System

Abstract

An object of a system according to an embodiment is to analyze past precedents based on information related to litigation and to provide a winning percentage.SOLUTION: A system includes a necessary item input part, a case analysis part, and a winning rate provision part. The necessary matter input part inputs necessary matters related to litigation from a user. A case analysis part analyzes a past case on the basis of the information input by the necessary item input part. A winning rate providing part provides a winning rate on the basis of the result analyzed by the case analyzing part.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 technologies do not adequately analyze past precedents based on litigation information and provide winning rates, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze past precedents based on information about lawsuits and provide winning rates. [Means for solving the problem]

[0006] The system according to the embodiment includes a required information input unit, a case precedent analysis unit, and a winning rate providing unit. The required information input unit inputs required information about a lawsuit from a user. The case precedent analysis unit analyzes past cases based on the information input by the required information input unit. The winning rate providing unit provides a winning rate based on the results of the analysis by the case precedent analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze past precedents based on information about lawsuits and provide winning rates. [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 court win rate providing system according to an embodiment of the present invention is a system in which a generating AI analyzes past precedents and provides win rates based on necessary information about a lawsuit entered by a user. This allows ordinary people who are not legal experts to easily determine whether they will win or lose a lawsuit.

[0029] A court win rate providing system according to an embodiment includes a required information input unit, a precedent analysis unit, and a win rate providing unit. The required information input unit inputs necessary information about a lawsuit from a user. For example, the user can input the content of the lawsuit, relevant laws, past cases, etc. The required information input unit can also convert the input information into a format that is easy for the generation AI to analyze. The precedent analysis unit analyzes past precedents based on the information input by the required information input unit. For example, the generation AI searches a database of past precedents to extract similar cases. The generation AI uses a pre-finished model to find past precedents in which similar lawsuit content or relevant laws were applied. The win rate providing unit provides a win rate based on the results of the analysis by the precedent analysis unit. For example, the generation AI quantifies the user's chances of success by referring to the win and loss rates of similar past cases. This allows the court win rate providing system to easily determine whether a lawsuit will be won or lost by non-legal professionals.

[0030] The required information input unit allows the generation AI to automatically suggest additional information related to the information entered by the user, improving the accuracy of input. For example, when a user enters the details of a lawsuit, the generation AI automatically suggests related laws and past precedents. For example, if a user enters "breach of contract," the generation AI presents relevant contract laws and past precedents related to breach of contract. Furthermore, when a user enters a relevant law, the generation AI automatically suggests important provisions and interpretations related to that law. For example, if a user enters "Article 709 of the Civil Code," the generation AI presents an interpretation of that provision and related precedents. Furthermore, when a user enters a past case, the generation AI automatically suggests additional information related to that case. For example, if a user enters "lawsuit between Company A and Company B," the generation AI presents details of the lawsuit and related precedents. This improves the accuracy of user input.

[0031] The necessary information input unit allows the generation AI to automatically search for relevant legal documents and materials based on information entered by the user and provide them as reference materials. For example, when a user enters the details of a lawsuit, the generation AI automatically searches for relevant legal documents and materials and provides them as reference materials. For example, when a user enters "termination of labor contract," the generation AI searches for and provides relevant labor laws and past precedents. In addition, when a user enters a relevant law, the generation AI automatically searches for academic papers and expert opinions related to that law and provides them as reference materials. For example, when a user enters "consumer protection law," the generation AI searches for and provides relevant academic papers and expert opinions. In addition, when a user enters a past case, the generation AI automatically searches for legal documents and materials related to that case and provides them as reference materials. For example, when a user enters "patent infringement lawsuit," the generation AI searches for and provides relevant patent laws and past precedents. This allows users to easily obtain reference materials.

[0032] The required information input unit can add a voice input function, allowing the user to input the required information by voice. The required information input unit adds a function that allows the user to input the required information by voice, for example, using voice recognition technology. For example, when a user speaks, "Regarding the breach of contract," the generation AI converts the content into text and inputs it. The required information input unit also uses the voice input function to build a system that allows the user to input the required information by voice. For example, when a user speaks, "Regarding the termination of the labor contract," the generation AI converts the content into text and inputs it. The required information input unit also adds a voice input function to develop a system that allows the user to input the required information by voice. For example, when a user speaks, "Regarding the patent infringement lawsuit," the generation AI converts the content into text and inputs it. This allows the user to input the required information by voice.

[0033] The required information input unit can develop a mobile application that allows users to easily input required information from a smartphone. The required information input unit, for example, develops a mobile application that allows users to easily input required information from a smartphone. For example, when a user inputs the details of a lawsuit, the generation AI analyzes the details and provides related information. The required information input unit also develops a mobile application that allows users to input required information from a smartphone. For example, when a user inputs the relevant law, the generation AI provides information related to that law. The required information input unit also uses a mobile application to build a system that allows users to easily input required information from a smartphone. For example, when a user inputs a past case, the generation AI provides information related to that case. This allows users to easily input required information from a smartphone.

