System

Abstract

An object of the system according to the embodiment is to model a thought pattern of a judge and propose an optimal strategy.SOLUTION: A system includes a precedent collection part, a precedent analysis part, a thinking pattern modeling part, and a strategy proposal part. A case collection part collects past case data of a judge. A case analysis part analyzes the case data collected by the case collection part. The thought pattern modeling unit models a thought pattern of a judge based on the data analyzed by the judge case analysis unit. The strategy proposing section proposes an optimum strategy based on the thinking pattern modeled by the thinking pattern modeling section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to understand the judge's thought patterns and develop optimal strategies.

[0005] The system according to the embodiment aims to model the thought patterns of judges and propose optimal strategies. [Means for solving the problem]

[0006] The system according to the embodiment includes a case law collection unit, a case law analysis unit, a thought pattern modeling unit, and a strategy proposal unit. The case law collection unit collects past case law data of judges. The case law analysis unit analyzes the case law data collected by the case law collection unit. The thought pattern modeling unit models the thought pattern of the judge based on the data analyzed by the case law analysis unit. The strategy proposal unit proposes an optimal fighting strategy based on the thought pattern modeled by the thought pattern modeling unit. [Effects of the Invention]

[0007] The system according to the embodiment can model the thought patterns of judges and suggest optimal strategies. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The trial support system according to an embodiment of the present invention uses AI to analyze past precedents of judges and provide optimal strategies for litigation. This allows the trial support system to realize strategies that are in line with the judge's thinking, enabling the provision of trials that cannot be lost.

[0029] The trial support system according to the embodiment includes a case collection unit, a case analysis unit, a thought pattern modeling unit, and a strategy proposal unit. The case collection unit collects past case data of judges. For example, the case collection unit obtains judgments for each judge from a case database. The case collection unit can also collect court records and related legal documents. For example, the case collection unit downloads judgments from the court's official website. The case analysis unit analyzes the case data collected by the case collection unit. For example, the case analysis unit analyzes the content of the judgments using text mining technology. The case analysis unit can also analyze the structure of the judgments using natural language processing technology. For example, the case analysis unit extracts keywords from the judgments and analyzes their frequency. The thought pattern modeling unit models the judge's thought pattern based on the data analyzed by the case analysis unit. For example, the thought pattern modeling unit models the judge's decision-making criteria using a machine learning algorithm. The thought pattern modeling unit can also model a judge's priorities using statistical techniques. For example, the thought pattern modeling unit models what laws and facts a judge places importance on. The strategy proposal unit proposes an optimal strategy based on the thought patterns modeled by the thought pattern modeling unit. For example, the strategy proposal unit proposes a method of presenting evidence for a specific judge. The strategy proposal unit can also propose a strategy that focuses on the selection of witnesses. For example, if a judge places importance on the credibility of evidence, the strategy proposal unit proposes a strategy for presenting highly reliable evidence. This allows the trial support system according to the embodiment to realize a strategy that is in line with the judge's thinking and provide a trial that cannot be lost. For example, the trial support system analyzes the judge's past cases and proposes an optimal strategy for that judge. The trial support system also collects the results of the proposed strategy as feedback and improves the accuracy of proposals from the next time onwards.

[0030] The precedent collection unit collects not only the judges' case data but also their public statements and papers, and the generative AI analyzes them to gain an overall picture of the judges' legal thinking. For example, the precedent collection unit collects the judges' publicly published papers and lectures in addition to the judges' case data, and the generative AI analyzes them. For example, it analyzes papers in which the judges discuss specific legal interpretations to understand trends in those interpretations. The precedent collection unit also collects interviews and comments made by the judges in the media, and the generative AI analyzes them. For example, it analyzes comments made by the judges about specific cases to understand their views on those cases. The precedent collection unit also collects books and articles written by the judges in specialist journals, and the generative AI analyzes them. For example, it analyzes articles in which the judges discuss specific legal issues to understand their views on those issues. This allows for an overall picture of the judges' legal thinking, making it possible to propose more accurate strategies.

[0031] The case analysis unit extracts characteristics of the style and phrasing of judgments and can model the personal tendencies of judges. For example, the case analysis unit analyzes a judge's case data and extracts characteristics of the style and phrasing of judgments. For example, it identifies legal terms and phrases frequently used by a particular judge and models those tendencies. The case analysis unit also analyzes a judge's case data and extracts characteristics of the structure and logical development of judgments. For example, it analyzes the order in which a particular judge organizes arguments and models those tendencies. The case analysis unit also analyzes a judge's case data and extracts characteristics of emotional expressions and emphasis in judgments. For example, it analyzes the situations in which a particular judge uses emotional expressions and models those tendencies. This makes it possible to understand a judge's personal tendencies and propose more appropriate strategies.

[0032] The precedent collection unit collects case data from other judges of the same court in addition to the judge's case data, and the generation AI compares and analyzes them to understand trends across the court. For example, the precedent collection unit collects case data from other judges of the same court, and the generation AI compares and analyzes them. For example, it analyzes the types of decisions that judges of the same court tend to make, thereby understanding trends across the court. In addition to the judge's case data, the precedent collection unit collects case data from other judges of the same court, and the generation AI analyzes them. For example, it analyzes how judges of the same court interpret law, thereby understanding trends across the court. In addition to the judge's case data, the precedent collection unit collects case data from other judges of the same court, and the generation AI analyzes them. For example, it analyzes what type of evidence judges of the same court emphasize, thereby understanding trends across the court. This allows for understanding trends across the court, making it possible to propose strategies from a broader perspective.

[0033] When analyzing the case data of judges, the precedent collection unit also collects case data of judges from different countries and regions, allowing the generation AI to perform comparative analysis from an international perspective. For example, the case collection unit collects case data of judges from different countries and regions, and the generation AI performs comparative analysis on them. For example, it analyzes how judges from different countries issue decisions on the same legal issue and performs comparisons from an international perspective. In addition to the judge's case data, the case collection unit also collects case data of judges from different countries and regions, and the generation AI analyzes them. For example, it analyzes how judges from different regions interpret law and performs comparisons from an international perspective. In addition to the judge's case data, the case collection unit also collects case data of judges from different countries and regions, and the generation AI analyzes them. For example, it analyzes what evidence judges from different countries emphasize and performs comparisons from an international perspective. This allows for comparative analysis from an international perspective to propose more multifaceted strategies.

