using, for example, AI, or may be performed without AI. For example, the proposal unit can provide strategies generated using AI to the wrestlers and their masters.

[0033] The suggestion unit may have an interface through which the wrestlers and the coach receive the suggestions. The suggestion unit presents the suggestions to the wrestlers and the coach using, for example, a device with a touch screen. For example, the suggestion unit may use a tablet to display strategies to the wrestlers and the coach. The suggestion unit may also provide suggestions to the wrestlers and the coach using voice recognition technology. For example, the suggestion unit may use a voice assistant to explain strategies to the wrestlers and the coach. The suggestion unit may also provide suggestions to the wrestlers and the coach using a mobile application. For example, the suggestion unit may present strategies to the wrestlers and the coach through a smartphone app. This makes it easier for the wrestlers and the coach to receive suggestions. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit may provide strategies generated using AI through a touch screen or a voice assistant.

[0034] The AI ​​system further includes a feedback unit that collects post-bout results and uses them for the next analysis. The feedback unit, for example, collects win / loss results after a bout and stores them in a database. For example, the feedback unit records the results of the wrestlers' matches and uses the data for analysis. The feedback unit can also collect the success rate of techniques after a bout and use the data for analysis. For example, the feedback unit records the success rate of techniques after a bout and uses the data for analysis. The feedback unit can also collect data on the wrestlers' physical condition after a bout and use the data for analysis. For example, the feedback unit collects data such as the wrestlers' body temperature, heart rate, and fatigue level after a bout and uses the data for analysis. As a result, the feedback unit reflects the post-bout results in the next analysis, thereby improving the accuracy of the analysis. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without AI. For example, the feedback unit can input post-bout data into AI and use it for the next analysis.

[0035] The collection unit can analyze the wrestler's past bout data and select the optimal data collection method. For example, the collection unit can analyze the wrestler's past bout data and prioritize collecting data related to a specific technique. For example, the collection unit can analyze the wrestler's past bout data, calculate the success rate of a specific technique, and prioritize collecting data related to that technique. The collection unit can also prioritize collecting data related to a specific opponent based on the wrestler's past match results. For example, the collection unit can analyze the wrestler's past match results and prioritize collecting data related to a specific opponent. Furthermore, the collection unit can also collect data related to factors affecting the wrestler's physical condition by taking the wrestler's past physical condition data into consideration. For example, the collection unit can analyze the wrestler's past physical condition data and prioritize collecting data related to factors affecting the wrestler's physical condition. This enables the collection unit to analyze the past bout data and collect data more effectively. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the wrestler's past bout data into AI and select the optimal data collection method.

[0036] When collecting data, the collection unit can filter the data based on the wrestler's current physical condition and training status. For example, the collection unit collects data when the wrestler is in good physical condition based on the wrestler's current physical condition data. For example, the collection unit analyzes data such as the wrestler's body temperature, heart rate, and fatigue level, and collects data when the wrestler is in good physical condition. The collection unit can also prioritize collecting post-training data taking into account the wrestler's training status. For example, the collection unit collects post-training data of the wrestler and uses that data for analysis. Furthermore, if the wrestler is in poor physical condition, the collection unit can refrain from collecting data and wait until the wrestler's physical condition improves. For example, if the wrestler is in poor physical condition, the collection unit postpones data collection until the next day. This enables the collection unit to collect data according to the wrestler's current condition. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the wrestler's physical condition data into AI and collect data based on the wrestler's physical condition.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the wrestler. For example, when the wrestler is in a specific location, the collection unit prioritizes collecting data related to that location. For example, when the wrestler is at a training facility, the collection unit prioritizes collecting training data at the facility. Furthermore, when the wrestler is traveling, the collection unit can also collect data related to the destination. For example, the collection unit collects training data performed by the wrestler while traveling. Furthermore, when the wrestler is at home, the collection unit can prioritize collecting past training data. For example, the collection unit collects training data performed by the wrestler at home. In this way, the collection unit can collect highly relevant data by taking the geographical location information into account. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the geographical location information of the wrestler into AI and prioritize collecting highly relevant data.

[0038] During data collection, the collection unit can analyze the social media activities of the wrestlers and collect related data. The collection unit, for example, analyzes the wrestlers' social media posts and collects data related to their training. For example, the collection unit analyzes the wrestlers' social media posts and collects information related to their training. The collection unit can also analyze the wrestlers' interactions with their followers and collect data related to their mental states. For example, the collection unit analyzes the wrestlers' interactions with their followers and collects information related to their mental states. Furthermore, the collection unit can determine the optimal timing for collecting data based on the wrestlers' social media activity time. For example, the collection unit analyzes the wrestlers' social media activity time and determines the optimal timing for collecting data. This allows the collection unit to collect related data by analyzing the social media activity. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the wrestlers' social media data into AI to collect related data.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the wrestler's physical condition and past match results. For example, the analysis unit performs a detailed analysis based on the wrestler's physical condition data if the wrestler is in good physical condition and a brief analysis if the wrestler is in poor physical condition. For example, the analysis unit analyzes data such as the wrestler's body temperature, heart rate, and fatigue level, and performs a detailed analysis if the wrestler is in good physical condition and a brief analysis if the wrestler is in poor physical condition. The analysis unit can also adjust the level of detail of the analysis of an opponent based on the wrestler's past match results. For example, the analysis unit analyzes the wrestler's past match results and performs a detailed analysis of a specific opponent. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the success rate of the wrestler's techniques. For example, the analysis unit analyzes the success rate of the wrestler's techniques and performs a detailed analysis of techniques with a high success rate. This enables the analysis unit to perform an analysis based on the wrestler's physical condition and past match results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the physical condition of sumo wrestlers into the AI ​​and adjust the level of detail of the analysis based on their physical condition.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the type of technique used by the wrestler and the characteristics of the opponent. The analysis unit, for example, selects an optimal analysis algorithm depending on the wrestler's specialty technique. For example, the analysis unit analyzes the wrestler's specialty technique and selects an analysis algorithm optimal for that technique. The analysis unit can also apply different analysis algorithms depending on the characteristics of the opponent. For example, the analysis unit analyzes the characteristics of the opponent and applies an analysis algorithm depending on the characteristics. Furthermore, the analysis unit can adjust the level of detail of the analysis depending on the type of technique used by the wrestler. For example, the analysis unit analyzes the type of technique used by the wrestler and adjusts the level of detail of the analysis depending on the technique. This enables the analysis unit to perform analysis depending on the type of technique and the characteristics of the opponent. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the type of technique used by the wrestler and the characteristics of the opponent into AI and apply the optimal analysis algorithm.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the wrestler's past training data. For example, the analysis unit prioritizes analysis of important data based on the wrestler's past training data. For example, the analysis unit analyzes the wrestler's past training data and prioritizes analysis of important data. The analysis unit can also determine the priority of analysis based on the wrestler's training frequency. For example, the analysis unit analyzes the wrestler's training frequency and prioritizes analysis of training data with high frequency. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the wrestler's training results. For example, the analysis unit analyzes the wrestler's training results and analyzes training data with high results in detail. This allows the analysis unit to perform analysis based on the priority of past training data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the wrestler's past training data into AI to determine the priority.

