system

Abstract

The system according to this embodiment is intended to evaluate AI technology. [Solution] The system according to this embodiment comprises a battle unit, an analysis unit, and an evaluation unit. In the battle unit, AIs battle each other. The analysis unit analyzes the results of the battle performed by the battle unit. The evaluation unit evaluates the AI's technology based on the results analyzed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the technical evaluation of AI has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to evaluate the technology of AI.

Means for Solving the Problems

[0006] The system according to the embodiment includes a battle unit, an analysis unit, and an evaluation unit. The battle unit has AIs fight against each other. The analysis unit analyzes the results of the battles fought by the battle unit. The evaluation unit evaluates the technology of AI based on the results analyzed by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can evaluate AI technology. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​Shogi League System according to an embodiment of the present invention is a system established to promote the development, research, and advancement of AI. This AI Shogi League System establishes a league in which AIs compete against each other in shogi, and sets up a system of support through corporate sponsors, local residents, and the government working together. For example, companies provide funding as sponsors, the government provides support through policies, and residents provide support through cheering and admission fees. In this way, a system is built to support the AI ​​shogi development team. Next, the AI ​​technology is disseminated through the matches of the AI ​​Shogi League. For example, the ability of AI to defeat a shogi master is demonstrated, thereby raising awareness of the AI's capabilities. Also, by having AIs compete against each other, it is possible to demonstrate how much AI technology has advanced. Furthermore, based on the success of the AI ​​Shogi League, it will be applied to other games. For example, AI chess leagues, AI Othello leagues, AI Go leagues, AI Mahjong leagues, AI poker leagues, etc., will be established to further develop AI technology. Through this mechanism, the development, research, and advancement of AI will be promoted, and the aim is to make AI a familiar presence. For example, by demonstrating the extent of AI technology's advancement through AI Shogi League matches, the capabilities of AI can be widely recognized. Furthermore, matches between AIs can showcase the evolution of AI technology. Building on the success of the AI ​​Shogi League, its application to other games will further advance AI technology. In this way, the AI ​​Shogi League system will promote the development, research, and advancement of AI, making AI a more familiar presence.

[0029] The AI ​​Shogi League System according to this embodiment comprises a match unit, an analysis unit, and an evaluation unit. The match unit is the unit for AIs to play against each other. The match unit provides, for example, a platform for AIs to play against each other in Shogi. The match unit sets the rules and format for the AIs to play and manages the matches. For example, the match unit provides a program for AIs to play according to the rules of Shogi. The match unit also monitors the progress of the matches in real time and records the match results. The analysis unit is the unit that analyzes the results of the matches played by the match unit. For example, the analysis unit collects data on the match results and analyzes how much the AI's technology has evolved. The analysis unit uses data analysis methods to analyze the match results in detail. For example, the analysis unit evaluates the AI's match performance and measures the degree of technological advancement. The analysis unit also identifies the AI's technological strengths and weaknesses based on the match results. The evaluation unit is the unit that evaluates the AI's technology based on the results analyzed by the analysis unit. For example, the evaluation unit performs a technical evaluation of the AI ​​based on the analysis results. The evaluation unit objectively evaluates the AI's technology using technical evaluation indicators. For example, the evaluation unit scores the AI's performance and assesses the degree of technological advancement. Furthermore, based on the analysis results, the evaluation unit identifies areas for technical improvement in the AI. Thus, the AI ​​shogi league system according to this embodiment can accelerate the technological advancement of the AI ​​by analyzing the results of matches between AIs and evaluating their technology.

[0030] The Battle Department is responsible for facilitating matches between AIs. For example, it provides a platform for AIs to play shogi (Japanese chess). Specifically, the Battle Department sets the rules and format for AI matches and manages the matches. For instance, it provides a program for AIs to play shogi according to its rules. The battle platform provides an interface for AIs to initiate matches and monitors the progress of the matches in real time. During a match, it manages each AI's turn and time limit to ensure accurate matches are conducted according to the rules. The Battle Department also records the match results and stores them in a database, allowing the Analysis and Evaluation Departments to access the results later. Furthermore, to maintain fairness, the Battle Department monitors the AI ​​match logs and checks for any cheating. For example, if abnormal behavior or a fraudulent algorithm is detected during a match, it interrupts the match and takes measures to resolve the issue. This allows the Battle Department to manage AI matches fairly and smoothly, providing a highly reliable playing environment.

[0031] The Analysis Department is responsible for analyzing the results of matches played by the Playing Department. For example, the Analysis Department collects data on match results and analyzes how much AI technology has evolved. Specifically, the Analysis Department uses data analysis methods to analyze match results in detail. For example, the Analysis Department evaluates the AI's match performance and measures the degree of technological advancement. The Analysis Department analyzes the AI's moves turn by turn, and analyzes in detail the evaluation value of each move and the fluctuations in the win rate. This allows the Analysis Department to identify the strengths and weaknesses of the AI's strategies and tactics. The Analysis Department also analyzes the AI's match data over time to grasp the trends in technological evolution. For example, it evaluates how much a particular technology or algorithm has evolved by comparing it with past match data. Furthermore, the Analysis Department analyzes the AI's match data using clustering and classification methods to clarify the differences in characteristics and performance between different AIs. This allows the Analysis Department to grasp the technological evolution of AI in detail and provide information that contributes to technological improvement.

[0032] The Evaluation Department is responsible for evaluating AI technology based on the results analyzed by the Analysis Department. For example, the Evaluation Department performs technical evaluations of AI based on the analysis results. Specifically, the Evaluation Department objectively evaluates AI technology using technical evaluation indicators. For instance, the Evaluation Department scores AI performance and assesses the degree of technical advancement. Based on the AI's match results, the Evaluation Department meticulously evaluates the strengths and weaknesses of each AI and identifies areas for technical improvement. For example, based on the AI's turn-by-turn evaluation values ​​and win rate fluctuations, it clarifies areas for improvement in strategy and tactics in specific situations. Furthermore, the Evaluation Department sets indicators for quantitatively evaluating the AI's technical advancement based on data provided by the Analysis Department. This allows the Evaluation Department to objectively and quantitatively evaluate the AI's technical advancement and provide concrete guidelines for technological improvement. Additionally, based on the evaluation results, the Evaluation Department identifies areas for technical improvement and provides feedback to developers. This allows the Evaluation Department to promote the AI's technical advancement and contribute to the technical improvement of Shogi AI.