[0034] The thought pattern modeling unit can take into account the personal background information of a judge and construct a thought pattern based thereon. The thought pattern modeling unit, for example, takes into account the personal background information of a judge (e.g., educational background and work history) and constructs a thought pattern based thereon. For example, it analyzes what kind of educational background and work history a particular judge has and constructs a thought pattern based on that background. The thought pattern modeling unit also takes into account the personal background information of a judge (e.g., educational background and work history) and constructs a thought pattern based thereon. For example, it analyzes what kind of specialty a particular judge has and constructs a thought pattern based on that background. The thought pattern modeling unit also takes into account the personal background information of a judge (e.g., educational background and work history) and constructs a thought pattern based thereon. For example, it analyzes what kind of work history a particular judge has and constructs a thought pattern based on that background. In this way, by taking into account the personal background information of a judge, it is possible to construct a more accurate thought pattern.

[0035] The thought pattern modeling unit can also model the thought patterns of judges in different legal systems and perform comparative analysis. The thought pattern modeling unit, for example, models the thought patterns of judges in different legal systems (e.g., common law and civil law) and performs comparative analysis. For example, it analyzes how common law judges and civil law judges have different thought patterns. The thought pattern modeling unit also models the thought patterns of judges in different legal systems (e.g., common law and civil law) and performs comparative analysis. For example, it analyzes what legal grounds common law judges emphasize and compares them with civil law judges. The thought pattern modeling unit also models the thought patterns of judges in different legal systems (e.g., common law and civil law) and performs comparative analysis. For example, it analyzes what evidence civil law judges emphasize and compares them with common law judges. In this way, by comparing and analyzing the thought patterns of judges in different legal systems, it is possible to propose strategies from a more multifaceted perspective.

[0036] The thought pattern modeling unit can construct thought patterns for each field using case law data from different fields. The thought pattern modeling unit, for example, constructs a judge's thought pattern using case law data from different fields (e.g., criminal cases and civil cases). For example, it compares and analyzes the judge's thought pattern in criminal cases and the thought pattern in civil cases. The thought pattern modeling unit also constructs a judge's thought pattern using case law data from different fields (e.g., criminal cases and civil cases). For example, it analyzes the degree of importance of evidence in criminal cases and the degree of importance of evidence in civil cases. The thought pattern modeling unit also constructs a judge's thought pattern using case law data from different fields (e.g., criminal cases and civil cases). For example, it analyzes the frequency of citation of legal grounds in criminal cases and the frequency of citation in civil cases. In this way, by using case law data from different fields, it is possible to construct thought patterns for each field.

[0037] The strategy proposal unit performs simulations of the strategy proposed by the generation AI based on past court results, and can evaluate the effectiveness of the proposal in advance. For example, the strategy proposal unit performs simulations of the strategy proposed by the generation AI based on past court results. For example, it evaluates the effectiveness of the proposed strategy based on the results of similar past cases. The strategy proposal unit also performs simulations of the strategy proposed by the generation AI based on past court results. For example, it simulates the influence of specific evidence or witnesses and evaluates the effectiveness of the proposed strategy. The strategy proposal unit also performs simulations of the strategy proposed by the generation AI based on past court results. For example, it simulates the influence of specific legal interpretations and evaluates the effectiveness of the proposed strategy. This allows the provision of more reliable strategies by evaluating the effectiveness of the proposal in advance.

[0038] The strategy proposal unit can consider different scenarios for the strategy proposed by the generation AI and compare and evaluate multiple strategies. For example, the strategy proposal unit considers different scenarios for the strategy proposed by the generation AI and compares and evaluates multiple strategies. For example, it simulates the addition of evidence or the change of witnesses and selects the optimal strategy. The strategy proposal unit also considers different scenarios for the strategy proposed by the generation AI and compares and evaluates multiple strategies. For example, it simulates different legal interpretations and selects the optimal strategy. The strategy proposal unit also considers different scenarios for the strategy proposed by the generation AI and compares and evaluates multiple strategies. For example, it simulates the testimony of different witnesses and selects the optimal strategy. In this way, the optimal strategy can be selected by considering different scenarios.

[0039] The strategy proposal unit can apply the strategy proposed by the generative AI to different legal systems and construct a strategy from an international perspective. For example, the strategy proposal unit can apply the strategy proposed by the generative AI to different legal systems (e.g., international court cases) and construct a strategy from an international perspective. For example, it can analyze how evidence is presented in international court cases and propose the optimal strategy. The strategy proposal unit can also apply the strategy proposed by the generative AI to different legal systems (e.g., international court cases) and construct a strategy from an international perspective. For example, it can analyze the legal interpretations of different countries and propose the optimal strategy. The strategy proposal unit can also apply the strategy proposed by the generative AI to different legal systems (e.g., international court cases) and construct a strategy from an international perspective. For example, it can analyze the thinking patterns of judges from different countries and propose the optimal strategy. In this way, by constructing a strategy from an international perspective, it is possible to propose a more diversified strategy.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] In addition to the judges' case data, the precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI can analyze. For example, it analyzes what kind of mediation and arbitration results the judges reached and identifies trends. The precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI analyzes. For example, it analyzes what mediation and arbitration methods the judges used and identifies trends. The precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI analyzes. For example, it analyzes what kind of mediation and arbitration results the judges place importance on and identifies trends. This makes it possible to understand the judges' mediation and arbitration trends and propose more appropriate strategies.