[0034] The precedent analysis unit can also take into account the background of the precedent and the judge's criteria when analyzing past precedents. For example, when the generation AI analyzes past precedents, the precedent analysis unit takes into account the background information of the precedent. For example, it analyzes the time when the precedent was issued and the social background to understand the intent of the precedent. The precedent analysis unit also takes into account the judge's criteria when the generation AI analyzes past precedents. For example, it analyzes the judge's past judicial trends and criteria to interpret the precedent. The precedent analysis unit also builds a system in which the generation AI analyzes past precedents, taking into account the background of the precedent and the judge's criteria. For example, it registers the background information of the precedent and the judge's criteria in a database and uses it in the analysis. This makes it possible to perform analysis that takes into account the background of the precedent and the judge's criteria.

[0035] The precedent analysis unit can also take into account the impact of related legal amendments and new laws when analyzing precedents. For example, the precedent analysis unit takes into account the impact of related legal amendments and new laws when the generation AI analyzes past precedents. For example, if a legal amendment is made after a precedent is issued, the impact is reflected in the analysis. The precedent analysis unit also builds a system in which the generation AI analyzes past precedents, taking into account the impact of new laws. For example, if a new law is enacted, the impact is reflected in the analysis. The precedent analysis unit is also equipped with a function that takes into account the impact of related legal amendments and new laws when the generation AI analyzes precedents. For example, it analyzes the impact of legal amendments and new laws on precedents and interprets the precedents. This makes it possible to perform analysis that takes into account the impact of legal amendments and new laws.

[0036] The precedent analysis unit can also search overseas precedent databases and perform analysis from an international perspective. For example, the generation AI will search overseas precedent databases and build a system that performs analysis from an international perspective. For example, it will search American and European precedent databases and extract related precedents. The precedent analysis unit will also be equipped with a function that includes overseas precedent databases in its search targets and performs analysis from an international perspective. For example, it will analyze international precedents and compare them with domestic precedents. The precedent analysis unit will also include overseas precedent databases in its search targets and perform analysis from an international perspective. For example, it will analyze international precedents and compare them with domestic precedents. This makes analysis from an international perspective possible.

[0037] The precedent analysis unit can also refer to related academic papers and expert opinions when analyzing precedents. For example, the precedent analysis unit builds a system that allows the generation AI to refer to related academic papers and expert opinions when analyzing precedents. For example, it searches for academic papers and expert opinions related to precedents and reflects them in the analysis. The precedent analysis unit is also equipped with a function that allows the generation AI to analyze precedents by referring to academic papers and expert opinions. For example, it searches for academic papers and expert opinions related to precedents and reflects them in the analysis. The precedent analysis unit also refers to related academic papers and expert opinions when the generation AI analyzes precedents. For example, it searches for academic papers and expert opinions related to precedents and reflects them in the analysis. This makes it possible to perform analysis by referring to academic papers and expert opinions.

[0038] The win rate providing unit can provide detailed analysis results and rationale for the win rate it provides, making it easier for users to understand. For example, the win rate providing unit builds a system that provides detailed analysis results and rationale for the win rate provided by the generation AI. For example, it provides statistical data on past precedents and interpretations of related laws. In addition, the win rate providing unit is equipped with a function that displays detailed analysis results and rationale when providing the win rate. For example, it displays graphs of the win and loss rates of similar past cases. In addition, the win rate providing unit provides detailed analysis results and rationale for the win rate provided by the generation AI. For example, it explains background information on the precedent and the judge's decision-making criteria. This makes it easier for users to understand the rationale for the win rate.

[0039] The win rate providing unit can also provide simulation results that take into account different scenarios for the win rate it provides. The win rate providing unit builds a system that provides simulation results that take into account different scenarios for the win rate provided by the generation AI. For example, it simulates how the win rate will change when different evidence or witnesses are added. The win rate providing unit is also equipped with a function that provides simulation results that take into account different scenarios. For example, it simulates how the win rate will change when the strength of evidence or the reliability of witnesses is changed. The win rate providing unit also provides simulation results that take into account different scenarios for the win rate provided by the generation AI. For example, it simulates how the win rate will change when different evidence or witnesses are added. This makes it possible to provide simulation results that take into account different scenarios.

[0040] In addition to providing the win rate, the win rate providing unit can also provide the expected amount of compensation and cost-effectiveness if successful. For example, the win rate providing unit builds a system that provides the expected amount of compensation and cost-effectiveness if successful in addition to the win rate provided by the generation AI. For example, it predicts the amount of compensation based on past case law data and calculates the cost-effectiveness. In addition, when providing the win rate, the win rate providing unit is equipped with a function that displays the expected amount of compensation and cost-effectiveness if successful. For example, it displays a graph of the predicted amount of compensation and cost-effectiveness. In addition to the win rate provided by the generation AI, the win rate providing unit provides the expected amount of compensation and cost-effectiveness if successful. For example, it predicts the amount of compensation based on past case law data and calculates the cost-effectiveness. This makes it possible to provide the amount of compensation and cost-effectiveness.