[0033] The Support Department is responsible for establishing a system of collaborative support from corporate sponsors, local residents, and the government. For example, the Support Department will create a system where corporate sponsors provide funding, the government provides support through policies, and residents support through cheering and admission fees. The Support Department will conclude contracts with corporate sponsors to ensure they provide the necessary funds for the operation of the AI ​​Shogi League. Furthermore, the Support Department will formulate and implement policies for the government to support the AI ​​Shogi League. In addition, the Support Department will plan events to encourage local residents to support the AI ​​Shogi League through admission fees. This collaborative support from corporate sponsors, local residents, and the government will accelerate the development and research of AI. Some or all of the above processes within the Support Department may be performed using AI, or not. For example, the Support Department can use AI to automatically manage funding contracts from corporate sponsors and monitor the progress of government policy support.

[0034] The battle unit allows AIs to play against each other in shogi. The battle unit provides, for example, a platform for AIs to play against each other according to the rules of shogi. The battle unit provides a program for AIs to play against each other according to the rules of shogi. The battle unit also monitors the progress of the battle in real time and records the results. For example, when AIs play shogi, the battle unit monitors the progress of the battle in real time and saves the results to a database. This provides a platform for AIs to compete by playing shogi against each other. Some or all of the above processes in the battle unit may be performed using AI, for example, or not using AI. For example, the battle unit can automatically monitor the progress of the battle using AI and analyze the results using AI.

[0035] The analysis unit can analyze the match results and demonstrate how much the AI's technology has advanced. For example, the analysis unit collects match result data and analyzes how much the AI's technology has advanced. The analysis unit uses data analysis methods to analyze the match results in detail. For example, the analysis unit evaluates the AI's match performance and measures the degree of technological advancement. The analysis unit also identifies the AI's technological strengths and weaknesses based on the match results. In this way, by analyzing the match results, the degree of the AI's technological advancement can be clearly demonstrated. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can automatically analyze the match result data using AI and evaluate the degree of technological advancement using AI.

[0036] The evaluation unit can evaluate the AI ​​technology based on the analysis results. For example, the evaluation unit performs a technical evaluation of the AI ​​based on the analysis results. The evaluation unit objectively evaluates the AI ​​technology using technical evaluation indicators. For example, the evaluation unit scores the AI's performance and assesses the degree of technological advancement. The evaluation unit also identifies areas for technical improvement in the AI ​​based on the analysis results. In this way, by evaluating the AI ​​technology based on the analysis results, the technological advancement can be objectively assessed. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can automatically evaluate the analysis results using AI and score the degree of technological advancement using AI.

[0037] The support department can establish a system in which corporate sponsors provide funding, the government provides support through policies, and residents provide support through cheering and admission fees. For example, the support department can conclude contracts with corporate sponsors to provide the necessary funds for the operation of the AI ​​Shogi League. The support department can also formulate and implement policies for the government to support the AI ​​Shogi League. Furthermore, the support department can plan events for local residents to cheer on the AI ​​Shogi League and provide support through admission fees. In this way, the development and research of AI can be sustained through the unified support of corporate sponsors, the government, and residents. Some or all of the above processes in the support department may be carried out using AI, or not. For example, the support department can use AI to automatically manage contracts for funding from corporate sponsors and monitor the progress of government policy support using AI.

[0038] The battle unit can select the optimal opponent by referring to the AI's past battle history during a match. For example, the battle unit may re-select an opponent with whom it had a close match in the past. The battle unit may avoid opponents it has overwhelmingly defeated in the past, aiming for a more balanced match. The battle unit may also rematch opponents it has lost to in the past to check for advancements in skill. In this way, referencing past battle history leads to more balanced matches. Some or all of the above processes in the battle unit may be performed using AI, for example, or without AI. For example, the battle unit may input past battle history data into a generating AI and have the generating AI select the optimal opponent.

[0039] The battle unit can adjust the difficulty of a match based on the AI's learning progress. For example, the battle unit will select a high-difficulty opponent for an AI that has made significant progress in learning, and a low-difficulty opponent for an AI that has not yet made sufficient progress in learning. The battle unit will also gradually increase the difficulty of the opponents as the learning progresses. In this way, by adjusting the difficulty of the match according to the AI's learning progress, a match of appropriate difficulty is provided. Some or all of the above processing in the battle unit may be performed using an AI, or it may be performed without an AI. For example, the battle unit can input AI learning progress data into a generating AI and have the generating AI perform the adjustment of the match difficulty.

[0040] The battle unit can select opponents during a battle by considering the geographical distribution of the AIs. For example, the battle unit can have AIs from the same region battle each other. The battle unit can have AIs from different regions battle each other. The battle unit can also have AIs that are geographically close to each other battle each other. In this way, by considering geographical distribution, battles can be held by different regions. Some or all of the above processing in the battle unit may be performed using an AI, or it may be performed without an AI. For example, the battle unit can input geographical distribution data of the AIs into a generating AI and have the generating AI perform the selection of opponents.

[0041] The battle unit can customize the rules of a match to reflect the intentions of the AI ​​developer. For example, the battle unit can conduct a match based on specific rules set by the developer. The battle unit can adjust the time limit of the match according to the developer's intentions. The battle unit can also prohibit certain strategies based on the developer's intentions. This ensures that the match is conducted under rules that align with the developer's intentions. Some or all of the above processes in the battle unit may be performed using AI, or not. For example, the battle unit can input developer intention data into a generating AI and have the generating AI perform the customization of the match rules.

[0042] The analysis unit can perform a detailed analysis of the strategies and methods used in the matches and evaluate the technological advancements of the AI. For example, the analysis unit can analyze the frequency of strategic changes during a match. The analysis unit can evaluate the diversity of methods used during a match. The analysis unit can also analyze the success rate of strategies used during a match. In this way, by analyzing the strategies and methods used in a match in detail, the technological advancements of the AI ​​can be evaluated. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input match data into a generating AI and have the generating AI perform the analysis of strategies and methods.

[0043] The analysis unit can track the changes in the AI's behavior over time during the analysis, thereby clarifying the evolutionary process. For example, the analysis unit tracks the behavioral patterns from the start to the end of the match. The analysis unit analyzes changes in strategy over time. The analysis unit also evaluates the changes in behavior during the match in a time series. By tracking the changes in behavior over time, the evolutionary process of the AI ​​can be clarified. Some or all of the above-described processes in the analysis unit may be performed using an AI, for example, or without an AI. For example, the analysis unit can input match data into a generating AI and have the generating AI perform the tracking of behavioral changes.

[0044] The analysis unit can classify the analysis results by considering the geographical factors of the matches during the analysis. For example, the analysis unit can classify the results of matches between AIs that are geographically close to each other. The analysis unit can also classify the results of matches between AIs that are geographically different. Furthermore, the analysis unit can classify the analysis results by region based on geographical factors. In this way, by considering geographical factors, analysis results for each region can be obtained. Some or all of the above processing in the analysis unit may be performed using an AI, for example, or without using an AI. For example, the analysis unit can input match result data into a generating AI and have the generating AI perform classification based on geographical factors.