[0042] The precedent analysis unit analyzes not only the judge's case data but also data on past mediations and arbitrations in which the judge was involved, making it possible to grasp the judge's mediation and arbitration tendencies. For example, it analyzes what kind of mediation and arbitration results the judge achieved and grasps those trends. The precedent analysis unit also analyzes data on past mediations and arbitrations in which the judge was involved to grasp the judge's mediation and arbitration tendencies. For example, it analyzes what kind of mediation and arbitration methods the judge used and grasps those trends. The precedent analysis unit also analyzes data on past mediations and arbitrations in which the judge was involved to grasp the judge's mediation and arbitration tendencies. For example, it analyzes what kind of mediation and arbitration results the judge places importance on and grasps those trends. In this way, by understanding the judge's mediation and arbitration tendencies, it is possible to propose more appropriate strategies.

[0043] In addition to the judges' case data, the precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI can analyze. For example, it analyzes what kind of mediation and arbitration results the judges reached and identifies trends. The precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI analyzes. For example, it analyzes what mediation and arbitration methods the judges used and identifies trends. The precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI analyzes. For example, it analyzes what kind of mediation and arbitration results the judges place importance on and identifies trends. This makes it possible to understand the judges' mediation and arbitration trends and propose more appropriate strategies.

[0044] In addition to the judges' case data, the precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI can analyze. For example, it analyzes what kind of mediation and arbitration results the judges reached and identifies trends. The precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI analyzes. For example, it analyzes what mediation and arbitration methods the judges used and identifies trends. The precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI analyzes. For example, it analyzes what kind of mediation and arbitration results the judges place importance on and identifies trends. This makes it possible to understand the judges' mediation and arbitration trends and propose more appropriate strategies.

[0045] In addition to the judges' case data, the precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI can analyze. For example, it analyzes what kind of mediation and arbitration results the judges reached and identifies trends. The precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI analyzes. For example, it analyzes what mediation and arbitration methods the judges used and identifies trends. The precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI analyzes. For example, it analyzes what kind of mediation and arbitration results the judges place importance on and identifies trends. This makes it possible to understand the judges' mediation and arbitration trends and propose more appropriate strategies.

[0046] In addition to the judges' case data, the precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI can analyze. For example, it analyzes what kind of mediation and arbitration results the judges reached and identifies trends. The precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI analyzes. For example, it analyzes what mediation and arbitration methods the judges used and identifies trends. The precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI analyzes. For example, it analyzes what kind of mediation and arbitration results the judges place importance on and identifies trends. This makes it possible to understand the judges' mediation and arbitration trends and propose more appropriate strategies.

[0047] In addition to the judges' case data, the precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI can analyze. For example, it analyzes what kind of mediation and arbitration results the judges reached and identifies trends. The precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI analyzes. For example, it analyzes what mediation and arbitration methods the judges used and identifies trends. The precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI analyzes. For example, it analyzes what kind of mediation and arbitration results the judges place importance on and identifies trends. This makes it possible to understand the judges' mediation and arbitration trends and propose more appropriate strategies.

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

[0049] Step 1: The case collection unit collects past case data of judges. For example, the case collection unit retrieves the judgments of each judge from the case database. The case collection unit may also collect court records and related legal documents. For example, the case collection unit downloads judgments from the court's official website. Step 2: The case analysis unit analyzes the case data collected by the case collection unit. For example, the case analysis unit analyzes the content of the judgment sentence using text mining technology. The case analysis unit can also analyze the structure of the judgment sentence using natural language processing technology. For example, the case analysis unit extracts keywords from the judgment sentence and analyzes their frequency. Step 3: The thought pattern modeling unit models the judge's thought patterns based on the data analyzed by the case analysis unit. For example, the thought pattern modeling unit uses a machine learning algorithm to model the judge's decision-making criteria. The thought pattern modeling unit can also model the judge's priorities using statistical methods. For example, the thought pattern modeling unit models what laws and facts the judge places importance on. Step 4: The Strategy Proposal Unit proposes the optimal way to fight based on the thought patterns modeled by the Thought Pattern Modeling Unit. For example, the Strategy Proposal Unit may propose a method for presenting evidence to a specific judge. The Strategy Proposal Unit may also propose a strategy that focuses on the selection of witnesses. For example, if the judge places importance on the credibility of evidence, the Strategy Proposal Unit may propose a strategy for presenting highly reliable evidence.

[0050] (Example 2) The trial support system according to an embodiment of the present invention uses AI to analyze past precedents of judges and provide optimal strategies for litigation. This allows the trial support system to realize strategies that are in line with the judge's thinking, enabling the provision of trials that cannot be lost.

[0051] The trial support system according to the embodiment includes a case collection unit, a case analysis unit, a thought pattern modeling unit, and a strategy proposal unit. The case collection unit collects past case data of judges. For example, the case collection unit obtains judgments for each judge from a case database. The case collection unit can also collect court records and related legal documents. For example, the case collection unit downloads judgments from the court's official website. The case analysis unit analyzes the case data collected by the case collection unit. For example, the case analysis unit analyzes the content of the judgments using text mining technology. The case analysis unit can also analyze the structure of the judgments using natural language processing technology. For example, the case analysis unit extracts keywords from the judgments and analyzes their frequency. The thought pattern modeling unit models the judge's thought pattern based on the data analyzed by the case analysis unit. For example, the thought pattern modeling unit models the judge's decision-making criteria using a machine learning algorithm. The thought pattern modeling unit can also model a judge's priorities using statistical techniques. For example, the thought pattern modeling unit models what laws and facts a judge places importance on. The strategy proposal unit proposes an optimal strategy based on the thought patterns modeled by the thought pattern modeling unit. For example, the strategy proposal unit proposes a method of presenting evidence for a specific judge. The strategy proposal unit can also propose a strategy that focuses on the selection of witnesses. For example, if a judge places importance on the credibility of evidence, the strategy proposal unit proposes a strategy for presenting highly reliable evidence. This allows the trial support system according to the embodiment to realize a strategy that is in line with the judge's thinking and provide a trial that cannot be lost. For example, the trial support system analyzes the judge's past cases and proposes an optimal strategy for that judge. The trial support system also collects the results of the proposed strategy as feedback and improves the accuracy of proposals from the next time onwards.