[0041] The win rate providing unit can visualize the win rate it provides and display it in a graph or chart, allowing the user to intuitively understand it. The win rate providing unit, for example, builds a system that visualizes the win rate provided by the generation AI and displays it in a graph or chart. For example, it displays the fluctuations in win rate in a line graph. Furthermore, the win rate providing unit is equipped with a function that visualizes the win rate and displays it in a graph or chart when providing it. For example, it displays the distribution of win rates in a pie chart. Furthermore, the win rate providing unit visualizes the win rate provided by the generation AI and displays it in a graph or chart. For example, it displays the fluctuations in win rate in a line graph. This allows the user to intuitively understand the win rate.

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

[0043] In the required information input section, the AI ​​automatically suggests additional information related to the information entered by the user, improving the accuracy of the input. For example, when a user enters the details of a lawsuit, the AI ​​automatically suggests relevant laws and past precedents. When a user enters "breach of contract," the AI ​​presents relevant contract laws and past precedents regarding breach of contract. Furthermore, when a user enters a relevant law, the AI ​​automatically suggests important provisions and interpretations related to that law. For example, when a user enters "Article 709 of the Civil Code," the AI ​​presents an interpretation of that provision and related precedents. Furthermore, when a user enters a past case, the AI ​​automatically suggests additional information related to that case. For example, when a user enters "lawsuit between Company A and Company B," the AI ​​presents details of the lawsuit and related precedents. This improves the accuracy of the user's input.

[0044] The necessary information input section allows the generation AI to automatically search for relevant legal documents and materials based on the information entered by the user and provide them as reference materials. For example, when a user enters the details of a lawsuit, the generation AI automatically searches for relevant legal documents and materials and provides them as reference materials. When a user enters "termination of labor contract," the generation AI searches for and provides relevant labor laws and past precedents. Furthermore, when a user enters a relevant law, the generation AI automatically searches for academic papers and expert opinions related to that law and provides them as reference materials. For example, when a user enters "consumer protection law," the generation AI searches for and provides relevant academic papers and expert opinions. Furthermore, when a user enters a past case, the generation AI automatically searches for legal documents and materials related to that case and provides them as reference materials. For example, when a user enters "patent infringement lawsuit," the generation AI searches for and provides relevant patent laws and past precedents. This allows users to easily obtain reference materials.

[0045] The required information input unit can add a voice input function, allowing users to input required information by voice. For example, using voice recognition technology, a function can be added that allows users to input required information by voice. When a user speaks, "Regarding breach of contract," the generation AI converts the content into text and enters it. In addition, using the voice input function, a system can be built that allows users to input required information by voice. When a user speaks, "Regarding termination of employment contract," the generation AI converts the content into text and enters it. Furthermore, a voice input function can be added to develop a system that allows users to input required information by voice. When a user speaks, "Regarding patent infringement lawsuit," the generation AI converts the content into text and enters it. This allows users to input required information by voice.

[0046] The required information input unit can develop a mobile application to allow users to easily enter required information from their smartphones. For example, a mobile application can be developed to allow users to easily enter required information from their smartphones. When a user enters the details of the lawsuit, the generation AI analyzes the details and provides related information. A mobile application can also be developed that allows users to enter required information from their smartphones. When a user enters the relevant law, the generation AI provides information related to that law. Furthermore, a system can be built using the mobile application that allows users to easily enter required information from their smartphones. When a user enters a past case, the generation AI provides information related to that case. This allows users to easily enter required information from their smartphones.

[0047] The precedent analysis unit can also take into account the background of the precedent and the judge's criteria when analyzing past precedents. For example, when the generation AI analyzes past precedents, it takes into account the background information of the precedent. It analyzes the time when the precedent was issued and the social background to understand the intent of the precedent. The generation AI also analyzes past precedents taking into account the judge's criteria. It analyzes the judge's past judgment trends and criteria to interpret the precedent. Furthermore, a system is built in which the generation AI analyzes past precedents taking into account the background of the precedent and the judge's criteria. The background information of the precedent and the judge's criteria are registered in a database and used in the analysis. This makes it possible to take into account the background of the precedent and the judge's criteria for analysis.

[0048] The case analysis unit can also take into account the impact of related legal amendments and new laws when analyzing precedents. For example, when the generation AI analyzes past precedents, it takes into account the impact of related legal amendments and new laws. If legal amendments are made after a precedent is issued, the impact will be reflected in the analysis. In addition, a system will be built in which the generation AI analyzes past precedents, taking into account the impact of new laws. If a new law is enacted, its impact will be reflected in the analysis. Furthermore, when the generation AI analyzes precedents, it will be equipped with a function that takes into account the impact of related legal amendments and new laws. It will analyze the impact of legal amendments and new laws on precedents and interpret the precedents. This makes it possible to perform analysis that takes into account the impact of legal amendments and new laws.