[0045] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the match during the analysis process. For example, when analyzing the match results, the analysis unit refers to relevant academic papers. When analyzing the match results, the analysis unit refers to past research data. Furthermore, when analyzing the match results, the analysis unit refers to relevant technical literature. By referring to relevant literature, the accuracy of the analysis is improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0046] The evaluation unit can maintain evaluation consistency by referring to the AI's past evaluation history during the evaluation process. For example, the evaluation unit performs the current evaluation based on past evaluation results. The evaluation unit unifies the evaluation criteria by referring to past evaluation history. The evaluation unit also analyzes past evaluation history to maintain evaluation consistency. In this way, evaluation consistency is maintained by referring to past evaluation history. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input past evaluation history data into a generating AI and have the generating AI perform processing to maintain evaluation consistency.

[0047] The evaluation unit can measure the rate of technological evolution of AI during evaluation and use it as an evaluation indicator. For example, the evaluation unit measures the rate of technological evolution and uses it as an evaluation indicator. The evaluation unit sets evaluation criteria based on the rate of technological evolution. The evaluation unit also analyzes the rate of technological evolution and uses it as an evaluation indicator. In this way, the evaluation indicator becomes clear by measuring the rate of technological evolution. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input technological evolution rate data into a generating AI and have the generating AI execute the evaluation indicator.

[0048] The evaluation unit can classify evaluation results while considering the geographical distribution of the AIs during evaluation. For example, the evaluation unit can classify evaluation results of AIs that are geographically close to each other. The evaluation unit can also classify evaluation results of AIs that are geographically different to each other. Furthermore, the evaluation unit can classify evaluation results by region based on geographical factors. In this way, evaluation results for each region can be obtained by considering the geographical distribution. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input evaluation result data into a generating AI and have the generating AI perform classification based on geographical factors.

[0049] The evaluation unit can improve the accuracy of its evaluation by referring to relevant AI literature during the evaluation process. For example, the evaluation unit refers to relevant academic papers when analyzing the evaluation results. The evaluation unit refers to past research data when analyzing the evaluation results. The evaluation unit also refers to relevant technical literature when analyzing the evaluation results. By referring to relevant literature, the accuracy of the evaluation is improved. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input relevant literature data into a generating AI and have the generating AI perform the evaluation accuracy improvement.

[0050] The support department can select the optimal support method by referring to past support history when establishing a support system. For example, the support department may propose a support method that has been successful in the past. The support department analyzes past support history and selects the optimal support method. In addition, the support department proposes a new support method based on past support history. In this way, the optimal support method is selected by referring to past support history. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input past support history data into a generating AI and have the generating AI perform the selection of the optimal support method.

[0051] The support department can customize support methods when establishing a support system, taking into account the geographical distribution of supporters. For example, the support department may propose direct support methods to geographically close supporters, and online support methods to geographically distant supporters. Furthermore, the support department customizes support methods for each region based on geographical distribution. This ensures that the most suitable support method is provided for each region by considering geographical distribution. Some or all of the above processing in the support department may be performed using AI, for example, or without AI. For example, the support department can input geographical distribution data of supporters into a generating AI and have the generating AI perform the customization of support methods.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The battle unit can select the optimal opponent by referring to the AI's past battle history during a match. For example, the battle unit may re-select an opponent with whom it had a close match in the past. The battle unit may avoid opponents it has overwhelmingly defeated in the past, aiming for a more balanced match. The battle unit may also rematch opponents it has lost to in the past to check for advancements in skill. In this way, referencing past battle history leads to more balanced matches. Some or all of the above processes in the battle unit may be performed using AI, for example, or without AI. For example, the battle unit may input past battle history data into a generating AI and have the generating AI select the optimal opponent.

[0054] The battle unit can adjust the difficulty of a match based on the AI's learning progress. For example, the battle unit will select a high-difficulty opponent for an AI that has made significant progress in learning, and a low-difficulty opponent for an AI that has not yet made sufficient progress in learning. The battle unit will also gradually increase the difficulty of the opponents as the learning progresses. In this way, by adjusting the difficulty of the match according to the AI's learning progress, a match of appropriate difficulty is provided. Some or all of the above processing in the battle unit may be performed using an AI, or it may be performed without an AI. For example, the battle unit can input AI learning progress data into a generating AI and have the generating AI perform the adjustment of the match difficulty.

[0055] The battle unit can select opponents during a battle by considering the geographical distribution of the AIs. For example, the battle unit can have AIs from the same region battle each other. The battle unit can have AIs from different regions battle each other. The battle unit can also have AIs that are geographically close to each other battle each other. In this way, by considering geographical distribution, battles can be held by different regions. Some or all of the above processing in the battle unit may be performed using an AI, or it may be performed without an AI. For example, the battle unit can input geographical distribution data of the AIs into a generating AI and have the generating AI perform the selection of opponents.

[0056] The battle unit can customize the rules of a match to reflect the intentions of the AI ​​developer. For example, the battle unit can conduct a match based on specific rules set by the developer. The battle unit can adjust the time limit of the match according to the developer's intentions. The battle unit can also prohibit certain strategies based on the developer's intentions. This ensures that the match is conducted under rules that align with the developer's intentions. Some or all of the above processes in the battle unit may be performed using AI, or not. For example, the battle unit can input developer intention data into a generating AI and have the generating AI perform the customization of the match rules.

[0057] The analysis unit can perform a detailed analysis of the strategies and methods used in the matches and evaluate the technological advancements of the AI. For example, the analysis unit can analyze the frequency of strategic changes during a match. The analysis unit can evaluate the diversity of methods used during a match. The analysis unit can also analyze the success rate of strategies used during a match. In this way, by analyzing the strategies and methods used in a match in detail, the technological advancements of the AI ​​can be evaluated. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input match data into a generating AI and have the generating AI perform the analysis of strategies and methods.

[0058] The analysis unit can track the changes in the AI's behavior over time during the analysis, thereby clarifying the evolutionary process. For example, the analysis unit tracks the behavioral patterns from the start to the end of the match. The analysis unit analyzes changes in strategy over time. The analysis unit also evaluates the changes in behavior during the match in a time series. By tracking the changes in behavior over time, the evolutionary process of the AI ​​can be clarified. Some or all of the above-described processes in the analysis unit may be performed using an AI, for example, or without an AI. For example, the analysis unit can input match data into a generating AI and have the generating AI perform the tracking of behavioral changes.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The Battle Department is the department for AIs to compete against each other. For example, the Battle Department provides a platform for AIs to play against each other in shogi (Japanese chess). The Battle Department sets the rules and format for the AIs to compete and manages the matches. For example, the Battle Department provides a program for AIs to play shogi according to the rules. The Battle Department also monitors the progress of the matches in real time and records the results. Step 2: The Analysis Department is responsible for analyzing the results of matches played by the Match Department. For example, the Analysis Department collects match result data and analyzes how much the AI's technology has advanced. The Analysis Department uses data analysis methods to analyze the match results in detail. For example, the Analysis Department evaluates the AI's match performance and measures the degree of technological advancement. The Analysis Department also identifies the AI's technological strengths and weaknesses based on the match results. Step 3: The Evaluation Department is responsible for evaluating the AI's technology based on the results analyzed by the Analysis Department. For example, the Evaluation Department performs a technical evaluation of the AI ​​based on the analysis results. The Evaluation Department objectively evaluates the AI's technology using technical evaluation indicators. For example, the Evaluation Department scores the AI's performance and assesses the degree of technological advancement. The Evaluation Department also identifies areas for technical improvement in the AI ​​based on the analysis results.