[0052] The precedent collection unit collects not only the judges' case data but also their public statements and papers, and the generative AI analyzes them to gain an overall picture of the judges' legal thinking. For example, the precedent collection unit collects the judges' publicly published papers and lectures in addition to the judges' case data, and the generative AI analyzes them. For example, it analyzes papers in which the judges discuss specific legal interpretations to understand trends in those interpretations. The precedent collection unit also collects interviews and comments made by the judges in the media, and the generative AI analyzes them. For example, it analyzes comments made by the judges about specific cases to understand their views on those cases. The precedent collection unit also collects books and articles written by the judges in specialist journals, and the generative AI analyzes them. For example, it analyzes articles in which the judges discuss specific legal issues to understand their views on those issues. This allows for an overall picture of the judges' legal thinking, making it possible to propose more accurate strategies.

[0053] The case analysis unit extracts characteristics of the style and phrasing of judgments and can model the personal tendencies of judges. For example, the case analysis unit analyzes a judge's case data and extracts characteristics of the style and phrasing of judgments. For example, it identifies legal terms and phrases frequently used by a particular judge and models those tendencies. The case analysis unit also analyzes a judge's case data and extracts characteristics of the structure and logical development of judgments. For example, it analyzes the order in which a particular judge organizes arguments and models those tendencies. The case analysis unit also analyzes a judge's case data and extracts characteristics of emotional expressions and emphasis in judgments. For example, it analyzes the situations in which a particular judge uses emotional expressions and models those tendencies. This makes it possible to understand a judge's personal tendencies and propose more appropriate strategies.

[0054] The case law analysis unit uses the emotion estimation function to analyze emotional expressions in the judgment and understand the emotional tendencies of the judge. The case law analysis unit, for example, uses the emotion estimation function to analyze emotional expressions in the judgment. For example, it identifies emotional words and phrases used by the judge in specific situations and understands their tendencies. The case law analysis unit also uses the emotion estimation function to analyze emotional emphasis and tone in the judgment. For example, it analyzes what emotional emphasis the judge places on specific points and understands their tendencies. The case law analysis unit also uses the emotion estimation function to analyze emotional fluctuations in the judgment. For example, it analyzes how the judge changes their emotional tone before and after the judgment and understands their tendencies. This allows the emotional tendencies of the judge to be understood and allows for the proposal of strategies that take emotions into consideration.

[0055] The precedent collection unit collects case data from other judges of the same court in addition to the judge's case data, and the generation AI compares and analyzes them to understand trends across the court. For example, the precedent collection unit collects case data from other judges of the same court, and the generation AI compares and analyzes them. For example, it analyzes the types of decisions that judges of the same court tend to make, thereby understanding trends across the court. In addition to the judge's case data, the precedent collection unit collects case data from other judges of the same court, and the generation AI analyzes them. For example, it analyzes how judges of the same court interpret law, thereby understanding trends across the court. In addition to the judge's case data, the precedent collection unit collects case data from other judges of the same court, and the generation AI analyzes them. For example, it analyzes what type of evidence judges of the same court emphasize, thereby understanding trends across the court. This allows for understanding trends across the court, making it possible to propose strategies from a broader perspective.

[0056] When analyzing the case data of judges, the precedent collection unit also collects case data of judges from different countries and regions, allowing the generation AI to perform comparative analysis from an international perspective. For example, the case collection unit collects case data of judges from different countries and regions, and the generation AI performs comparative analysis on them. For example, it analyzes how judges from different countries issue decisions on the same legal issue and performs comparisons from an international perspective. In addition to the judge's case data, the case collection unit also collects case data of judges from different countries and regions, and the generation AI analyzes them. For example, it analyzes how judges from different regions interpret law and performs comparisons from an international perspective. In addition to the judge's case data, the case collection unit also collects case data of judges from different countries and regions, and the generation AI analyzes them. For example, it analyzes what evidence judges from different countries emphasize and performs comparisons from an international perspective. This allows for comparative analysis from an international perspective to propose more multifaceted strategies.

[0057] The precedent analysis unit uses the emotion estimation function to analyze, in real time, the emotional fluctuations of a judge when making a decision, and can propose a strategy in accordance with the judge's emotional changes. The precedent analysis unit, for example, uses the emotion estimation function to analyze, in real time, the emotional fluctuations of a judge when making a decision. For example, it analyzes what emotional reaction a judge will have when viewing specific evidence, and proposes a strategy in accordance with those fluctuations. The precedent analysis unit also uses the emotion estimation function to analyze, in real time, the emotional fluctuations of a judge when making a decision. For example, it analyzes what emotional reaction a judge will have when hearing the testimony of a specific witness, and proposes a strategy in accordance with those fluctuations. The precedent analysis unit also uses the emotion estimation function to analyze, in real time, the emotional fluctuations of a judge when making a decision. For example, it analyzes what emotional reaction a judge will have when hearing a specific legal interpretation, and proposes a strategy in accordance with those fluctuations. In this way, by proposing a strategy in accordance with the judge's emotional changes, a more effective strategy can be implemented.

[0058] The thought pattern modeling unit can take into account the personal background information of a judge and construct a thought pattern based thereon. The thought pattern modeling unit, for example, takes into account the personal background information of a judge (e.g., educational background and work history) and constructs a thought pattern based thereon. For example, it analyzes what kind of educational background and work history a particular judge has and constructs a thought pattern based on that background. The thought pattern modeling unit also takes into account the personal background information of a judge (e.g., educational background and work history) and constructs a thought pattern based thereon. For example, it analyzes what kind of specialty a particular judge has and constructs a thought pattern based on that background. The thought pattern modeling unit also takes into account the personal background information of a judge (e.g., educational background and work history) and constructs a thought pattern based thereon. For example, it analyzes what kind of work history a particular judge has and constructs a thought pattern based on that background. In this way, by taking into account the personal background information of a judge, it is possible to construct a more accurate thought pattern.