[0049] The case law analysis unit can also search overseas case law databases and perform analysis from an international perspective. For example, a system can be built in which the generation AI also searches overseas case law databases and performs analysis from an international perspective. It searches American and European case law databases and extracts related cases. It will also be equipped with a function that includes overseas case law databases in its search targets and performs analysis from an international perspective. It analyzes international case law and compares it with domestic case law. Furthermore, the generation AI will include overseas case law databases in its search targets and perform analysis from an international perspective. It will analyze international case law and compare it with domestic case law. This makes it possible to perform analysis from an international perspective.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The necessary information input section inputs the necessary information about the lawsuit from the user. For example, the user can input the details of the lawsuit, relevant laws, past cases, etc. The necessary information input section can also convert the input information into a format that is easy for the generation AI to analyze. Step 2: The case analysis unit analyzes past cases based on the information entered by the required information input unit. For example, the generation AI searches a database of past cases and extracts similar cases. Using a pre-fine-tuned model, the generation AI finds past cases in which similar litigation content and related laws were applied. Step 3: The win rate provider provides a win rate based on the results of the case analysis. For example, the generation AI quantifies the likelihood of the user's lawsuit by referring to the win and loss rates of similar cases in the past. This allows the court win rate provider to easily determine whether a lawsuit will be won or lost by non-legal professionals.

[0052] (Example 2) The court win rate providing system according to an embodiment of the present invention is a system in which a generating AI analyzes past precedents and provides win rates based on necessary information about a lawsuit entered by a user. This allows ordinary people who are not legal experts to easily determine whether they will win or lose a lawsuit.

[0053] A court win rate providing system according to an embodiment includes a required information input unit, a precedent analysis unit, and a win rate providing unit. The required information input unit inputs necessary information about a lawsuit from a user. For example, the user can input the content of the lawsuit, relevant laws, past cases, etc. The required information input unit can also convert the input information into a format that is easy for the generation AI to analyze. The precedent analysis unit analyzes past precedents based on the information input by the required information input unit. For example, the generation AI searches a database of past precedents to extract similar cases. The generation AI uses a pre-finished model to find past precedents in which similar lawsuit content or relevant laws were applied. The win rate providing unit provides a win rate based on the results of the analysis by the precedent analysis unit. For example, the generation AI quantifies the user's chances of success by referring to the win and loss rates of similar past cases. This allows the court win rate providing system to easily determine whether a lawsuit will be won or lost by non-legal professionals.

[0054] The required information input unit allows the generation AI to automatically suggest additional information related to the information entered by the user, improving the accuracy of input. For example, when a user enters the details of a lawsuit, the generation AI automatically suggests related laws and past precedents. For example, if a user enters "breach of contract," the generation AI presents relevant contract laws and past precedents related to breach of contract. Furthermore, when a user enters a relevant law, the generation AI automatically suggests important provisions and interpretations related to that law. For example, if a user enters "Article 709 of the Civil Code," the generation AI presents an interpretation of that provision and related precedents. Furthermore, when a user enters a past case, the generation AI automatically suggests additional information related to that case. For example, if a user enters "lawsuit between Company A and Company B," the generation AI presents details of the lawsuit and related precedents. This improves the accuracy of user input.

[0055] The necessary information input unit allows the generation AI to automatically search for relevant legal documents and materials based on information entered by the user and provide them as reference materials. For example, when a user enters the details of a lawsuit, the generation AI automatically searches for relevant legal documents and materials and provides them as reference materials. For example, when a user enters "termination of labor contract," the generation AI searches for and provides relevant labor laws and past precedents. In addition, when a user enters a relevant law, the generation AI automatically searches for academic papers and expert opinions related to that law and provides them as reference materials. For example, when a user enters "consumer protection law," the generation AI searches for and provides relevant academic papers and expert opinions. In addition, when a user enters a past case, the generation AI automatically searches for legal documents and materials related to that case and provides them as reference materials. For example, when a user enters "patent infringement lawsuit," the generation AI searches for and provides relevant patent laws and past precedents. This allows users to easily obtain reference materials.

[0056] The required information input unit can use an emotion estimation function to analyze the user's emotions when entering information and provide an interface for reducing stress. The required information input unit is equipped with a function for, for example, analyzing the user's facial expressions and voice when entering information and estimating the user's emotions. For example, if the user is feeling stressed, the required information input unit provides advice to help the user relax or changes the interface. The required information input unit also uses the emotion estimation function to analyze the user's emotions when entering information in real time and provides an interface for reducing stress. For example, if the user is feeling anxious, the required information input unit provides an encouraging message or relaxing music. The required information input unit also builds a system that analyzes the user's emotions when entering information and provides an interface for reducing stress. For example, if the user is nervous, the color or design of the interface can be changed to provide a relaxing environment. This reduces the user's stress.