[0042] The analysis unit can improve the accuracy of the analysis by referring to literature related to the sumo wrestler during the analysis. The analysis unit, for example, refers to past research papers on the sumo wrestler to improve the accuracy of the analysis. For example, the analysis unit analyzes past research papers on the sumo wrestler and improves the accuracy of the analysis based on the content. The analysis unit can also adjust the level of detail of the analysis by referring to literature on the sumo wrestler's techniques. For example, the analysis unit analyzes literature on the sumo wrestler's techniques and adjusts the level of detail of the analysis based on the content. Furthermore, the analysis unit can also determine the priority of the analysis by referring to literature on sumo wrestler training methods. For example, the analysis unit analyzes literature on sumo wrestler training methods and determines the priority of the analysis based on the content. In this way, the analysis unit improves the accuracy of the analysis by referring to related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input literature related to the sumo wrestler into AI to improve the accuracy of the analysis.

[0043] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the wrestler's physical condition and past match results. For example, the suggestion unit may provide detailed suggestions based on the wrestler's physical condition data if the wrestler is in good physical condition and provide brief suggestions if the wrestler is in poor physical condition. For example, the suggestion unit may analyze data such as the wrestler's body temperature, heart rate, and fatigue level, and provide detailed suggestions if the wrestler is in good physical condition and provide brief suggestions if the wrestler is in poor physical condition. The suggestion unit may also adjust the level of detail of the proposal for an opponent based on the wrestler's past match results. For example, the suggestion unit may analyze the wrestler's past match results and provide detailed suggestions for specific opponents. Furthermore, the suggestion unit may adjust the level of detail of the proposal based on the success rate of the wrestler's techniques. For example, the suggestion unit may analyze the success rate of the wrestler's techniques and provide detailed suggestions for techniques with a high success rate. This enables the suggestion unit to make suggestions based on the wrestler's physical condition and past match results. Some or all of the above-described processing by the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion department can input data on the physical condition of sumo wrestlers into the AI ​​and adjust the level of detail in the suggestions based on their physical condition.

[0044] When making a proposal, the proposal unit can apply different proposal algorithms depending on the type of technique of the sumo wrestler and the characteristics of the opponent. The proposal unit, for example, selects an optimal proposal algorithm depending on the sumo wrestler's specialty technique. For example, the proposal unit analyzes the sumo wrestler's specialty technique and selects an optimal proposal algorithm for that technique. The proposal unit can also apply different proposal algorithms depending on the characteristics of the opponent. For example, the proposal unit analyzes the characteristics of the opponent and applies a proposal algorithm depending on the characteristics. Furthermore, the proposal unit can adjust the level of detail of the proposal depending on the type of technique of the sumo wrestler. For example, the proposal unit analyzes the type of technique of the sumo wrestler and adjusts the level of detail of the proposal depending on the technique. This enables the proposal unit to make a proposal depending on the type of technique and the characteristics of the opponent. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the type of technique of the sumo wrestler and the characteristics of the opponent into AI and apply the optimal proposal algorithm.

[0045] When making a proposal, the suggestion unit can determine the priority of the proposals based on the wrestler's past training data. For example, the suggestion unit prioritizes important proposals based on the wrestler's past training data. For example, the suggestion unit analyzes the wrestler's past training data and prioritizes important proposals. The suggestion unit can also determine the priority of the proposals based on the wrestler's training frequency. For example, the suggestion unit analyzes the wrestler's training frequency and makes suggestions based on training data with high frequency. Furthermore, the suggestion unit can adjust the level of detail of the proposals based on the wrestler's training results. For example, the suggestion unit analyzes the wrestler's training results and makes suggestions based on training data with high results. This allows the suggestion unit to make proposals in priority order based on past training data. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the wrestler's past training data into AI to determine the priority.

[0046] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to literature related to the sumo wrestler. The proposal unit, for example, refers to past research papers on the sumo wrestler to improve the accuracy of the proposal. For example, the proposal unit analyzes past research papers on the sumo wrestler and improves the accuracy of the proposal based on the content. The proposal unit can also adjust the level of detail of the proposal by referring to literature on sumo wrestler techniques. For example, the proposal unit analyzes literature on sumo wrestler techniques and adjusts the level of detail of the proposal based on the content. The proposal unit can also determine the priority of the proposal by referring to literature on sumo wrestler training methods. For example, the proposal unit analyzes literature on sumo wrestler training methods and determines the priority of the proposal based on the content. In this way, the proposal unit improves the accuracy of the proposal by referring to related literature. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input literature related to the sumo wrestler into AI to improve the accuracy of the proposal.

[0047] When collecting feedback, the feedback unit can adjust the level of detail of the feedback based on the wrestler's physical condition and the results of the bout. For example, based on the wrestler's physical condition data, the feedback unit collects detailed feedback if the wrestler is in good physical condition and collects brief feedback if the wrestler is in poor physical condition. For example, the feedback unit analyzes data such as the wrestler's body temperature, heart rate, and fatigue level, and collects detailed feedback if the wrestler is in good physical condition and collects brief feedback if the wrestler is in poor physical condition. The feedback unit can also collect detailed feedback based on the wrestler's bout results if the bout results are good. For example, the feedback unit analyzes the wrestler's bout results and collects detailed feedback if the bout results are good. This enables the feedback unit to collect feedback according to the wrestler's physical condition and the bout results. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without AI. For example, the feedback unit can input the wrestler's physical condition data and the bout results into AI and adjust the level of detail of the feedback.

[0048] When collecting feedback, the feedback unit can weight the feedback based on the wrestler's past training data. The feedback unit, for example, prioritizes collecting important feedback based on the wrestler's past training data. For example, the feedback unit analyzes the wrestler's past training data and prioritizes collecting important feedback. The feedback unit can also weight the feedback according to the wrestler's training frequency. For example, the feedback unit analyzes the wrestler's training frequency and provides feedback based on training data with high frequency. Furthermore, the feedback unit can adjust the level of detail of the feedback based on the wrestler's training results. For example, the feedback unit analyzes the wrestler's training results and provides feedback based on training data with high results. This allows the feedback unit to provide feedback with weighting based on the past training data. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the wrestler's past training data into AI and perform weighting.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The analysis unit can collect dietary data from sumo wrestlers and propose strategies that take nutritional balance into consideration. For example, the analysis unit can analyze a wrestler's diet and, if a specific nutrient is lacking, propose a meal plan to supplement that nutrient. The analysis unit can also suggest optimal meal timings before a match based on the wrestler's dietary data. Furthermore, the analysis unit can compare a wrestler's dietary data with other wrestlers and propose dietary improvements that will help improve performance. In this way, the analysis unit can utilize a wrestler's dietary data to propose more effective strategies.