[0061] (Example of form 2) The AI ​​Shogi League System according to an embodiment of the present invention is a system established to promote the development, research, and advancement of AI. This AI Shogi League System establishes a league in which AIs compete against each other in shogi, and sets up a system of support through corporate sponsors, local residents, and the government working together. For example, companies provide funding as sponsors, the government provides support through policies, and residents provide support through cheering and admission fees. In this way, a system is built to support the AI ​​shogi development team. Next, the AI ​​technology is disseminated through the matches of the AI ​​Shogi League. For example, the ability of AI to defeat a shogi master is demonstrated, thereby raising awareness of the AI's capabilities. Also, by having AIs compete against each other, it is possible to demonstrate how much AI technology has advanced. Furthermore, based on the success of the AI ​​Shogi League, it will be applied to other games. For example, AI chess leagues, AI Othello leagues, AI Go leagues, AI Mahjong leagues, AI poker leagues, etc., will be established to further develop AI technology. Through this mechanism, the development, research, and advancement of AI will be promoted, and the aim is to make AI a familiar presence. For example, by demonstrating the extent of AI technology's advancement through AI Shogi League matches, the capabilities of AI can be widely recognized. Furthermore, matches between AIs can showcase the evolution of AI technology. Building on the success of the AI ​​Shogi League, its application to other games will further advance AI technology. In this way, the AI ​​Shogi League system will promote the development, research, and advancement of AI, making AI a more familiar presence.

[0062] The AI ​​Shogi League System according to this embodiment comprises a match unit, an analysis unit, and an evaluation unit. The match unit is the unit for AIs to play against each other. The match unit provides, for example, a platform for AIs to play against each other in Shogi. The match unit sets the rules and format for the AIs to play and manages the matches. For example, the match unit provides a program for AIs to play according to the rules of Shogi. The match unit also monitors the progress of the matches in real time and records the match results. The analysis unit is the unit that analyzes the results of the matches played by the match unit. For example, the analysis unit collects data on the match results and analyzes how much the AI's technology has evolved. The analysis unit uses data analysis methods to analyze the match results in detail. For example, the analysis unit evaluates the AI's match performance and measures the degree of technological advancement. The analysis unit also identifies the AI's technological strengths and weaknesses based on the match results. The evaluation unit is the unit that evaluates the AI's technology based on the results analyzed by the analysis unit. For example, the evaluation unit performs a technical evaluation of the AI ​​based on the analysis results. The evaluation unit objectively evaluates the AI's technology using technical evaluation indicators. For example, the evaluation unit scores the AI's performance and assesses the degree of technological advancement. Furthermore, based on the analysis results, the evaluation unit identifies areas for technical improvement in the AI. Thus, the AI ​​shogi league system according to this embodiment can accelerate the technological advancement of the AI ​​by analyzing the results of matches between AIs and evaluating their technology.

[0063] The Battle Department is responsible for facilitating matches between AIs. For example, it provides a platform for AIs to play shogi (Japanese chess). Specifically, the Battle Department sets the rules and format for AI matches and manages the matches. For instance, it provides a program for AIs to play shogi according to its rules. The battle platform provides an interface for AIs to initiate matches and monitors the progress of the matches in real time. During a match, it manages each AI's turn and time limit to ensure accurate matches are conducted according to the rules. The Battle Department also records the match results and stores them in a database, allowing the Analysis and Evaluation Departments to access the results later. Furthermore, to maintain fairness, the Battle Department monitors the AI ​​match logs and checks for any cheating. For example, if abnormal behavior or a fraudulent algorithm is detected during a match, it interrupts the match and takes measures to resolve the issue. This allows the Battle Department to manage AI matches fairly and smoothly, providing a highly reliable playing environment.

[0064] The Analysis Department is responsible for analyzing the results of matches played by the Playing Department. For example, the Analysis Department collects data on match results and analyzes how much AI technology has evolved. Specifically, the Analysis Department uses data analysis methods to analyze match results in detail. For example, the Analysis Department evaluates the AI's match performance and measures the degree of technological advancement. The Analysis Department analyzes the AI's moves turn by turn, and analyzes in detail the evaluation value of each move and the fluctuations in the win rate. This allows the Analysis Department to identify the strengths and weaknesses of the AI's strategies and tactics. The Analysis Department also analyzes the AI's match data over time to grasp the trends in technological evolution. For example, it evaluates how much a particular technology or algorithm has evolved by comparing it with past match data. Furthermore, the Analysis Department analyzes the AI's match data using clustering and classification methods to clarify the differences in characteristics and performance between different AIs. This allows the Analysis Department to grasp the technological evolution of AI in detail and provide information that contributes to technological improvement.

[0065] The Evaluation Department is responsible for evaluating AI technology based on the results analyzed by the Analysis Department. For example, the Evaluation Department performs technical evaluations of AI based on the analysis results. Specifically, the Evaluation Department objectively evaluates AI technology using technical evaluation indicators. For instance, the Evaluation Department scores AI performance and assesses the degree of technical advancement. Based on the AI's match results, the Evaluation Department meticulously evaluates the strengths and weaknesses of each AI and identifies areas for technical improvement. For example, based on the AI's turn-by-turn evaluation values ​​and win rate fluctuations, it clarifies areas for improvement in strategy and tactics in specific situations. Furthermore, the Evaluation Department sets indicators for quantitatively evaluating the AI's technical advancement based on data provided by the Analysis Department. This allows the Evaluation Department to objectively and quantitatively evaluate the AI's technical advancement and provide concrete guidelines for technological improvement. Additionally, based on the evaluation results, the Evaluation Department identifies areas for technical improvement and provides feedback to developers. This allows the Evaluation Department to promote the AI's technical advancement and contribute to the technical improvement of Shogi AI.