[0057] The required information input unit can add a voice input function, allowing the user to input the required information by voice. The required information input unit adds a function that allows the user to input the required information by voice, for example, using voice recognition technology. For example, when a user speaks, "Regarding the breach of contract," the generation AI converts the content into text and inputs it. The required information input unit also uses the voice input function to build a system that allows the user to input the required information by voice. For example, when a user speaks, "Regarding the termination of the labor contract," the generation AI converts the content into text and inputs it. The required information input unit also adds a voice input function to develop a system that allows the user to input the required information by voice. For example, when a user speaks, "Regarding the patent infringement lawsuit," the generation AI converts the content into text and inputs it. This allows the user to input the required information by voice.

[0058] The required information input unit can develop a mobile application that allows users to easily input required information from a smartphone. The required information input unit, for example, develops a mobile application that allows users to easily input required information from a smartphone. For example, when a user inputs the details of a lawsuit, the generation AI analyzes the details and provides related information. The required information input unit also develops a mobile application that allows users to input required information from a smartphone. For example, when a user inputs the relevant law, the generation AI provides information related to that law. The required information input unit also uses a mobile application to build a system that allows users to easily input required information from a smartphone. For example, when a user inputs a past case, the generation AI provides information related to that case. This allows users to easily input required information from a smartphone.

[0059] The required information input unit can use the emotion estimation function to analyze the emotion of the user when entering information in real time and provide positive feedback. The required information input unit, for example, uses the emotion estimation function to build a system that analyzes the emotion of the user when entering information in real time and provides positive feedback. For example, if the user is feeling anxious, an encouraging message is displayed. The required information input unit is also equipped with a function that analyzes the emotion of the user when entering information in real time and provides positive feedback. For example, if the user is feeling stressed, advice on how to relax is provided. The required information input unit also uses the emotion estimation function to develop a system that analyzes the emotion of the user when entering information in real time and provides positive feedback. For example, if the user is nervous, an environment in which they can relax is provided. This makes it possible to provide positive feedback to the user.

[0060] The precedent analysis unit can also take into account the background of the precedent and the judge's criteria when analyzing past precedents. For example, when the generation AI analyzes past precedents, the precedent analysis unit takes into account the background information of the precedent. For example, it analyzes the time when the precedent was issued and the social background to understand the intent of the precedent. The precedent analysis unit also takes into account the judge's criteria when the generation AI analyzes past precedents. For example, it analyzes the judge's past judicial trends and criteria to interpret the precedent. The precedent analysis unit also builds a system in which the generation AI analyzes past precedents, taking into account the background of the precedent and the judge's criteria. For example, it registers the background information of the precedent and the judge's criteria in a database and uses it in the analysis. This makes it possible to perform analysis that takes into account the background of the precedent and the judge's criteria.

[0061] The precedent analysis unit can also take into account the impact of related legal amendments and new laws when analyzing precedents. For example, the precedent analysis unit takes into account the impact of related legal amendments and new laws when the generation AI analyzes past precedents. For example, if a legal amendment is made after a precedent is issued, the impact is reflected in the analysis. The precedent analysis unit also builds a system in which the generation AI analyzes past precedents, taking into account the impact of new laws. For example, if a new law is enacted, the impact is reflected in the analysis. The precedent analysis unit is also equipped with a function that takes into account the impact of related legal amendments and new laws when the generation AI analyzes precedents. For example, it analyzes the impact of legal amendments and new laws on precedents and interprets the precedents. This makes it possible to perform analysis that takes into account the impact of legal amendments and new laws.

[0062] The case law analysis unit uses the emotion estimation function to analyze the emotions of the parties in past cases and can perform an analysis that takes emotional factors into consideration. The case law analysis unit, for example, uses the emotion estimation function to analyze the emotions of the parties in past cases. For example, it estimates the emotions of the parties from the expressions and wording in the case sentences and reflects them in the analysis. The case law analysis unit also analyzes the emotions of the parties in past cases and builds a system that performs an analysis that takes emotional factors into consideration. For example, it analyzes the impact of the emotions of the parties on the judgment and interprets the case law. The case law analysis unit also uses the emotion estimation function to analyze the emotions of the parties in past cases in real time and perform an analysis that takes emotional factors into consideration. For example, it analyzes the impact of the emotions of the parties on the judgment and interprets the case law. This makes it possible to perform an analysis that takes emotional factors into consideration.