[0051] The collection unit can collect the sleep data of the wrestlers and provide it to the analysis unit. For example, the collection unit can record the sleep duration and quality of the wrestlers and store it in a database. The collection unit can also analyze the wrestlers' sleep patterns and evaluate the impact of lack of sleep on their performance. Furthermore, the collection unit can suggest optimal sleep duration and sleeping environment based on the wrestlers' sleep data. This allows the collection unit to utilize the wrestlers' sleep data to perform more accurate analysis.

[0052] The suggestion unit can make suggestions for improving the training menu based on the wrestler's training data. For example, the suggestion unit can analyze the wrestler's training data and, if a particular training is not effective, suggest an alternative training menu. The suggestion unit can also make suggestions for adjusting the intensity and frequency of training based on the wrestler's training data. Furthermore, the suggestion unit can compare the wrestler's training data with other wrestlers and suggest training methods that will help improve performance. In this way, the suggestion unit can utilize the wrestler's training data to suggest a more effective training menu.

[0053] The collection unit can collect data on the training environment of the wrestlers and provide it to the analysis unit. For example, the collection unit records environmental data such as the temperature, humidity, and lighting brightness of the training facility and stores it in a database. The collection unit can also evaluate the impact that changes in the training environment have on performance. Furthermore, the collection unit can suggest the optimal training environment based on the training environment data of the wrestlers. This enables the collection unit to utilize the training environment data of the wrestlers to perform more accurate analysis.

[0054] The feedback unit can evaluate the effectiveness of training based on the wrestler's training data and use the results to improve the wrestler's next training. For example, the feedback unit can analyze the wrestler's training data and evaluate whether a particular training session was effective. The feedback unit can also make suggestions to adjust the intensity and frequency of training based on the wrestler's training data. Furthermore, the feedback unit can compare the wrestler's training data with other wrestlers and suggest training methods that will help improve performance. In this way, the feedback unit can utilize the wrestler's training data to suggest more effective training plans.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The collection unit collects data on the wrestler's past matches and data on his opponents. For example, it collects data such as the wrestler's weight, height, type of technique, and past wins and losses. The collection unit measures the wrestler's weight and height and stores the data in a database. The collection unit can also analyze video data of past matches and record the type of technique and success rate. The collection unit also collects data on the wrestler's opponents to understand the wrestler's match record and technique trends. Step 2: The analysis unit analyzes the data collected by the collection unit and proposes the optimal strategy for the next day's bout. For example, the analysis unit considers the wrestler's physical condition and past match results to propose which technique should be used and what position the wrestler should be in during the bout. The analysis unit uses AI to analyze the data and identify the wrestler's strengths and weaknesses. For example, the analysis unit calculates the success rate of a specific technique based on the wrestler's past bout data and determines whether or not that technique should be used. Step 3: The proposal unit provides the proposals obtained by the analysis unit to the wrestlers and their coaches. For example, the proposal unit may present specific strategies to the wrestlers and support their preparations for the match. The proposal unit uses AI to generate proposals and provide appropriate advice to the wrestlers and their coaches. For example, the proposal unit may provide specific advice to a wrestler such as, "Wrestler B has a strong thrust with his right hand, so he should strengthen his push with his left hand."

[0057] (Example 2) An AI system according to an embodiment of the present invention is a system that enables sumo wrestlers and their coaches to efficiently develop strategies for their daily matches. This AI system collects data on the wrestlers' past matches and data on their opponents, analyzes this data, and proposes an optimal strategy for the next day's match. For example, data such as the wrestlers' weight, height, types of techniques, and past wins and losses is collected, and the AI ​​analyzes this data. Based on the analysis results, specific strategies are proposed to the wrestlers and their coaches. This allows the wrestlers and their coaches to efficiently prepare for the next day's match and reduce their mental and physical burden. This allows the AI ​​system to efficiently develop strategies for the wrestlers and their coaches' daily matches and reduce their mental and physical burden.

[0058] The AI ​​system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects data on the wrestler's past matches and data on his / her opponents. For example, the collection unit collects data such as the wrestler's weight, height, type of technique, and past wins and losses. The collection unit measures the wrestler's weight and height and stores the data in a database. The collection unit can also analyze video data of past matches and record the type of technique and success rate. The collection unit also collects data on the wrestler's opponents to understand the wrestler's match record and technique tendencies. The analysis unit analyzes the data collected by the collection unit and proposes an optimal strategy for the next day's match. For example, the analysis unit considers the wrestler's physical condition and past match record to propose which technique should be used and what position the wrestler should be in during the match. The analysis unit uses AI to analyze the data and identify the wrestler's strengths and weaknesses. For example, the analysis unit calculates the success rate of a specific technique based on the wrestler's past match data and determines whether to use that technique. The proposal unit provides the proposal obtained by the analysis unit to the wrestler and / or his / her coach. For example, the suggestion unit presents specific strategies to sumo wrestlers and supports their preparations for bouts. The suggestion unit generates suggestions using AI and provides appropriate advice to sumo wrestlers and their coaches. For example, the suggestion unit provides specific advice to a sumo wrestler, such as, "Sumo wrestler B's thrusts with his right hand are strong, so he should strengthen his pushes with his left hand." This allows the wrestlers to develop specific strategies for the next day's bout, thereby reducing their mental burden. As a result, the AI ​​system according to the embodiment allows sumo wrestlers and their coaches to efficiently develop strategies for each daily bout, thereby reducing their mental and physical burden.

[0059] The collection unit can collect data such as the weight, height, type of technique, past wins and losses, and success rates of techniques of the wrestlers. For example, the collection unit periodically measures the weight of the wrestlers and stores the data in a database. For example, the collection unit measures the weight of the wrestlers every morning and records the data. The collection unit can also measure the height of the wrestlers and store the data in a database. For example, the collection unit periodically measures the height of the wrestlers and records the data. The collection unit can also collect the type and success rate of the wrestlers' techniques. For example, the collection unit analyzes video data of past matches and records the type and success rate of the techniques. The collection unit can also collect data on the wrestlers' past wins and losses and store the data in a database. For example, the collection unit records the results of past matches of the wrestlers and analyzes the data. By collecting detailed data on the wrestlers, the collection unit can perform more accurate analysis. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection department can input sumo wrestlers' weight data into AI and analyze weight fluctuations.