[0059] The thought pattern modeling unit can use the emotion estimation function to model what emotions a judge will have in a specific situation and construct a thought pattern based on those emotions. The thought pattern modeling unit, for example, uses the emotion estimation function to model what emotions a judge will have in a specific situation. For example, it analyzes what emotions a judge will have when viewing specific evidence and constructs a thought pattern based on those emotions. The thought pattern modeling unit also uses the emotion estimation function to model what emotions a judge will have in a specific situation. For example, it analyzes what emotions a judge will have when hearing the testimony of a specific witness and constructs a thought pattern based on those emotions. The thought pattern modeling unit also uses the emotion estimation function to model what emotions a judge will have in a specific situation. For example, it analyzes what emotions a judge will have when hearing a specific legal interpretation and constructs a thought pattern based on those emotions. In this way, by constructing a thought pattern based on the judge's emotions, it is possible to propose a more appropriate strategy.

[0060] The thought pattern modeling unit can also model the thought patterns of judges in different legal systems and perform comparative analysis. The thought pattern modeling unit, for example, models the thought patterns of judges in different legal systems (e.g., common law and civil law) and performs comparative analysis. For example, it analyzes how common law judges and civil law judges have different thought patterns. The thought pattern modeling unit also models the thought patterns of judges in different legal systems (e.g., common law and civil law) and performs comparative analysis. For example, it analyzes what legal grounds common law judges emphasize and compares them with civil law judges. The thought pattern modeling unit also models the thought patterns of judges in different legal systems (e.g., common law and civil law) and performs comparative analysis. For example, it analyzes what evidence civil law judges emphasize and compares them with common law judges. In this way, by comparing and analyzing the thought patterns of judges in different legal systems, it is possible to propose strategies from a more multifaceted perspective.

[0061] The thought pattern modeling unit can construct thought patterns for each field using case law data from different fields. The thought pattern modeling unit, for example, constructs a judge's thought pattern using case law data from different fields (e.g., criminal cases and civil cases). For example, it compares and analyzes the judge's thought pattern in criminal cases and the thought pattern in civil cases. The thought pattern modeling unit also constructs a judge's thought pattern using case law data from different fields (e.g., criminal cases and civil cases). For example, it analyzes the degree of importance of evidence in criminal cases and the degree of importance of evidence in civil cases. The thought pattern modeling unit also constructs a judge's thought pattern using case law data from different fields (e.g., criminal cases and civil cases). For example, it analyzes the frequency of citation of legal grounds in criminal cases and the frequency of citation in civil cases. In this way, by using case law data from different fields, it is possible to construct thought patterns for each field.

[0062] The thought pattern modeling unit uses the emotion estimation function to model the emotions that judges have toward cases in different fields and to construct thought patterns based on those emotions. The thought pattern modeling unit, for example, uses the emotion estimation function to model the emotions that judges have toward cases in different fields. For example, it analyzes emotions toward criminal cases and emotions toward civil cases and constructs thought patterns based on those emotions. The thought pattern modeling unit also uses the emotion estimation function to model the emotions that judges have toward cases in different fields. For example, it analyzes emotions toward economic cases and emotions toward family cases and constructs thought patterns based on those emotions. The thought pattern modeling unit also uses the emotion estimation function to model the emotions that judges have toward cases in different fields. For example, it analyzes emotions toward environmental cases and emotions toward labor cases and constructs thought patterns based on those emotions. In this way, by constructing thought patterns based on the judge's emotions, more appropriate strategies can be proposed.

[0063] The strategy proposal unit performs simulations of the strategy proposed by the generation AI based on past court results, and can evaluate the effectiveness of the proposal in advance. For example, the strategy proposal unit performs simulations of the strategy proposed by the generation AI based on past court results. For example, it evaluates the effectiveness of the proposed strategy based on the results of similar past cases. The strategy proposal unit also performs simulations of the strategy proposed by the generation AI based on past court results. For example, it simulates the influence of specific evidence or witnesses and evaluates the effectiveness of the proposed strategy. The strategy proposal unit also performs simulations of the strategy proposed by the generation AI based on past court results. For example, it simulates the influence of specific legal interpretations and evaluates the effectiveness of the proposed strategy. This allows the provision of more reliable strategies by evaluating the effectiveness of the proposal in advance.

[0064] The strategy proposal unit can consider different scenarios for the strategy proposed by the generation AI and compare and evaluate multiple strategies. For example, the strategy proposal unit considers different scenarios for the strategy proposed by the generation AI and compares and evaluates multiple strategies. For example, it simulates the addition of evidence or the change of witnesses and selects the optimal strategy. The strategy proposal unit also considers different scenarios for the strategy proposed by the generation AI and compares and evaluates multiple strategies. For example, it simulates different legal interpretations and selects the optimal strategy. The strategy proposal unit also considers different scenarios for the strategy proposed by the generation AI and compares and evaluates multiple strategies. For example, it simulates the testimony of different witnesses and selects the optimal strategy. In this way, the optimal strategy can be selected by considering different scenarios.

[0065] The strategy proposal unit can use the emotion estimation function to predict how the judge will feel about the proposed strategy and adjust the strategy based on that emotion. The strategy proposal unit, for example, uses the emotion estimation function to predict how the judge will feel about the proposed strategy. For example, it predicts an emotional reaction to specific evidence or a witness and adjusts the strategy based on that emotion. The strategy proposal unit also uses the emotion estimation function to predict how the judge will feel about the proposed strategy. For example, it predicts an emotional reaction to a specific legal interpretation and adjusts the strategy based on that emotion. The strategy proposal unit also uses the emotion estimation function to predict how the judge will feel about the proposed strategy. For example, it predicts an emotional reaction to the testimony of a specific witness and adjusts the strategy based on that emotion. In this way, by adjusting the strategy based on the judge's emotion, a more effective strategy can be executed.

[0066] The strategy proposal unit can apply the strategy proposed by the generative AI to different legal systems and construct a strategy from an international perspective. For example, the strategy proposal unit can apply the strategy proposed by the generative AI to different legal systems (e.g., international court cases) and construct a strategy from an international perspective. For example, it can analyze how evidence is presented in international court cases and propose the optimal strategy. The strategy proposal unit can also apply the strategy proposed by the generative AI to different legal systems (e.g., international court cases) and construct a strategy from an international perspective. For example, it can analyze the legal interpretations of different countries and propose the optimal strategy. The strategy proposal unit can also apply the strategy proposed by the generative AI to different legal systems (e.g., international court cases) and construct a strategy from an international perspective. For example, it can analyze the thinking patterns of judges from different countries and propose the optimal strategy. In this way, by constructing a strategy from an international perspective, it is possible to propose a more diversified strategy.