[0063] The precedent analysis unit can also search overseas precedent databases and perform analysis from an international perspective. For example, the generation AI will search overseas precedent databases and build a system that performs analysis from an international perspective. For example, it will search American and European precedent databases and extract related precedents. The precedent analysis unit will also be equipped with a function that includes overseas precedent databases in its search targets and performs analysis from an international perspective. For example, it will analyze international precedents and compare them with domestic precedents. The precedent analysis unit will also include overseas precedent databases in its search targets and perform analysis from an international perspective. For example, it will analyze international precedents and compare them with domestic precedents. This makes analysis from an international perspective possible.

[0064] The precedent analysis unit can also refer to related academic papers and expert opinions when analyzing precedents. For example, the precedent analysis unit builds a system that allows the generation AI to refer to related academic papers and expert opinions when analyzing precedents. For example, it searches for academic papers and expert opinions related to precedents and reflects them in the analysis. The precedent analysis unit is also equipped with a function that allows the generation AI to analyze precedents by referring to academic papers and expert opinions. For example, it searches for academic papers and expert opinions related to precedents and reflects them in the analysis. The precedent analysis unit also refers to related academic papers and expert opinions when the generation AI analyzes precedents. For example, it searches for academic papers and expert opinions related to precedents and reflects them in the analysis. This makes it possible to perform analysis by referring to academic papers and expert opinions.

[0065] The case law analysis unit can use the emotion estimation function to analyze social reactions to past cases and reflect them in the analysis results. The case law analysis unit, for example, uses the emotion estimation function to analyze social reactions to past cases. For example, it analyzes media reports and social media reactions to the cases and reflects them in the analysis results. The case law analysis unit also builds a system that analyzes social reactions to past cases and reflects them in the analysis results. For example, it registers social reactions to the cases in a database and uses them in the analysis. The case law analysis unit also uses the emotion estimation function to analyze social reactions to past cases in real time and reflects them in the analysis results. For example, it analyzes media reports and social media reactions to the cases and reflects them in the analysis results. This makes it possible to perform analysis that takes social reactions into account.

[0066] The win rate providing unit can provide detailed analysis results and rationale for the win rate it provides, making it easier for users to understand. For example, the win rate providing unit builds a system that provides detailed analysis results and rationale for the win rate provided by the generation AI. For example, it provides statistical data on past precedents and interpretations of related laws. In addition, the win rate providing unit is equipped with a function that displays detailed analysis results and rationale when providing the win rate. For example, it displays graphs of the win and loss rates of similar past cases. In addition, the win rate providing unit provides detailed analysis results and rationale for the win rate provided by the generation AI. For example, it explains background information on the precedent and the judge's decision-making criteria. This makes it easier for users to understand the rationale for the win rate.

[0067] The win rate providing unit can also provide simulation results that take into account different scenarios for the win rate it provides. The win rate providing unit builds a system that provides simulation results that take into account different scenarios for the win rate provided by the generation AI. For example, it simulates how the win rate will change when different evidence or witnesses are added. The win rate providing unit is also equipped with a function that provides simulation results that take into account different scenarios. For example, it simulates how the win rate will change when the strength of evidence or the reliability of witnesses is changed. The win rate providing unit also provides simulation results that take into account different scenarios for the win rate provided by the generation AI. For example, it simulates how the win rate will change when different evidence or witnesses are added. This makes it possible to provide simulation results that take into account different scenarios.

[0068] The winning percentage providing unit can use the emotion estimation function to analyze the emotion a user feels when checking their winning percentage and provide appropriate advice. The winning percentage providing unit, for example, uses the emotion estimation function to build a system that analyzes the emotion a user feels when checking their winning percentage. For example, if the user feels anxious, advice to relax is provided. The winning percentage providing unit is also equipped with a function that analyzes the emotion a user feels when checking their winning percentage in real time and provides appropriate advice. For example, if the user feels nervous, an encouraging message is displayed. The winning percentage providing unit also uses the emotion estimation function to analyze the emotion a user feels when checking their winning percentage and provide appropriate advice. For example, if the user feels stressed, advice to relax is provided. This makes it possible to provide appropriate advice to the user.

[0069] In addition to providing the win rate, the win rate providing unit can also provide the expected amount of compensation and cost-effectiveness if successful. For example, the win rate providing unit builds a system that provides the expected amount of compensation and cost-effectiveness if successful in addition to the win rate provided by the generation AI. For example, it predicts the amount of compensation based on past case law data and calculates the cost-effectiveness. In addition, when providing the win rate, the win rate providing unit is equipped with a function that displays the expected amount of compensation and cost-effectiveness if successful. For example, it displays a graph of the predicted amount of compensation and cost-effectiveness. In addition to the win rate provided by the generation AI, the win rate providing unit provides the expected amount of compensation and cost-effectiveness if successful. For example, it predicts the amount of compensation based on past case law data and calculates the cost-effectiveness. This makes it possible to provide the amount of compensation and cost-effectiveness.