[0060] The analysis unit can propose specific strategies based on the collected data, taking into account the wrestler's physical condition and past match results. The analysis unit, for example, proposes an optimal strategy for the next day's bout based on the wrestler's physical condition data. For example, the analysis unit analyzes data such as the wrestler's body temperature, heart rate, and fatigue level, and proposes an aggressive strategy if the wrestler is in good physical condition and a defensive strategy if the wrestler is in poor physical condition. The analysis unit can also propose an optimal strategy against an opponent based on the wrestler's past match results. For example, the analysis unit can analyze the wrestler's past match results and propose which techniques are effective against a specific opponent. Furthermore, the analysis unit can propose an optimal strategy for the next day's bout based on the success rate of the wrestler's techniques. For example, the analysis unit can analyze the success rate of the wrestler's techniques and determine whether to use a technique with a high success rate. This allows the analysis unit to propose a more effective strategy by taking into account the wrestler's physical condition and past match results. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the physical condition of sumo wrestlers into the AI ​​and suggest strategies based on their physical condition.

[0061] The proposal unit can provide the proposed strategies to the wrestlers and their masters. For example, the proposal unit can present specific strategies to the wrestlers and support their preparations for the bout. For example, the proposal unit can provide specific advice to the wrestlers, such as, "Wrestler B's thrusts with his right hand are strong, so he should strengthen his pushes with his left hand." The proposal unit can also present specific strategies to the masters and provide appropriate advice to the wrestlers. For example, the proposal unit can provide advice to the masters, such as, "Wrestler A is in good physical condition, so he should adopt an aggressive strategy." The proposal unit can also present the proposed strategies using a digital device. For example, the proposal unit can present strategies to the wrestlers and their masters using a smartphone or tablet. This allows the wrestlers and their masters to prepare for the bout based on the proposed strategies. Some or all of the above-described processing by the proposal unit may be performed using, for example, AI, or may be performed without AI. For example, the proposal unit can provide strategies generated using AI to the wrestlers and their masters.

[0062] The suggestion unit may have an interface through which the wrestlers and the coach receive the suggestions. The suggestion unit presents the suggestions to the wrestlers and the coach using, for example, a device with a touch screen. For example, the suggestion unit may use a tablet to display strategies to the wrestlers and the coach. The suggestion unit may also provide suggestions to the wrestlers and the coach using voice recognition technology. For example, the suggestion unit may use a voice assistant to explain strategies to the wrestlers and the coach. The suggestion unit may also provide suggestions to the wrestlers and the coach using a mobile application. For example, the suggestion unit may present strategies to the wrestlers and the coach through a smartphone app. This makes it easier for the wrestlers and the coach to receive suggestions. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit may provide strategies generated using AI through a touch screen or a voice assistant.

[0063] The AI ​​system further includes a feedback unit that collects post-bout results and uses them for the next analysis. The feedback unit, for example, collects win / loss results after a bout and stores them in a database. For example, the feedback unit records the results of the wrestlers' matches and uses the data for analysis. The feedback unit can also collect the success rate of techniques after a bout and use the data for analysis. For example, the feedback unit records the success rate of techniques after a bout and uses the data for analysis. The feedback unit can also collect data on the wrestlers' physical condition after a bout and use the data for analysis. For example, the feedback unit collects data such as the wrestlers' body temperature, heart rate, and fatigue level after a bout and uses the data for analysis. As a result, the feedback unit reflects the post-bout results in the next analysis, thereby improving the accuracy of the analysis. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without AI. For example, the feedback unit can input post-bout data into AI and use it for the next analysis.

[0064] The collection unit can estimate the emotions of the wrestlers and adjust the timing of data collection based on the estimated emotions. For example, the collection unit captures the wrestlers' facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. For example, the collection unit estimates emotions based on changes in facial expressions, collecting data at normal times if the wrestler is relaxed and slightly delaying data collection if the wrestler is nervous. The collection unit can also record the wrestlers' voices and estimate their emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the voice to estimate emotions. Furthermore, the collection unit can collect the wrestlers' biometric data (heart rate and electrodermal activity) with a sensor and estimate their emotions using an emotion estimation algorithm. For example, the collection unit estimates emotions based on fluctuations in heart rate and postpones data collection until the next morning if the wrestler is tired. This allows the collection unit to adjust the timing of data collection according to the wrestlers' emotions, thereby collecting more appropriate data. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit may input image data of sumo wrestlers taken with a camera into the generation AI and cause the generation AI to estimate emotions.

[0065] The collection unit can analyze the wrestler's past bout data and select the optimal data collection method. For example, the collection unit can analyze the wrestler's past bout data and prioritize collecting data related to a specific technique. For example, the collection unit can analyze the wrestler's past bout data, calculate the success rate of a specific technique, and prioritize collecting data related to that technique. The collection unit can also prioritize collecting data related to a specific opponent based on the wrestler's past match results. For example, the collection unit can analyze the wrestler's past match results and prioritize collecting data related to a specific opponent. Furthermore, the collection unit can also collect data related to factors affecting the wrestler's physical condition by taking the wrestler's past physical condition data into consideration. For example, the collection unit can analyze the wrestler's past physical condition data and prioritize collecting data related to factors affecting the wrestler's physical condition. This enables the collection unit to analyze the past bout data and collect data more effectively. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the wrestler's past bout data into AI and select the optimal data collection method.

[0066] When collecting data, the collection unit can filter the data based on the wrestler's current physical condition and training status. For example, the collection unit collects data when the wrestler is in good physical condition based on the wrestler's current physical condition data. For example, the collection unit analyzes data such as the wrestler's body temperature, heart rate, and fatigue level, and collects data when the wrestler is in good physical condition. The collection unit can also prioritize collecting post-training data taking into account the wrestler's training status. For example, the collection unit collects post-training data of the wrestler and uses that data for analysis. Furthermore, if the wrestler is in poor physical condition, the collection unit can refrain from collecting data and wait until the wrestler's physical condition improves. For example, if the wrestler is in poor physical condition, the collection unit postpones data collection until the next day. This enables the collection unit to collect data according to the wrestler's current condition. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the wrestler's physical condition data into AI and collect data based on the wrestler's physical condition.

[0067] The collection unit can estimate the emotions of the wrestlers and prioritize data collection based on the estimated emotions. For example, the collection unit captures the wrestlers' facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. For example, the collection unit estimates emotions based on changes in facial expressions, collecting regular data if the wrestler is relaxed and prioritizing important data if the wrestler is nervous. The collection unit can also record the wrestlers' voices and estimate their emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the voice to estimate emotions. Furthermore, the collection unit can collect the wrestlers' biometric data (heart rate and electrodermal activity) with a sensor and estimate their emotions using an emotion estimation algorithm. For example, the collection unit estimates emotions based on fluctuations in heart rate and collects only the minimum amount of data necessary if the wrestler is tired. This allows the collection unit to prioritize data collection based on the wrestlers' emotions, allowing important data to be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit may input image data of sumo wrestlers taken with a camera into the generation AI and cause the generation AI to estimate emotions.