[0067] The strategy proposal unit uses the emotion estimation function to monitor in real time how the judge feels about different strategies and continuously adjust the optimal strategy. The strategy proposal unit, for example, uses the emotion estimation function to monitor in real time how the judge feels about different strategies. For example, it monitors emotional reactions to specific evidence or witnesses and adjusts the strategy based on the results. The strategy proposal unit also uses the emotion estimation function to monitor in real time how the judge feels about different strategies. For example, it monitors emotional reactions to specific legal interpretations and adjusts the strategy based on the results. The strategy proposal unit also uses the emotion estimation function to monitor in real time how the judge feels about different strategies. For example, it monitors emotional reactions to the testimony of a specific witness and adjusts the strategy based on the results. In this way, by monitoring the judge's emotions in real time and continuously adjusting the strategy, a more effective strategy can be executed.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] In addition to the judges' case data, the precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI can analyze. For example, it analyzes what kind of mediation and arbitration results the judges reached and identifies trends. The precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI analyzes. For example, it analyzes what mediation and arbitration methods the judges used and identifies trends. The precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI analyzes. For example, it analyzes what kind of mediation and arbitration results the judges place importance on and identifies trends. This makes it possible to understand the judges' mediation and arbitration trends and propose more appropriate strategies.

[0070] The precedent analysis unit analyzes not only the judge's case data but also data on past mediations and arbitrations in which the judge was involved, making it possible to grasp the judge's mediation and arbitration tendencies. For example, it analyzes what kind of mediation and arbitration results the judge achieved and grasps those trends. The precedent analysis unit also analyzes data on past mediations and arbitrations in which the judge was involved to grasp the judge's mediation and arbitration tendencies. For example, it analyzes what kind of mediation and arbitration methods the judge used and grasps those trends. The precedent analysis unit also analyzes data on past mediations and arbitrations in which the judge was involved to grasp the judge's mediation and arbitration tendencies. For example, it analyzes what kind of mediation and arbitration results the judge places importance on and grasps those trends. In this way, by understanding the judge's mediation and arbitration tendencies, it is possible to propose more appropriate strategies.

[0071] The precedent analysis unit can use the emotion estimation function to analyze what emotions a judge has during the mediation or arbitration process and propose a strategy based on those emotions. For example, it can analyze what emotions a judge has in a specific mediation or arbitration situation and propose a strategy based on those emotions. The precedent analysis unit also uses the emotion estimation function to analyze what emotions a judge has during the mediation or arbitration process and propose a strategy based on those emotions. For example, it can analyze what emotions a judge has toward a specific mediation or arbitration method and propose a strategy based on those emotions. The precedent analysis unit also uses the emotion estimation function to analyze what emotions a judge has during the mediation or arbitration process and propose a strategy based on those emotions. For example, it can analyze what emotions a judge has toward a specific mediation or arbitration result and propose a strategy based on those emotions. In this way, by proposing a strategy based on the judge's emotions, a more effective strategy can be implemented.

[0072] The case law analysis unit uses the emotion estimation function to analyze in real time what emotions a judge has during the mediation or arbitration process and propose a strategy based on those emotions. For example, it analyzes in real time what emotions a judge has in a specific mediation or arbitration situation and proposes a strategy based on those emotions. The case law analysis unit also uses the emotion estimation function to analyze in real time what emotions a judge has during the mediation or arbitration process and proposes a strategy based on those emotions. For example, it analyzes in real time what emotions a judge has toward a specific mediation or arbitration method and proposes a strategy based on those emotions. The case law analysis unit also uses the emotion estimation function to analyze in real time what emotions a judge has during the mediation or arbitration process and proposes a strategy based on those emotions. For example, it analyzes in real time what emotions a judge has toward a specific mediation or arbitration result and proposes a strategy based on those emotions. In this way, by proposing a strategy based on the judge's emotions, a more effective strategy can be implemented.

[0073] In addition to the judges' case data, the precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI can analyze. For example, it analyzes what kind of mediation and arbitration results the judges reached and identifies trends. The precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI analyzes. For example, it analyzes what mediation and arbitration methods the judges used and identifies trends. The precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI analyzes. For example, it analyzes what kind of mediation and arbitration results the judges place importance on and identifies trends. This makes it possible to understand the judges' mediation and arbitration trends and propose more appropriate strategies.

[0074] The precedent analysis unit can use the emotion estimation function to analyze what emotions a judge has during the mediation or arbitration process and propose a strategy based on those emotions. For example, it can analyze what emotions a judge has in a specific mediation or arbitration situation and propose a strategy based on those emotions. The precedent analysis unit also uses the emotion estimation function to analyze what emotions a judge has during the mediation or arbitration process and propose a strategy based on those emotions. For example, it can analyze what emotions a judge has toward a specific mediation or arbitration method and propose a strategy based on those emotions. The precedent analysis unit also uses the emotion estimation function to analyze what emotions a judge has during the mediation or arbitration process and propose a strategy based on those emotions. For example, it can analyze what emotions a judge has toward a specific mediation or arbitration result and propose a strategy based on those emotions. In this way, by proposing a strategy based on the judge's emotions, a more effective strategy can be implemented.

[0075] The case law analysis unit uses the emotion estimation function to analyze in real time what emotions a judge has during the mediation or arbitration process and propose a strategy based on those emotions. For example, it analyzes in real time what emotions a judge has in a specific mediation or arbitration situation and proposes a strategy based on those emotions. The case law analysis unit also uses the emotion estimation function to analyze in real time what emotions a judge has during the mediation or arbitration process and proposes a strategy based on those emotions. For example, it analyzes in real time what emotions a judge has toward a specific mediation or arbitration method and proposes a strategy based on those emotions. The case law analysis unit also uses the emotion estimation function to analyze in real time what emotions a judge has during the mediation or arbitration process and proposes a strategy based on those emotions. For example, it analyzes in real time what emotions a judge has toward a specific mediation or arbitration result and proposes a strategy based on those emotions. In this way, by proposing a strategy based on the judge's emotions, a more effective strategy can be implemented.