[0070] The win rate providing unit can visualize the win rate it provides and display it in a graph or chart, allowing the user to intuitively understand it. The win rate providing unit, for example, builds a system that visualizes the win rate provided by the generation AI and displays it in a graph or chart. For example, it displays the fluctuations in win rate in a line graph. Furthermore, the win rate providing unit is equipped with a function that visualizes the win rate and displays it in a graph or chart when providing it. For example, it displays the distribution of win rates in a pie chart. Furthermore, the win rate providing unit visualizes the win rate provided by the generation AI and displays it in a graph or chart. For example, it displays the fluctuations in win rate in a line graph. This allows the user to intuitively understand the win rate.

[0071] The winning rate providing unit can use the emotion estimation function to monitor in real time the emotions of the user when checking their winning rate and provide positive feedback. The winning rate providing unit, for example, uses the emotion estimation function to build a system that monitors in real time the emotions of the user when checking their winning rate. For example, if the user is feeling anxious, an encouraging message is displayed. The winning rate providing unit also has a function that monitors in real time the emotions of the user when checking their winning rate and provides positive feedback. For example, if the user is feeling nervous, advice to relax is provided. The winning rate providing unit also uses the emotion estimation function to monitor in real time the emotions of the user when checking their winning rate and provides positive feedback. For example, if the user is feeling stressed, advice to relax is provided. This makes it possible to provide positive feedback to the user.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] In the required information input section, the AI ​​automatically suggests additional information related to the information entered by the user, improving the accuracy of the input. For example, when a user enters the details of a lawsuit, the AI ​​automatically suggests relevant laws and past precedents. When a user enters "breach of contract," the AI ​​presents relevant contract laws and past precedents regarding breach of contract. Furthermore, when a user enters a relevant law, the AI ​​automatically suggests important provisions and interpretations related to that law. For example, when a user enters "Article 709 of the Civil Code," the AI ​​presents an interpretation of that provision and related precedents. Furthermore, when a user enters a past case, the AI ​​automatically suggests additional information related to that case. For example, when a user enters "lawsuit between Company A and Company B," the AI ​​presents details of the lawsuit and related precedents. This improves the accuracy of the user's input.

[0074] The necessary information input section allows the generation AI to automatically search for relevant legal documents and materials based on the information entered by the user and provide them as reference materials. For example, when a user enters the details of a lawsuit, the generation AI automatically searches for relevant legal documents and materials and provides them as reference materials. When a user enters "termination of labor contract," the generation AI searches for and provides relevant labor laws and past precedents. Furthermore, when a user enters a relevant law, the generation AI automatically searches for academic papers and expert opinions related to that law and provides them as reference materials. For example, when a user enters "consumer protection law," the generation AI searches for and provides relevant academic papers and expert opinions. Furthermore, when a user enters a past case, the generation AI automatically searches for legal documents and materials related to that case and provides them as reference materials. For example, when a user enters "patent infringement lawsuit," the generation AI searches for and provides relevant patent laws and past precedents. This allows users to easily obtain reference materials.

[0075] The required information input unit can use an emotion estimation function to analyze the user's emotions when entering information and provide an interface for reducing stress. For example, it is equipped with a function for analyzing the user's facial expressions and voice when entering information and estimating emotions. If the user is feeling stressed, it provides relaxation advice or changes the interface. In addition, it uses the emotion estimation function to analyze the user's emotions when entering information in real time and provides an interface for reducing stress. If the user is feeling anxious, it provides an encouraging message or relaxing music. Furthermore, a system is constructed that analyzes the user's emotions when entering information and provides an interface for reducing stress. If the user is nervous, it changes the color or design of the interface to provide a relaxing environment. This reduces the user's stress.

[0076] The required information input unit can add a voice input function, allowing users to input required information by voice. For example, using voice recognition technology, a function can be added that allows users to input required information by voice. When a user speaks, "Regarding breach of contract," the generation AI converts the content into text and enters it. In addition, using the voice input function, a system can be built that allows users to input required information by voice. When a user speaks, "Regarding termination of employment contract," the generation AI converts the content into text and enters it. Furthermore, a voice input function can be added to develop a system that allows users to input required information by voice. When a user speaks, "Regarding patent infringement lawsuit," the generation AI converts the content into text and enters it. This allows users to input required information by voice.

[0077] The required information input unit can develop a mobile application to allow users to easily enter required information from their smartphones. For example, a mobile application can be developed to allow users to easily enter required information from their smartphones. When a user enters the details of the lawsuit, the generation AI analyzes the details and provides related information. A mobile application can also be developed that allows users to enter required information from their smartphones. When a user enters the relevant law, the generation AI provides information related to that law. Furthermore, a system can be built using the mobile application that allows users to easily enter required information from their smartphones. When a user enters a past case, the generation AI provides information related to that case. This allows users to easily enter required information from their smartphones.