[0068] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the wrestler. For example, when the wrestler is in a specific location, the collection unit prioritizes collecting data related to that location. For example, when the wrestler is at a training facility, the collection unit prioritizes collecting training data at the facility. Furthermore, when the wrestler is traveling, the collection unit can also collect data related to the destination. For example, the collection unit collects training data performed by the wrestler while traveling. Furthermore, when the wrestler is at home, the collection unit can prioritize collecting past training data. For example, the collection unit collects training data performed by the wrestler at home. In this way, the collection unit can collect highly relevant data by taking the geographical location information into account. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the geographical location information of the wrestler into AI and prioritize collecting highly relevant data.

[0069] During data collection, the collection unit can analyze the social media activities of the wrestlers and collect related data. The collection unit, for example, analyzes the wrestlers' social media posts and collects data related to their training. For example, the collection unit analyzes the wrestlers' social media posts and collects information related to their training. The collection unit can also analyze the wrestlers' interactions with their followers and collect data related to their mental states. For example, the collection unit analyzes the wrestlers' interactions with their followers and collects information related to their mental states. Furthermore, the collection unit can determine the optimal timing for collecting data based on the wrestlers' social media activity time. For example, the collection unit analyzes the wrestlers' social media activity time and determines the optimal timing for collecting data. This allows the collection unit to collect related data by analyzing the social media activity. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the wrestlers' social media data into AI to collect related data.

[0070] The analysis unit can estimate the emotions of the sumo wrestlers and adjust the method of expression of the analysis based on the estimated emotions. For example, the analysis unit captures the facial expressions of the sumo wrestlers with a camera and estimates their emotions using an emotion estimation algorithm. For example, the analysis unit estimates emotions based on changes in facial expressions, providing detailed analysis results if the wrestler is relaxed and providing concise, to-the-point analysis results if the wrestler is nervous. The analysis unit can also record the wrestlers' voices and estimate their emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice to estimate their emotions. Furthermore, the analysis unit can collect the wrestlers' biometric data (heart rate and electrodermal activity) using a sensor and estimate their emotions using an emotion estimation algorithm. For example, the analysis unit estimates emotions based on fluctuations in heart rate and provides visually easy-to-understand analysis results if the wrestler is tired. This allows the analysis unit to provide more appropriate analysis results by adjusting the method of expression of the analysis according to the wrestlers' emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit may input image data of a sumo wrestler taken with a camera into the generation AI and cause the generation AI to estimate emotions.

[0071] During analysis, the analysis unit can adjust the level of detail of the analysis based on the wrestler's physical condition and past match results. For example, the analysis unit performs a detailed analysis based on the wrestler's physical condition data if the wrestler is in good physical condition and a brief analysis if the wrestler is in poor physical condition. For example, the analysis unit analyzes data such as the wrestler's body temperature, heart rate, and fatigue level, and performs a detailed analysis if the wrestler is in good physical condition and a brief analysis if the wrestler is in poor physical condition. The analysis unit can also adjust the level of detail of the analysis of an opponent based on the wrestler's past match results. For example, the analysis unit analyzes the wrestler's past match results and performs a detailed analysis of a specific opponent. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the success rate of the wrestler's techniques. For example, the analysis unit analyzes the success rate of the wrestler's techniques and performs a detailed analysis of techniques with a high success rate. This enables the analysis unit to perform an analysis based on the wrestler's physical condition and past match results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the physical condition of sumo wrestlers into the AI ​​and adjust the level of detail of the analysis based on their physical condition.

[0072] During analysis, the analysis unit can apply different analysis algorithms depending on the type of technique used by the wrestler and the characteristics of the opponent. The analysis unit, for example, selects an optimal analysis algorithm depending on the wrestler's specialty technique. For example, the analysis unit analyzes the wrestler's specialty technique and selects an analysis algorithm optimal for that technique. The analysis unit can also apply different analysis algorithms depending on the characteristics of the opponent. For example, the analysis unit analyzes the characteristics of the opponent and applies an analysis algorithm depending on the characteristics. Furthermore, the analysis unit can adjust the level of detail of the analysis depending on the type of technique used by the wrestler. For example, the analysis unit analyzes the type of technique used by the wrestler and adjusts the level of detail of the analysis depending on the technique. This enables the analysis unit to perform analysis depending on the type of technique and the characteristics of the opponent. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the type of technique used by the wrestler and the characteristics of the opponent into AI and apply the optimal analysis algorithm.

[0073] The analysis unit can estimate the emotions of the sumo wrestlers and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit captures the facial expressions of the sumo wrestlers with a camera and estimates their emotions using an emotion estimation algorithm. For example, the analysis unit estimates emotions based on changes in facial expressions, performing a detailed analysis if the wrestler is relaxed and a brief analysis if the wrestler is nervous. The analysis unit can also record the wrestlers' voices and estimate their emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice to estimate their emotions. Furthermore, the analysis unit can collect the wrestlers' biometric data (heart rate and electrodermal activity) with a sensor and estimate their emotions using an emotion estimation algorithm. For example, the analysis unit estimates emotions based on fluctuations in heart rate, and if the wrestler is tired, performs a short, concise analysis. This allows the analysis unit to adjust the length of the analysis according to the wrestlers' emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit may input image data of a sumo wrestler taken with a camera into the generation AI and cause the generation AI to estimate emotions.

[0074] During analysis, the analysis unit can determine the priority of analysis based on the wrestler's past training data. For example, the analysis unit prioritizes analysis of important data based on the wrestler's past training data. For example, the analysis unit analyzes the wrestler's past training data and prioritizes analysis of important data. The analysis unit can also determine the priority of analysis based on the wrestler's training frequency. For example, the analysis unit analyzes the wrestler's training frequency and prioritizes analysis of training data with high frequency. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the wrestler's training results. For example, the analysis unit analyzes the wrestler's training results and analyzes training data with high results in detail. This allows the analysis unit to perform analysis based on the priority of past training data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the wrestler's past training data into AI to determine the priority.