[0066] The Support Department is responsible for establishing a system of collaborative support from corporate sponsors, local residents, and the government. For example, the Support Department will create a system where corporate sponsors provide funding, the government provides support through policies, and residents support through cheering and admission fees. The Support Department will conclude contracts with corporate sponsors to ensure they provide the necessary funds for the operation of the AI ​​Shogi League. Furthermore, the Support Department will formulate and implement policies for the government to support the AI ​​Shogi League. In addition, the Support Department will plan events to encourage local residents to support the AI ​​Shogi League through admission fees. This collaborative support from corporate sponsors, local residents, and the government will accelerate the development and research of AI. Some or all of the above processes within the Support Department may be performed using AI, or not. For example, the Support Department can use AI to automatically manage funding contracts from corporate sponsors and monitor the progress of government policy support.

[0067] The battle unit allows AIs to play against each other in shogi. The battle unit provides, for example, a platform for AIs to play against each other according to the rules of shogi. The battle unit provides a program for AIs to play against each other according to the rules of shogi. The battle unit also monitors the progress of the battle in real time and records the results. For example, when AIs play shogi, the battle unit monitors the progress of the battle in real time and saves the results to a database. This provides a platform for AIs to compete by playing shogi against each other. Some or all of the above processes in the battle unit may be performed using AI, for example, or not using AI. For example, the battle unit can automatically monitor the progress of the battle using AI and analyze the results using AI.

[0068] The analysis unit can analyze the match results and demonstrate how much the AI's technology has advanced. For example, the analysis unit collects match result data and analyzes how much the AI's technology has advanced. The analysis unit uses data analysis methods to analyze the match results in detail. For example, the analysis unit evaluates the AI's match performance and measures the degree of technological advancement. The analysis unit also identifies the AI's technological strengths and weaknesses based on the match results. In this way, by analyzing the match results, the degree of the AI's technological advancement can be clearly demonstrated. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can automatically analyze the match result data using AI and evaluate the degree of technological advancement using AI.

[0069] The evaluation unit can evaluate the AI ​​technology based on the analysis results. For example, the evaluation unit performs a technical evaluation of the AI ​​based on the analysis results. The evaluation unit objectively evaluates the AI ​​technology using technical evaluation indicators. For example, the evaluation unit scores the AI's performance and assesses the degree of technological advancement. The evaluation unit also identifies areas for technical improvement in the AI ​​based on the analysis results. In this way, by evaluating the AI ​​technology based on the analysis results, the technological advancement can be objectively assessed. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can automatically evaluate the analysis results using AI and score the degree of technological advancement using AI.

[0070] The support department can establish a system in which corporate sponsors provide funding, the government provides support through policies, and residents provide support through cheering and admission fees. For example, the support department can conclude contracts with corporate sponsors to provide the necessary funds for the operation of the AI ​​Shogi League. The support department can also formulate and implement policies for the government to support the AI ​​Shogi League. Furthermore, the support department can plan events for local residents to cheer on the AI ​​Shogi League and provide support through admission fees. In this way, the development and research of AI can be sustained through the unified support of corporate sponsors, the government, and residents. Some or all of the above processes in the support department may be carried out using AI, or not. For example, the support department can use AI to automatically manage contracts for funding from corporate sponsors and monitor the progress of government policy support using AI.

[0071] The battle unit can estimate the emotions of the AI ​​it is fighting and adjust its battle strategy based on the estimated emotions. For example, if the AI ​​is nervous, the battle unit will change its strategy to help it relax. If the AI ​​is confident, the battle unit will adopt a more challenging strategy. Also, if the AI ​​is anxious, the battle unit will adjust its strategy to help it regain its composure. By adjusting the strategy based on the AI's emotions, a more appropriate battle becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the battle unit may be performed using an AI, for example, or not using an AI. For example, the battle unit can input the emotion data of the AI ​​it is fighting into a generative AI and have the generative AI perform emotion estimation.

[0072] The battle unit can select the optimal opponent by referring to the AI's past battle history during a match. For example, the battle unit may re-select an opponent with whom it had a close match in the past. The battle unit may avoid opponents it has overwhelmingly defeated in the past, aiming for a more balanced match. The battle unit may also rematch opponents it has lost to in the past to check for advancements in skill. In this way, referencing past battle history leads to more balanced matches. Some or all of the above processes in the battle unit may be performed using AI, for example, or without AI. For example, the battle unit may input past battle history data into a generating AI and have the generating AI select the optimal opponent.

[0073] The battle unit can adjust the difficulty of a match based on the AI's learning progress. For example, the battle unit will select a high-difficulty opponent for an AI that has made significant progress in learning, and a low-difficulty opponent for an AI that has not yet made sufficient progress in learning. The battle unit will also gradually increase the difficulty of the opponents as the learning progresses. In this way, by adjusting the difficulty of the match according to the AI's learning progress, a match of appropriate difficulty is provided. Some or all of the above processing in the battle unit may be performed using an AI, or it may be performed without an AI. For example, the battle unit can input AI learning progress data into a generating AI and have the generating AI perform the adjustment of the match difficulty.

[0074] The battle unit can estimate the emotions of the AI ​​it is competing against and adjust the start time of the battle based on the estimated emotions. For example, the battle unit can start a battle when the AI ​​is relaxed, when the AI ​​is focused, or when the AI ​​is excited. By adjusting the start time of the battle based on the AI's emotions, the battle can be held at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the battle unit may be performed using an AI, or not using an AI. For example, the battle unit can input the emotion data of the AI ​​it is competing against into a generative AI and have the generative AI adjust the start time of the battle.

[0075] The battle unit can select opponents during a battle by considering the geographical distribution of the AIs. For example, the battle unit can have AIs from the same region battle each other. The battle unit can have AIs from different regions battle each other. The battle unit can also have AIs that are geographically close to each other battle each other. In this way, by considering geographical distribution, battles can be held by different regions. Some or all of the above processing in the battle unit may be performed using an AI, or it may be performed without an AI. For example, the battle unit can input geographical distribution data of the AIs into a generating AI and have the generating AI perform the selection of opponents.

[0076] The battle unit can customize the rules of a match to reflect the intentions of the AI ​​developer. For example, the battle unit can conduct a match based on specific rules set by the developer. The battle unit can adjust the time limit of the match according to the developer's intentions. The battle unit can also prohibit certain strategies based on the developer's intentions. This ensures that the match is conducted under rules that align with the developer's intentions. Some or all of the above processes in the battle unit may be performed using AI, or not. For example, the battle unit can input developer intention data into a generating AI and have the generating AI perform the customization of the match rules.

[0077] The analysis unit can estimate the AI's emotions when analyzing match results and adjust the analysis perspective based on the estimated emotions. For example, if the AI ​​was nervous, the analysis unit takes that into account. If the AI ​​was relaxed, the analysis unit takes that into account. Also, if the AI ​​was excited, the analysis unit takes that into account. By adjusting the analysis perspective based on the AI's emotions, a more appropriate analysis can be performed. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using an AI, or not using an AI. For example, the analysis unit can input match result data into a generative AI, have the generative AI perform emotion estimation, and adjust the analysis perspective.