[0078] The precedent analysis unit can also take into account the background of the precedent and the judge's criteria when analyzing past precedents. For example, when the generation AI analyzes past precedents, it takes into account the background information of the precedent. It analyzes the time when the precedent was issued and the social background to understand the intent of the precedent. The generation AI also analyzes past precedents taking into account the judge's criteria. It analyzes the judge's past judgment trends and criteria to interpret the precedent. Furthermore, a system is built in which the generation AI analyzes past precedents taking into account the background of the precedent and the judge's criteria. The background information of the precedent and the judge's criteria are registered in a database and used in the analysis. This makes it possible to take into account the background of the precedent and the judge's criteria for analysis.

[0079] The case analysis unit can also take into account the impact of related legal amendments and new laws when analyzing precedents. For example, when the generation AI analyzes past precedents, it takes into account the impact of related legal amendments and new laws. If legal amendments are made after a precedent is issued, the impact will be reflected in the analysis. In addition, a system will be built in which the generation AI analyzes past precedents, taking into account the impact of new laws. If a new law is enacted, its impact will be reflected in the analysis. Furthermore, when the generation AI analyzes precedents, it will be equipped with a function that takes into account the impact of related legal amendments and new laws. It will analyze the impact of legal amendments and new laws on precedents and interpret the precedents. This makes it possible to perform analysis that takes into account the impact of legal amendments and new laws.

[0080] The case law analysis unit uses the emotion estimation function to analyze the emotions of the parties in past cases, and can perform an analysis that takes emotional factors into account. For example, the emotion estimation function is used to analyze the emotions of the parties in past cases. The emotions of the parties are estimated from the expressions and wording in the case text and reflected in the analysis. In addition, a system is constructed that analyzes the emotions of the parties in past cases and performs an analysis that takes emotional factors into account. The system analyzes the impact of the emotions of the parties on the judgment and interprets the case law. Furthermore, the emotion estimation function is used to analyze the emotions of the parties in past cases in real time and perform an analysis that takes emotional factors into account. The system analyzes the impact of the emotions of the parties on the judgment and interprets the case law. This makes it possible to perform an analysis that takes emotional factors into account.

[0081] The case law analysis unit can also search overseas case law databases and perform analysis from an international perspective. For example, a system can be built in which the generation AI also searches overseas case law databases and performs analysis from an international perspective. It searches American and European case law databases and extracts related cases. It will also be equipped with a function that includes overseas case law databases in its search targets and performs analysis from an international perspective. It analyzes international case law and compares it with domestic case law. Furthermore, the generation AI will include overseas case law databases in its search targets and perform analysis from an international perspective. It will analyze international case law and compare it with domestic case law. This makes it possible to perform analysis from an international perspective.

[0082] The case law analysis unit can use the emotion estimation function to analyze social reactions to past cases and reflect them in the analysis results. For example, the emotion estimation function is used to analyze social reactions to past cases. Media reports and social media reactions to the cases are analyzed and reflected in the analysis results. A system is also built to analyze social reactions to past cases and reflect them in the analysis results. Social reactions to the cases are registered in a database and used for analysis. Furthermore, the emotion estimation function is used to analyze social reactions to past cases in real time and reflect them in the analysis results. Media reports and social media reactions to the cases are analyzed and reflected in the analysis results. This makes it possible to perform analysis that takes social reactions into account.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The necessary information input section inputs the necessary information about the lawsuit from the user. For example, the user can input the details of the lawsuit, relevant laws, past cases, etc. The necessary information input section can also convert the input information into a format that is easy for the generation AI to analyze. Step 2: The case analysis unit analyzes past cases based on the information entered by the required information input unit. For example, the generation AI searches a database of past cases and extracts similar cases. Using a pre-fine-tuned model, the generation AI finds past cases in which similar litigation content and related laws were applied. Step 3: The win rate provider provides a win rate based on the results of the case analysis. For example, the generation AI quantifies the likelihood of the user's lawsuit by referring to the win and loss rates of similar cases in the past. This allows the court win rate provider to easily determine whether a lawsuit will be won or lost by non-legal professionals.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The 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.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 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.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the 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.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the 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.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 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 necessary information input section for inputting necessary information regarding the lawsuit from a user; a case precedent analysis unit that analyzes past cases based on the information input by the necessary item input unit; a winning percentage providing unit that provides a winning percentage based on the results of the analysis by the case analysis unit. A system characterized by:

2. The required information input unit Generative AI automatically suggests additional information related to the information entered by the user, improving the accuracy of the input.

2. The system of claim 1.

3. The required information input unit Based on the information entered by the user, the AI ​​automatically searches for relevant legal documents and materials and provides them as reference material.

2. The system of claim 1.

4. The required information input unit Analyzes emotions when users input data and provides an interface to reduce stress 2. The system of claim 1.

5. The required information input unit Add voice input functionality to allow users to enter required information by voice 2. The system of claim 1.

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