[0075] The analysis unit can improve the accuracy of the analysis by referring to literature related to the sumo wrestler during the analysis. The analysis unit, for example, refers to past research papers on the sumo wrestler to improve the accuracy of the analysis. For example, the analysis unit analyzes past research papers on the sumo wrestler and improves the accuracy of the analysis based on the content. The analysis unit can also adjust the level of detail of the analysis by referring to literature on the sumo wrestler's techniques. For example, the analysis unit analyzes literature on the sumo wrestler's techniques and adjusts the level of detail of the analysis based on the content. Furthermore, the analysis unit can also determine the priority of the analysis by referring to literature on sumo wrestler training methods. For example, the analysis unit analyzes literature on sumo wrestler training methods and determines the priority of the analysis based on the content. In this way, the analysis unit improves the accuracy of the analysis by referring to related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input literature related to the sumo wrestler into AI to improve the accuracy of the analysis.

[0076] The suggestion unit can estimate the emotions of the sumo wrestler and adjust the way in which the suggestion is expressed based on the estimated emotions of the sumo wrestler. For example, the suggestion unit captures the facial expressions of the sumo wrestler with a camera and estimates the emotions using an emotion estimation algorithm. For example, the suggestion unit estimates the emotions based on changes in facial expressions, and if the wrestler is relaxed, makes detailed suggestions, and if the wrestler is nervous, makes concise suggestions to the point. The suggestion unit can also record the wrestler's voice and estimate the emotions using voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the voice to estimate the emotions. Furthermore, the suggestion unit can collect the wrestler's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotions using an emotion estimation algorithm. For example, the suggestion unit estimates the emotions based on fluctuations in heart rate and makes visually easy-to-understand suggestions if the wrestler is tired. This allows the suggestion unit to provide more appropriate suggestions by adjusting the way in which the suggestion is expressed based on the wrestler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the proposal unit may be performed using AI, or may be performed without using AI. For example, the proposal unit may input image data of a sumo wrestler taken with a camera into the generation AI and cause the generation AI to estimate emotions.

[0077] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the wrestler's physical condition and past match results. For example, the suggestion unit may provide detailed suggestions based on the wrestler's physical condition data if the wrestler is in good physical condition and provide brief suggestions if the wrestler is in poor physical condition. For example, the suggestion unit may analyze data such as the wrestler's body temperature, heart rate, and fatigue level, and provide detailed suggestions if the wrestler is in good physical condition and provide brief suggestions if the wrestler is in poor physical condition. The suggestion unit may also adjust the level of detail of the proposal for an opponent based on the wrestler's past match results. For example, the suggestion unit may analyze the wrestler's past match results and provide detailed suggestions for specific opponents. Furthermore, the suggestion unit may adjust the level of detail of the proposal based on the success rate of the wrestler's techniques. For example, the suggestion unit may analyze the success rate of the wrestler's techniques and provide detailed suggestions for techniques with a high success rate. This enables the suggestion unit to make suggestions based on the wrestler's physical condition and past match results. Some or all of the above-described processing by the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion department can input data on the physical condition of sumo wrestlers into the AI ​​and adjust the level of detail in the suggestions based on their physical condition.

[0078] When making a proposal, the proposal unit can apply different proposal algorithms depending on the type of technique of the sumo wrestler and the characteristics of the opponent. The proposal unit, for example, selects an optimal proposal algorithm depending on the sumo wrestler's specialty technique. For example, the proposal unit analyzes the sumo wrestler's specialty technique and selects an optimal proposal algorithm for that technique. The proposal unit can also apply different proposal algorithms depending on the characteristics of the opponent. For example, the proposal unit analyzes the characteristics of the opponent and applies a proposal algorithm depending on the characteristics. Furthermore, the proposal unit can adjust the level of detail of the proposal depending on the type of technique of the sumo wrestler. For example, the proposal unit analyzes the type of technique of the sumo wrestler and adjusts the level of detail of the proposal depending on the technique. This enables the proposal unit to make a proposal depending on the type of technique and the characteristics of the opponent. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the type of technique of the sumo wrestler and the characteristics of the opponent into AI and apply the optimal proposal algorithm.

[0079] The suggestion unit can estimate the emotions of the sumo wrestlers and adjust the length of the suggestions based on the estimated emotions. For example, the suggestion unit captures the facial expressions of the sumo wrestlers with a camera and estimates their emotions using an emotion estimation algorithm. For example, the suggestion unit estimates their emotions based on changes in their facial expressions, and provides detailed suggestions if they are relaxed and brief suggestions if they are nervous. The suggestion unit can also record the wrestlers' voices and estimate their emotions using voice analysis technology. For example, the suggestion unit analyzes the tone and speed of their voices to estimate their emotions. Furthermore, the suggestion unit can collect the wrestlers' biometric data (heart rate and electrodermal activity) with a sensor and estimate their emotions using an emotion estimation algorithm. For example, the suggestion unit estimates their emotions based on fluctuations in their heart rate, and if they are tired, provides short, concise suggestions. This allows the suggestion unit to provide more appropriate suggestions by adjusting the length of the suggestions according to the wrestlers' emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the proposal unit may be performed using AI, or may be performed without using AI. For example, the proposal unit may input image data of a sumo wrestler taken with a camera into the generation AI and cause the generation AI to estimate emotions.

[0080] When making a proposal, the suggestion unit can determine the priority of the proposals based on the wrestler's past training data. For example, the suggestion unit prioritizes important proposals based on the wrestler's past training data. For example, the suggestion unit analyzes the wrestler's past training data and prioritizes important proposals. The suggestion unit can also determine the priority of the proposals based on the wrestler's training frequency. For example, the suggestion unit analyzes the wrestler's training frequency and makes suggestions based on training data with high frequency. Furthermore, the suggestion unit can adjust the level of detail of the proposals based on the wrestler's training results. For example, the suggestion unit analyzes the wrestler's training results and makes suggestions based on training data with high results. This allows the suggestion unit to make proposals in priority order based on past training data. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the wrestler's past training data into AI to determine the priority.

[0081] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to literature related to the sumo wrestler. The proposal unit, for example, refers to past research papers on the sumo wrestler to improve the accuracy of the proposal. For example, the proposal unit analyzes past research papers on the sumo wrestler and improves the accuracy of the proposal based on the content. The proposal unit can also adjust the level of detail of the proposal by referring to literature on sumo wrestler techniques. For example, the proposal unit analyzes literature on sumo wrestler techniques and adjusts the level of detail of the proposal based on the content. The proposal unit can also determine the priority of the proposal by referring to literature on sumo wrestler training methods. For example, the proposal unit analyzes literature on sumo wrestler training methods and determines the priority of the proposal based on the content. In this way, the proposal unit improves the accuracy of the proposal by referring to related literature. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input literature related to the sumo wrestler into AI to improve the accuracy of the proposal.