[0078] The analysis unit can perform a detailed analysis of the strategies and methods used in the matches and evaluate the technological advancements of the AI. For example, the analysis unit can analyze the frequency of strategic changes during a match. The analysis unit can evaluate the diversity of methods used during a match. The analysis unit can also analyze the success rate of strategies used during a match. In this way, by analyzing the strategies and methods used in a match in detail, the technological advancements of the AI ​​can be evaluated. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input match data into a generating AI and have the generating AI perform the analysis of strategies and methods.

[0079] The analysis unit can track the changes in the AI's behavior over time during the analysis, thereby clarifying the evolutionary process. For example, the analysis unit tracks the behavioral patterns from the start to the end of the match. The analysis unit analyzes changes in strategy over time. The analysis unit also evaluates the changes in behavior during the match in a time series. By tracking the changes in behavior over time, the evolutionary process of the AI ​​can be clarified. Some or all of the above-described processes in the analysis unit may be performed using an AI, for example, or without an AI. For example, the analysis unit can input match data into a generating AI and have the generating AI perform the tracking of behavioral changes.

[0080] The analysis unit can estimate the AI's emotions when analyzing match results and determine the priority of analysis based on the estimated emotions. For example, if the AI ​​was nervous, the analysis unit will prioritize that analysis. If the AI ​​was relaxed, the analysis unit will prioritize that analysis. Also, if the AI ​​was excited, the analysis unit will prioritize that analysis. In this way, important analyses are prioritized by determining the priority of analysis based on the AI's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the analysis unit may be performed using an AI, for example, or not using an AI. For example, the analysis unit can input match result data into a generative AI, have the generative AI perform emotion estimation, and then determine the priority of analysis.

[0081] The analysis unit can classify the analysis results by considering the geographical factors of the matches during the analysis. For example, the analysis unit can classify the results of matches between AIs that are geographically close to each other. The analysis unit can also classify the results of matches between AIs that are geographically different. Furthermore, the analysis unit can classify the analysis results by region based on geographical factors. In this way, by considering geographical factors, analysis results for each region can be obtained. Some or all of the above processing in the analysis unit may be performed using an AI, for example, or without using an AI. For example, the analysis unit can input match result data into a generating AI and have the generating AI perform classification based on geographical factors.

[0082] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the match during the analysis process. For example, when analyzing the match results, the analysis unit refers to relevant academic papers. When analyzing the match results, the analysis unit refers to past research data. Furthermore, when analyzing the match results, the analysis unit refers to relevant technical literature. By referring to relevant literature, the accuracy of the analysis is improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0083] The evaluation unit can estimate the AI's emotions when evaluating the AI's technology based on the analysis results, and adjust the evaluation criteria based on the estimated emotions. For example, if the AI ​​is tense, the evaluation unit adjusts the evaluation criteria considering that effect. If the AI ​​is relaxed, the evaluation unit adjusts the evaluation criteria considering that effect. Also, if the AI ​​is excited, the evaluation unit adjusts the evaluation criteria considering that effect. By adjusting the evaluation criteria based on the AI's emotions, a more appropriate evaluation can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the evaluation unit may be performed using an AI, for example, or not using an AI. For example, the evaluation unit can input the AI's emotion data into a generative AI and have the generative AI perform the adjustment of the evaluation criteria.

[0084] The evaluation unit can maintain evaluation consistency by referring to the AI's past evaluation history during the evaluation process. For example, the evaluation unit performs the current evaluation based on past evaluation results. The evaluation unit unifies the evaluation criteria by referring to past evaluation history. The evaluation unit also analyzes past evaluation history to maintain evaluation consistency. In this way, evaluation consistency is maintained by referring to past evaluation history. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input past evaluation history data into a generating AI and have the generating AI perform processing to maintain evaluation consistency.

[0085] The evaluation unit can measure the rate of technological evolution of AI during evaluation and use it as an evaluation indicator. For example, the evaluation unit measures the rate of technological evolution and uses it as an evaluation indicator. The evaluation unit sets evaluation criteria based on the rate of technological evolution. The evaluation unit also analyzes the rate of technological evolution and uses it as an evaluation indicator. In this way, the evaluation indicator becomes clear by measuring the rate of technological evolution. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input technological evolution rate data into a generating AI and have the generating AI execute the evaluation indicator.

[0086] The evaluation unit can estimate the AI's emotions when evaluating the AI's technology based on the analysis results, and determine the evaluation priority based on the estimated emotions. For example, if the AI ​​was tense, the evaluation unit will prioritize that evaluation. If the AI ​​was relaxed, the evaluation unit will prioritize that evaluation. Also, if the AI ​​was excited, the evaluation unit will prioritize that evaluation. In this way, by determining the evaluation priority based on the AI's emotions, important evaluations are prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the evaluation unit may be performed using an AI, for example, or not using an AI. For example, the evaluation unit can input the AI's emotion data into a generative AI and have the generative AI perform the process of determining the evaluation priority.

[0087] The evaluation unit can classify evaluation results while considering the geographical distribution of the AIs during evaluation. For example, the evaluation unit can classify evaluation results of AIs that are geographically close to each other. The evaluation unit can also classify evaluation results of AIs that are geographically different to each other. Furthermore, the evaluation unit can classify evaluation results by region based on geographical factors. In this way, evaluation results for each region can be obtained by considering the geographical distribution. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input evaluation result data into a generating AI and have the generating AI perform classification based on geographical factors.

[0088] The evaluation unit can improve the accuracy of its evaluation by referring to relevant AI literature during the evaluation process. For example, the evaluation unit refers to relevant academic papers when analyzing the evaluation results. The evaluation unit refers to past research data when analyzing the evaluation results. The evaluation unit also refers to relevant technical literature when analyzing the evaluation results. By referring to relevant literature, the accuracy of the evaluation is improved. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input relevant literature data into a generating AI and have the generating AI perform the evaluation accuracy improvement.

[0089] The support department, when setting up a support system, can estimate the emotions of the supporter and adjust the support method based on the estimated emotions. For example, if the supporter is excited, the support department will suggest an aggressive support method. If the supporter is relaxed, the support department will suggest a gentle support method. Also, if the supporter is tense, the support department will suggest a reassuring support method. In this way, more effective support is provided by adjusting the support method based on the supporter's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the supporter's emotion data into a generative AI and have the generative AI perform the adjustment of the support method.

[0090] The support department can select the optimal support method by referring to past support history when establishing a support system. For example, the support department may propose a support method that has been successful in the past. The support department analyzes past support history and selects the optimal support method. In addition, the support department proposes a new support method based on past support history. In this way, the optimal support method is selected by referring to past support history. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input past support history data into a generating AI and have the generating AI perform the selection of the optimal support method.