[0076] In addition to the judges' case data, the precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI can analyze. For example, it analyzes what kind of mediation and arbitration results the judges reached and identifies trends. The precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI analyzes. For example, it analyzes what mediation and arbitration methods the judges used and identifies trends. The precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI analyzes. For example, it analyzes what kind of mediation and arbitration results the judges place importance on and identifies trends. This makes it possible to understand the judges' mediation and arbitration trends and propose more appropriate strategies.

[0077] The precedent analysis unit can use the emotion estimation function to analyze what emotions a judge has during the mediation or arbitration process and propose a strategy based on those emotions. For example, it can analyze what emotions a judge has in a specific mediation or arbitration situation and propose a strategy based on those emotions. The precedent analysis unit also uses the emotion estimation function to analyze what emotions a judge has during the mediation or arbitration process and propose a strategy based on those emotions. For example, it can analyze what emotions a judge has toward a specific mediation or arbitration method and propose a strategy based on those emotions. The precedent analysis unit also uses the emotion estimation function to analyze what emotions a judge has during the mediation or arbitration process and propose a strategy based on those emotions. For example, it can analyze what emotions a judge has toward a specific mediation or arbitration result and propose a strategy based on those emotions. In this way, by proposing a strategy based on the judge's emotions, a more effective strategy can be implemented.

[0078] The case law analysis unit uses the emotion estimation function to analyze in real time what emotions a judge has during the mediation or arbitration process and propose a strategy based on those emotions. For example, it analyzes in real time what emotions a judge has in a specific mediation or arbitration situation and proposes a strategy based on those emotions. The case law analysis unit also uses the emotion estimation function to analyze in real time what emotions a judge has during the mediation or arbitration process and proposes a strategy based on those emotions. For example, it analyzes in real time what emotions a judge has toward a specific mediation or arbitration method and proposes a strategy based on those emotions. The case law analysis unit also uses the emotion estimation function to analyze in real time what emotions a judge has during the mediation or arbitration process and proposes a strategy based on those emotions. For example, it analyzes in real time what emotions a judge has toward a specific mediation or arbitration result and proposes a strategy based on those emotions. In this way, by proposing a strategy based on the judge's emotions, a more effective strategy can be implemented.

[0079] In addition to the judges' case data, the precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI can analyze. For example, it analyzes what kind of mediation and arbitration results the judges reached and identifies trends. The precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI analyzes. For example, it analyzes what mediation and arbitration methods the judges used and identifies trends. The precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI analyzes. For example, it analyzes what kind of mediation and arbitration results the judges place importance on and identifies trends. This makes it possible to understand the judges' mediation and arbitration trends and propose more appropriate strategies.

[0080] The precedent analysis unit can use the emotion estimation function to analyze what emotions a judge has during the mediation or arbitration process and propose a strategy based on those emotions. For example, it can analyze what emotions a judge has in a specific mediation or arbitration situation and propose a strategy based on those emotions. The precedent analysis unit also uses the emotion estimation function to analyze what emotions a judge has during the mediation or arbitration process and propose a strategy based on those emotions. For example, it can analyze what emotions a judge has toward a specific mediation or arbitration method and propose a strategy based on those emotions. The precedent analysis unit also uses the emotion estimation function to analyze what emotions a judge has during the mediation or arbitration process and propose a strategy based on those emotions. For example, it can analyze what emotions a judge has toward a specific mediation or arbitration result and propose a strategy based on those emotions. In this way, by proposing a strategy based on the judge's emotions, a more effective strategy can be implemented.

[0081] The case law analysis unit uses the emotion estimation function to analyze in real time what emotions a judge has during the mediation or arbitration process and propose a strategy based on those emotions. For example, it analyzes in real time what emotions a judge has in a specific mediation or arbitration situation and proposes a strategy based on those emotions. The case law analysis unit also uses the emotion estimation function to analyze in real time what emotions a judge has during the mediation or arbitration process and proposes a strategy based on those emotions. For example, it analyzes in real time what emotions a judge has toward a specific mediation or arbitration method and proposes a strategy based on those emotions. The case law analysis unit also uses the emotion estimation function to analyze in real time what emotions a judge has during the mediation or arbitration process and proposes a strategy based on those emotions. For example, it analyzes in real time what emotions a judge has toward a specific mediation or arbitration result and proposes a strategy based on those emotions. In this way, by proposing a strategy based on the judge's emotions, a more effective strategy can be implemented.

[0082] In addition to the judges' case data, the precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI can analyze. For example, it analyzes what kind of mediation and arbitration results the judges reached and identifies trends. The precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI analyzes. For example, it analyzes what mediation and arbitration methods the judges used and identifies trends. The precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI analyzes. For example, it analyzes what kind of mediation and arbitration results the judges place importance on and identifies trends. This makes it possible to understand the judges' mediation and arbitration trends and propose more appropriate strategies.

[0083] The precedent analysis unit can use the emotion estimation function to analyze what emotions a judge has during the mediation or arbitration process and propose a strategy based on those emotions. For example, it can analyze what emotions a judge has in a specific mediation or arbitration situation and propose a strategy based on those emotions. The precedent analysis unit also uses the emotion estimation function to analyze what emotions a judge has during the mediation or arbitration process and propose a strategy based on those emotions. For example, it can analyze what emotions a judge has toward a specific mediation or arbitration method and propose a strategy based on those emotions. The precedent analysis unit also uses the emotion estimation function to analyze what emotions a judge has during the mediation or arbitration process and propose a strategy based on those emotions. For example, it can analyze what emotions a judge has toward a specific mediation or arbitration result and propose a strategy based on those emotions. In this way, by proposing a strategy based on the judge's emotions, a more effective strategy can be implemented.