[0082] The feedback unit can estimate the emotions of the wrestlers and adjust the feedback collection method based on the estimated emotions. For example, the feedback unit captures the wrestlers' facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. For example, the feedback unit estimates their emotions based on changes in their facial expressions, collecting detailed feedback if they are relaxed and collecting concise feedback if they are nervous. The feedback unit can also record the wrestlers' voices and estimate their emotions using voice analysis technology. For example, the feedback unit analyzes the tone and speed of their voices to estimate their emotions. Furthermore, the feedback unit can collect the wrestlers' biometric data (heart rate and electrodermal activity) using a sensor and estimate their emotions using an emotion estimation algorithm. For example, the feedback unit estimates their emotions based on fluctuations in their heart rate and collects brief, concise feedback if they are tired. This allows the feedback unit to adjust the feedback collection method according to the wrestlers' emotions, thereby collecting more appropriate feedback. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the feedback unit may be performed using AI, or may be performed without using AI. For example, the feedback unit may input image data of a sumo wrestler taken with a camera into the generation AI and cause the generation AI to estimate emotions.

[0083] When collecting feedback, the feedback unit can adjust the level of detail of the feedback based on the wrestler's physical condition and the results of the bout. For example, based on the wrestler's physical condition data, the feedback unit collects detailed feedback if the wrestler is in good physical condition and collects brief feedback if the wrestler is in poor physical condition. For example, the feedback unit analyzes data such as the wrestler's body temperature, heart rate, and fatigue level, and collects detailed feedback if the wrestler is in good physical condition and collects brief feedback if the wrestler is in poor physical condition. The feedback unit can also collect detailed feedback based on the wrestler's bout results if the bout results are good. For example, the feedback unit analyzes the wrestler's bout results and collects detailed feedback if the bout results are good. This enables the feedback unit to collect feedback according to the wrestler's physical condition and the bout results. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without AI. For example, the feedback unit can input the wrestler's physical condition data and the bout results into AI and adjust the level of detail of the feedback.

[0084] The feedback unit can estimate the emotions of the sumo wrestlers and determine the priority of feedback based on the estimated emotions. For example, the feedback unit captures the facial expressions of the sumo wrestlers with a camera and estimates their emotions using an emotion estimation algorithm. For example, the feedback unit estimates emotions based on changes in facial expressions, and if the wrestler is relaxed, it collects normal feedback, and if the wrestler is nervous, it prioritizes collecting important feedback. The feedback unit can also record the wrestlers' voices and estimate their emotions using voice analysis technology. For example, the feedback unit analyzes the tone and speed of the voice to estimate their emotions. Furthermore, the feedback unit can collect the wrestlers' biometric data (heart rate and electrodermal activity) using a sensor and estimate their emotions using an emotion estimation algorithm. For example, the feedback unit estimates emotions based on fluctuations in heart rate, and if the wrestler is tired, it collects only the minimum necessary feedback. In this way, the feedback unit can prioritize feedback according to the wrestlers' emotions, thereby prioritizing the collection of important feedback. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the feedback unit may be performed using AI, or may be performed without using AI. For example, the feedback unit may input image data of a sumo wrestler taken with a camera into the generation AI and cause the generation AI to estimate emotions.

[0085] When collecting feedback, the feedback unit can weight the feedback based on the wrestler's past training data. The feedback unit, for example, prioritizes collecting important feedback based on the wrestler's past training data. For example, the feedback unit analyzes the wrestler's past training data and prioritizes collecting important feedback. The feedback unit can also weight the feedback according to the wrestler's training frequency. For example, the feedback unit analyzes the wrestler's training frequency and provides feedback based on training data with high frequency. Furthermore, the feedback unit can adjust the level of detail of the feedback based on the wrestler's training results. For example, the feedback unit analyzes the wrestler's training results and provides feedback based on training data with high results. This allows the feedback unit to provide feedback with weighting based on the past training data. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the wrestler's past training data into AI and perform weighting. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and feedback unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data on the wrestlers using the camera 42 and microphone 38B of the smart device 14 and stores the data in the database 24 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and suggests an optimal strategy. The suggestion unit, realized, for example, by the control unit 46A of the smart device 14, provides specific strategies to the wrestlers and their coaches based on the analysis results. The feedback unit, realized, for example, by the specific processing unit 290 of the data processing device 12, collects the results of a match and uses them for the next analysis. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and feedback unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data on the wrestlers using the camera 42 and microphone 238 of the smart glasses 214 and stores the data in the database 24 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and suggests optimal strategies. The suggestion unit, realized, for example, by the control unit 46A of the smart glasses 214, provides specific strategies to the wrestlers and their coaches based on the analysis results. The feedback unit, realized, for example, by the specific processing unit 290 of the data processing device 12, collects the results of a match and uses them for the next analysis. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and feedback unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects data on the wrestlers using the camera 42 and microphone 238 of the headset-type terminal 314 and stores the data in the database 24 by the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and suggests optimal strategies. The suggestion unit, realized, for example, by the control unit 46A of the headset-type terminal 314, provides specific strategies to the wrestlers and their coaches based on the analysis results. The feedback unit, realized, for example, by the specific processing unit 290 of the data processing device 12, collects the results of a match and uses them for the next analysis. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, and feedback unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data on the wrestlers using the camera 42 and microphone 238 of the robot 414 and stores the data in the database 24 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to propose an optimal strategy. The proposal unit is realized, for example, by the control unit 46A of the robot 414, and provides specific strategies to the wrestlers and their coaches based on the analysis results. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects the results of a match after it has been won to use them for the next analysis.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The analysis unit can collect dietary data from sumo wrestlers and propose strategies that take nutritional balance into consideration. For example, the analysis unit can analyze a wrestler's diet and, if a specific nutrient is lacking, propose a meal plan to supplement that nutrient. The analysis unit can also suggest optimal meal timings before a match based on the wrestler's dietary data. Furthermore, the analysis unit can compare a wrestler's dietary data with other wrestlers and propose dietary improvements that will help improve performance. In this way, the analysis unit can utilize a wrestler's dietary data to propose more effective strategies.

[0088] The collection unit can collect the sleep data of the wrestlers and provide it to the analysis unit. For example, the collection unit can record the sleep duration and quality of the wrestlers and store it in a database. The collection unit can also analyze the wrestlers' sleep patterns and evaluate the impact of lack of sleep on their performance. Furthermore, the collection unit can suggest optimal sleep duration and sleeping environment based on the wrestlers' sleep data. This allows the collection unit to utilize the wrestlers' sleep data to perform more accurate analysis.

[0089] The analysis unit can evaluate the psychological state of a sumo wrestler and suggest mental training. For example, the analysis unit can analyze the results of a sumo wrestler's psychological test to evaluate his stress level and concentration state. The analysis unit can also suggest relaxation techniques and training to improve concentration based on the wrestler's psychological state. Furthermore, the analysis unit can compare the wrestler's psychological state with other wrestlers and suggest areas for mental strengthening. This allows the analysis unit to suggest strategies that take the wrestler's psychological state into consideration.