[0091] The support department can estimate the emotions of supporters when setting up a support system and determine the priority of support based on the estimated emotions. For example, if a supporter is excited, the support department will prioritize that support. If a supporter is relaxed, the support department will prioritize that support. Also, if a supporter is tense, the support department will prioritize that support. In this way, by determining the priority of support based on the emotions of supporters, important support is prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support department may be performed using AI, for example, or not using AI. For example, the support department can input supporter emotion data into a generative AI and have the generative AI perform the process of determining the priority of support.

[0092] The support department can customize support methods when establishing a support system, taking into account the geographical distribution of supporters. For example, the support department may propose direct support methods to geographically close supporters, and online support methods to geographically distant supporters. Furthermore, the support department customizes support methods for each region based on geographical distribution. This ensures that the most suitable support method is provided for each region by considering geographical distribution. Some or all of the above processing in the support department may be performed using AI, for example, or without AI. For example, the support department can input geographical distribution data of supporters into a generating AI and have the generating AI perform the customization of support methods.

[0093] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0094] The battle unit can estimate the emotions of the AI ​​it is fighting and adjust its battle strategy based on the estimated emotions. For example, if the AI ​​is nervous, the battle unit will change its strategy to help it relax. If the AI ​​is confident, the battle unit will adopt a more challenging strategy. Also, if the AI ​​is anxious, the battle unit will adjust its strategy to help it regain its composure. By adjusting the strategy based on the AI's emotions, a more appropriate battle becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the battle unit may be performed using an AI, for example, or not using an AI. For example, the battle unit can input the emotion data of the AI ​​it is fighting into a generative AI and have the generative AI perform emotion estimation.

[0095] The battle unit can select the optimal opponent by referring to the AI's past battle history during a match. For example, the battle unit may re-select an opponent with whom it had a close match in the past. The battle unit may avoid opponents it has overwhelmingly defeated in the past, aiming for a more balanced match. The battle unit may also rematch opponents it has lost to in the past to check for advancements in skill. In this way, referencing past battle history leads to more balanced matches. Some or all of the above processes in the battle unit may be performed using AI, for example, or without AI. For example, the battle unit may input past battle history data into a generating AI and have the generating AI select the optimal opponent.

[0096] The battle unit can adjust the difficulty of a match based on the AI's learning progress. For example, the battle unit will select a high-difficulty opponent for an AI that has made significant progress in learning, and a low-difficulty opponent for an AI that has not yet made sufficient progress in learning. The battle unit will also gradually increase the difficulty of the opponents as the learning progresses. In this way, by adjusting the difficulty of the match according to the AI's learning progress, a match of appropriate difficulty is provided. Some or all of the above processing in the battle unit may be performed using an AI, or it may be performed without an AI. For example, the battle unit can input AI learning progress data into a generating AI and have the generating AI perform the adjustment of the match difficulty.

[0097] The battle unit can estimate the emotions of the AI ​​it is competing against and adjust the start time of the battle based on the estimated emotions. For example, the battle unit can start a battle when the AI ​​is relaxed, when the AI ​​is focused, or when the AI ​​is excited. By adjusting the start time of the battle based on the AI's emotions, the battle can be held at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the battle unit may be performed using an AI, or not using an AI. For example, the battle unit can input the emotion data of the AI ​​it is competing against into a generative AI and have the generative AI adjust the start time of the battle.

[0098] The battle unit can select opponents during a battle by considering the geographical distribution of the AIs. For example, the battle unit can have AIs from the same region battle each other. The battle unit can have AIs from different regions battle each other. The battle unit can also have AIs that are geographically close to each other battle each other. In this way, by considering geographical distribution, battles can be held by different regions. Some or all of the above processing in the battle unit may be performed using an AI, or it may be performed without an AI. For example, the battle unit can input geographical distribution data of the AIs into a generating AI and have the generating AI perform the selection of opponents.

[0099] The battle unit can customize the rules of a match to reflect the intentions of the AI ​​developer. For example, the battle unit can conduct a match based on specific rules set by the developer. The battle unit can adjust the time limit of the match according to the developer's intentions. The battle unit can also prohibit certain strategies based on the developer's intentions. This ensures that the match is conducted under rules that align with the developer's intentions. Some or all of the above processes in the battle unit may be performed using AI, or not. For example, the battle unit can input developer intention data into a generating AI and have the generating AI perform the customization of the match rules.

[0100] The analysis unit can estimate the AI's emotions when analyzing match results and adjust the analysis perspective based on the estimated emotions. For example, if the AI ​​was nervous, the analysis unit takes that into account. If the AI ​​was relaxed, the analysis unit takes that into account. Also, if the AI ​​was excited, the analysis unit takes that into account. By adjusting the analysis perspective based on the AI's emotions, a more appropriate analysis can be performed. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using an AI, or not using an AI. For example, the analysis unit can input match result data into a generative AI, have the generative AI perform emotion estimation, and adjust the analysis perspective.

[0101] The analysis unit can perform a detailed analysis of the strategies and methods used in the matches and evaluate the technological advancements of the AI. For example, the analysis unit can analyze the frequency of strategic changes during a match. The analysis unit can evaluate the diversity of methods used during a match. The analysis unit can also analyze the success rate of strategies used during a match. In this way, by analyzing the strategies and methods used in a match in detail, the technological advancements of the AI ​​can be evaluated. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input match data into a generating AI and have the generating AI perform the analysis of strategies and methods.

[0102] The analysis unit can track the changes in the AI's behavior over time during the analysis, thereby clarifying the evolutionary process. For example, the analysis unit tracks the behavioral patterns from the start to the end of the match. The analysis unit analyzes changes in strategy over time. The analysis unit also evaluates the changes in behavior during the match in a time series. By tracking the changes in behavior over time, the evolutionary process of the AI ​​can be clarified. Some or all of the above-described processes in the analysis unit may be performed using an AI, for example, or without an AI. For example, the analysis unit can input match data into a generating AI and have the generating AI perform the tracking of behavioral changes.

[0103] The analysis unit can estimate the AI's emotions when analyzing match results and determine the priority of analysis based on the estimated emotions. For example, if the AI ​​was nervous, the analysis unit will prioritize that analysis. If the AI ​​was relaxed, the analysis unit will prioritize that analysis. Also, if the AI ​​was excited, the analysis unit will prioritize that analysis. In this way, important analyses are prioritized by determining the priority of analysis based on the AI's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the analysis unit may be performed using an AI, for example, or not using an AI. For example, the analysis unit can input match result data into a generative AI, have the generative AI perform emotion estimation, and then determine the priority of analysis.

[0104] The following briefly describes the processing flow for example form 2.