[0084] The case law analysis unit uses the emotion estimation function to analyze in real time what emotions a judge has during the mediation or arbitration process and propose a strategy based on those emotions. For example, it analyzes in real time what emotions a judge has in a specific mediation or arbitration situation and proposes a strategy based on those emotions. The case law analysis unit also uses the emotion estimation function to analyze in real time what emotions a judge has during the mediation or arbitration process and proposes a strategy based on those emotions. For example, it analyzes in real time what emotions a judge has toward a specific mediation or arbitration method and proposes a strategy based on those emotions. The case law analysis unit also uses the emotion estimation function to analyze in real time what emotions a judge has during the mediation or arbitration process and proposes a strategy based on those emotions. For example, it analyzes in real time what emotions a judge has toward a specific mediation or arbitration result and proposes a strategy based on those emotions. In this way, by proposing a strategy based on the judge's emotions, a more effective strategy can be implemented.

[0085] In addition to the judges' case data, the precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI can analyze. For example, it analyzes what kind of mediation and arbitration results the judges reached and identifies trends. The precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI analyzes. For example, it analyzes what mediation and arbitration methods the judges used and identifies trends. The precedent collection unit also collects data on past mediations and arbitrations in which the judges were involved, which the generation AI analyzes. For example, it analyzes what kind of mediation and arbitration results the judges place importance on and identifies trends. This makes it possible to understand the judges' mediation and arbitration trends and propose more appropriate strategies.

[0086] The precedent analysis unit can use the emotion estimation function to analyze what emotions a judge has during the mediation or arbitration process and propose a strategy based on those emotions. For example, it can analyze what emotions a judge has in a specific mediation or arbitration situation and propose a strategy based on those emotions. The precedent analysis unit also uses the emotion estimation function to analyze what emotions a judge has during the mediation or arbitration process and propose a strategy based on those emotions. For example, it can analyze what emotions a judge has toward a specific mediation or arbitration method and propose a strategy based on those emotions. The precedent analysis unit also uses the emotion estimation function to analyze what emotions a judge has during the mediation or arbitration process and propose a strategy based on those emotions. For example, it can analyze what emotions a judge has toward a specific mediation or arbitration result and propose a strategy based on those emotions. In this way, by proposing a strategy based on the judge's emotions, a more effective strategy can be implemented.

[0087] The case law analysis unit uses the emotion estimation function to analyze in real time what emotions a judge has during the mediation or arbitration process and propose a strategy based on those emotions. For example, it analyzes in real time what emotions a judge has in a specific mediation or arbitration situation and proposes a strategy based on those emotions. The case law analysis unit also uses the emotion estimation function to analyze in real time what emotions a judge has during the mediation or arbitration process and proposes a strategy based on those emotions. For example, it analyzes in real time what emotions a judge has toward a specific mediation or arbitration method and proposes a strategy based on those emotions. The case law analysis unit also uses the emotion estimation function to analyze in real time what emotions a judge has during the mediation or arbitration process and proposes a strategy based on those emotions. For example, it analyzes in real time what emotions a judge has toward a specific mediation or arbitration result and proposes a strategy based on those emotions. In this way, by proposing a strategy based on the judge's emotions, a more effective strategy can be implemented.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The case collection unit collects past case data of judges. For example, the case collection unit retrieves the judgments of each judge from the case database. The case collection unit may also collect court records and related legal documents. For example, the case collection unit downloads judgments from the court's official website. Step 2: The case analysis unit analyzes the case data collected by the case collection unit. For example, the case analysis unit analyzes the content of the judgment sentence using text mining technology. The case analysis unit can also analyze the structure of the judgment sentence using natural language processing technology. For example, the case analysis unit extracts keywords from the judgment sentence and analyzes their frequency. Step 3: The thought pattern modeling unit models the judge's thought patterns based on the data analyzed by the case analysis unit. For example, the thought pattern modeling unit uses a machine learning algorithm to model the judge's decision-making criteria. The thought pattern modeling unit can also model the judge's priorities using statistical methods. For example, the thought pattern modeling unit models what laws and facts the judge places importance on. Step 4: The Strategy Proposal Unit proposes the optimal way to fight based on the thought patterns modeled by the Thought Pattern Modeling Unit. For example, the Strategy Proposal Unit may propose a method for presenting evidence to a specific judge. The Strategy Proposal Unit may also propose a strategy that focuses on the selection of witnesses. For example, if the judge places importance on the credibility of evidence, the Strategy Proposal Unit may propose a strategy for presenting highly reliable evidence.

[0090] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0091] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0092] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0096] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0097] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0098] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0099] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0100] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0101] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0102] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0103] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0104] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 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.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0131] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0140] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0141] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0142] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0143] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0144] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0145] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0146] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0147] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0148] 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.

[0149] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0150] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0151] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0152] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0153] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0154] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0155] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0156] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0157] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A case law collection department that collects data on past cases of judges; a case analysis unit that analyzes the case data collected by the case collection unit; a thought pattern modeling unit that models the thought pattern of a judge based on the data analyzed by the case precedent analysis unit; a strategy proposal unit that proposes an optimal fighting strategy based on the thought pattern modeled by the thought pattern modeling unit. A system characterized by:

2. The case law collection department In addition to the judge's case law data, public statements and papers will also be collected, and the generative AI will analyze them to obtain an overall picture of the judge's legal thinking.

2. The system of claim 1.

3. The case analysis unit Extracting characteristics of the style and wording of the judgment and modeling the judge's personal tendencies 2. The system of claim 1.

4. The case analysis unit Analyzing emotional expressions in the judgment to understand the emotional tendencies of the judge 2. The system of claim 1.

5. The case law collection department In addition to the case data of the judge in question, case data of other judges in the same court will also be collected, and the AI ​​will compare and analyze them to understand trends across the court.

2. The system of claim 1.

6. The case law collection department When analyzing the case data of the judges, case data of judges from different countries and regions is also collected, and the generating AI performs a comparative analysis from an international perspective.

2. The system of claim 1.

7. The case analysis unit Analyze the emotional fluctuations of the judge when making a decision in real time and propose strategies according to the changes in the judge's emotions.

2. The system of claim 1.

Images