[0090] The suggestion unit can make suggestions for improving the training menu based on the wrestler's training data. For example, the suggestion unit can analyze the wrestler's training data and, if a particular training is not effective, suggest an alternative training menu. The suggestion unit can also make suggestions for adjusting the intensity and frequency of training based on the wrestler's training data. Furthermore, the suggestion unit can compare the wrestler's training data with other wrestlers and suggest training methods that will help improve performance. In this way, the suggestion unit can utilize the wrestler's training data to suggest a more effective training menu.

[0091] The suggestion unit can estimate the emotions of the sumo wrestler and make suggestions to improve training motivation based on the estimated emotions. For example, the suggestion unit can analyze the facial expressions and voice data of the sumo wrestler, and if motivation is low, suggest an encouraging message or music to boost motivation. The suggestion unit can also suggest training goal setting and a training plan that helps the wrestler feel a sense of accomplishment based on the wrestler's emotional data. Furthermore, the suggestion unit can compare the emotional data of the sumo wrestler with that of other wrestlers and suggest best practices for improving motivation. This allows the suggestion unit to suggest motivation improvement measures that take the wrestler's emotions into consideration.

[0092] The feedback unit can estimate the emotions of the wrestler and adjust the content of the feedback based on the estimated emotions. For example, the feedback unit can analyze the wrestler's facial expressions and voice data, and emphasize positive feedback when the wrestler is emotionally excited, and provide encouraging feedback when the wrestler is emotionally depressed. The feedback unit can also adjust the timing of feedback based on the wrestler's emotional data. Furthermore, the feedback unit can compare the emotional data of the wrestler with that of other wrestlers and suggest the optimal feedback method according to the emotion. This allows the feedback unit to provide feedback that takes the wrestler's emotions into consideration.

[0093] The collection unit can collect data on the training environment of the wrestlers and provide it to the analysis unit. For example, the collection unit records environmental data such as the temperature, humidity, and lighting brightness of the training facility and stores it in a database. The collection unit can also evaluate the impact that changes in the training environment have on performance. Furthermore, the collection unit can suggest the optimal training environment based on the training environment data of the wrestlers. This enables the collection unit to utilize the training environment data of the wrestlers to perform more accurate analysis.

[0094] The analysis unit can estimate the emotions of the sumo wrestlers and evaluate the effectiveness of their training based on the estimated emotions. For example, the analysis unit can analyze the facial expressions and voice data of the sumo wrestlers and evaluate changes in their emotions after training. The analysis unit can also quantify the effectiveness of training based on the emotional data of the wrestlers and propose a training plan in accordance with the changes in their emotions. Furthermore, the analysis unit can compare the emotional data of the wrestlers with that of other wrestlers and propose improvements to maximize the effectiveness of training. This allows the analysis unit to evaluate the effectiveness of training taking into account the emotions of the wrestlers.

[0095] The suggestion unit can estimate the emotions of a sumo wrestler and suggest pre-match mental care based on the estimated emotions. For example, the suggestion unit can analyze the wrestler's facial expressions and voice data and suggest relaxation or breathing techniques if the wrestler is nervous. The suggestion unit can also adjust the timing of pre-match mental care based on the wrestler's emotional data. Furthermore, the suggestion unit can compare the wrestler's emotional data with other wrestlers and suggest best practices for mental care. This allows the suggestion unit to suggest mental care that takes the wrestler's emotions into consideration.

[0096] The feedback unit can evaluate the effectiveness of training based on the wrestler's training data and use the results to improve the wrestler's next training. For example, the feedback unit can analyze the wrestler's training data and evaluate whether a particular training session was effective. The feedback unit can also make suggestions to adjust the intensity and frequency of training based on the wrestler's training data. Furthermore, the feedback unit can compare the wrestler's training data with other wrestlers and suggest training methods that will help improve performance. In this way, the feedback unit can utilize the wrestler's training data to suggest more effective training plans.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The collection unit collects data on the wrestler's past matches and data on his opponents. For example, it collects data such as the wrestler's weight, height, type of technique, and past wins and losses. The collection unit measures the wrestler's weight and height and stores the data in a database. The collection unit can also analyze video data of past matches and record the type of technique and success rate. The collection unit also collects data on the wrestler's opponents to understand the wrestler's match record and technique trends. Step 2: The analysis unit analyzes the data collected by the collection unit and proposes the optimal strategy for the next day's bout. For example, the analysis unit considers the wrestler's physical condition and past match results to propose which technique should be used and what position the wrestler should be in during the bout. The analysis unit uses AI to analyze the data and identify the wrestler's strengths and weaknesses. For example, the analysis unit calculates the success rate of a specific technique based on the wrestler's past bout data and determines whether or not that technique should be used. Step 3: The proposal unit provides the proposals obtained by the analysis unit to the wrestlers and their coaches. For example, the proposal unit may present specific strategies to the wrestlers and support their preparations for the match. The proposal unit uses AI to generate proposals and provide appropriate advice to the wrestlers and their coaches. For example, the proposal unit may provide specific advice to a wrestler such as, "Wrestler B has a strong thrust with his right hand, so he should strengthen his push with his left hand."

[0099] 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.

[0100] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0101] 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.

[0102] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0147] 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.

[0148] 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.

[0149] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] [Explanation of symbols]

[0171] 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 collection department that collects data on wrestlers' past matches and opponents, an analysis unit that analyzes the data collected by the collection unit and proposes a specific strategy for the next day's efforts; a proposal unit that provides the proposals obtained by the analysis unit to the wrestlers and their bosses. A system characterized by:

2. The collecting unit Collect data such as the wrestlers' weight, height, type of technique, past wins and losses, and success rate of techniques 2. The system of claim 1.

3. The analysis unit Based on the collected data, specific strategies are proposed taking into account the physical condition of the wrestlers and their past match results.

2. The system of claim 1.

4. The proposal unit Providing suggested strategies to wrestlers and coaches 2. The system of claim 1.

5. The proposal unit Provide an interface for wrestlers and coaches to receive suggestions 2. The system of claim 1.

6. A feedback section will be added to collect the results of the initiative and use them for the next analysis.

2. The system of claim 1.

7. The collecting unit Estimate the emotions of the wrestlers and adjust the timing of data collection based on the estimated emotions of the wrestlers.

2. The system of claim 1.

8. The collecting unit Analyze the wrestlers' past match data and select the most appropriate data collection method 2. The system of claim 1.

9. The collecting unit When collecting data, filtering is performed based on the wrestlers' current physical condition and training status.

2. The system of claim 1.

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