[0105] Step 1: The Battle Department is the department for AIs to compete against each other. For example, the Battle Department provides a platform for AIs to play against each other in shogi (Japanese chess). The Battle Department sets the rules and format for the AIs to compete and manages the matches. For example, the Battle Department provides a program for AIs to play shogi according to the rules. The Battle Department also monitors the progress of the matches in real time and records the results. Step 2: The Analysis Department is responsible for analyzing the results of matches played by the Match Department. For example, the Analysis Department collects match result data and analyzes how much the AI's technology has advanced. The Analysis Department uses data analysis methods to analyze the match results in detail. For example, the Analysis Department evaluates the AI's match performance and measures the degree of technological advancement. The Analysis Department also identifies the AI's technological strengths and weaknesses based on the match results. Step 3: The Evaluation Department is responsible for evaluating the AI's technology based on the results analyzed by the Analysis Department. For example, the Evaluation Department performs a technical evaluation of the AI ​​based on the analysis results. The Evaluation Department objectively evaluates the AI's technology using technical evaluation indicators. For example, the Evaluation Department scores the AI's performance and assesses the degree of technological advancement. The Evaluation Department also identifies areas for technical improvement in the AI ​​based on the analysis results.

[0106] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0107] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0108] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0109] Each of the multiple elements described above, including the battle unit, analysis unit, evaluation unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the battle unit is implemented by the control unit 46A of the smart device 14 and manages battles between AIs. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the battle results. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the AI's technology based on the analysis results. The support unit is implemented by the control unit 46A of the smart device 14 and establishes a system of support in which corporate sponsors, local residents, and government agencies work together. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0110] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0111] As shown in Figure 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.

[0112] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0116] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0117] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0118] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0119] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0120] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0122] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0124] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements described above, including the battle unit, analysis unit, evaluation unit, and support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the battle unit is implemented by the control unit 46A of the smart glasses 214 and manages battles between AIs. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the battle results. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the AI's technology based on the analysis results. The support unit is implemented by the control unit 46A of the smart glasses 214 and establishes a system of support in which corporate sponsors, local residents, and the government work together. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0126] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0127] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0130] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the battle unit, analysis unit, evaluation unit, and support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the battle unit is implemented by the control unit 46A of the headset terminal 314 and manages battles between AIs. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the battle results. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the AI's technology based on the analysis results. The support unit is implemented by the control unit 46A of the headset terminal 314 and establishes a system of support in which corporate sponsors, local residents, and the government work together. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0142] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0143] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0146] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0150] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the battle unit, analysis unit, evaluation unit, and support unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the battle unit is implemented by the control unit 46A of the robot 414 and manages battles between AIs. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the battle results. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the AI's technology based on the analysis results. The support unit is implemented by the control unit 46A of the robot 414 and establishes a system of support in which corporate sponsors, local residents, and the government work together. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0159] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0161] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0162] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0163] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0167] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0168] 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.

[0169] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0170] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0171] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0172] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0174] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0175] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0176] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0177] (Note 1) A battle section for AI to compete against each other, An analysis unit that analyzes the results of the matches played by the aforementioned match unit, An evaluation unit that evaluates the AI ​​technology based on the results analyzed by the aforementioned analysis unit, Equipped with A system characterized by the following features. (Note 2) The department will be equipped with a support division to create a system where corporate sponsors, local residents, and the government work together to provide support. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned battle section is, AIs play against each other in a game of shogi. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Analyze the match results to show how much AI technology has advanced. The system described in Appendix 1, characterized by the features described herein. (Note 5) The evaluation unit, Evaluate AI technology based on analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support unit, The system will be structured so that corporate sponsors provide funding, the government supports the system through policies, and residents support it through cheering and admission fees. The system described in Appendix 2, characterized by the features described herein. (Note 7) The aforementioned battle section is, It estimates the emotions of the opposing AI and adjusts the battle strategy based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned battle section is, During a match, the AI ​​selects the optimal opponent by referring to its past match history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned battle section is, During a match, the difficulty level is adjusted based on the AI's learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned battle section is, It estimates the emotions of the AI ​​opponent and adjusts the start time of the match based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned battle section is, When playing a match, the AI ​​selects an opponent considering its geographical distribution. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned battle section is, During a match, the rules of the match are customized to reflect the intentions of the AI ​​developer. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, When analyzing match results, the AI's emotions are estimated, and the perspective of the analysis is adjusted based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, we will analyze the strategies and methods of the matches in detail and evaluate the technological advancements of the AI. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, we track the changes in the AI's behavior as the match progresses, clarifying the evolutionary process. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, When analyzing match results, the AI's emotions are estimated, and the analysis priorities are determined based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the geographical factors of the matches are taken into consideration when classifying the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant literature on the matches to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, When evaluating AI technology based on analysis results, we estimate the AI's emotions and adjust the evaluation criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, During the evaluation process, the AI's past evaluation history is referenced to maintain evaluation consistency. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, During the evaluation process, the rate of technological advancement of AI will be measured and used as an indicator for evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, When evaluating AI technology based on analysis results, the AI's emotions are estimated, and the evaluation priorities are determined based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, During evaluation, the evaluation results are classified considering the geographical distribution of the AI. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, During evaluation, we refer to relevant AI literature to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit, When establishing a support system, estimate the emotions of the supporters and adjust the support methods based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned support unit, When establishing a support system, refer to past support history to select the most suitable support method. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned support unit, When establishing a support system, the emotions of the supporters are estimated, and the priority of support is determined based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned support unit, When establishing a support system, customize the support methods by taking into account the geographical distribution of supporters. The system described in Appendix 2, characterized by the features described herein. [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A battle section for AIs to compete against each other, An analysis unit that analyzes the results of the matches played by the aforementioned match unit, An evaluation unit that evaluates AI technology based on the results analyzed by the aforementioned analysis unit, Equipped with A system characterized by the following features.

2. The department will be equipped with a support division to create a system where corporate sponsors, local residents, and the government work together to provide support. The system according to feature 1.

3. The aforementioned battle section is, AIs play against each other in a game of shogi. The system according to feature 1.

4. The aforementioned analysis unit, Analyzing the match results will show how much AI technology has advanced. The system according to feature 1.

5. The evaluation unit, Evaluate AI technology based on analysis results. The system according to feature 1.

6. The aforementioned support unit, The system will be structured so that corporate sponsors provide funding, the government supports the system through policies, and residents support it through cheering and admission fees. The system according to feature 2.

7. The aforementioned battle section is, It estimates the emotions of the opposing AI and adjusts the battle strategy based on those estimated emotions. The system according to feature 1.

8. The aforementioned battle section is, During a match, the AI ​​selects the optimal opponent by referring to its past match history. The system according to feature 1.

9. The aforementioned battle section is, The difficulty level of the match will be adjusted based on the AI's learning progress. The system according to feature 1.

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