system

Abstract

We provide the system. [Solution] An information management system that stores and updates legal information and past case information, A discussion mechanism in which multiple artificial intelligences communicate with each other to make decisions, An interface means for inputting detailed information about a case and receiving a draft judgment generated by artificial intelligence, A judgment generation means that uses artificial intelligence to process detailed information of a case based on legal information and past case information to generate a judgment based on agreement, A court support system including this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern judicial systems, judges are required to efficiently process vast amounts of information such as laws and past cases and render fair judgments that exclude preconceptions and biases. However, it is difficult for conventional methods to fully meet such requirements, and there are biases derived from human judgment and limitations in information processing. Against this background, there is a need to establish a new mechanism that can achieve both fairness and efficiency in adjudication.

Means for Solving the Problems

[0005] To solve the above problems, this invention provides an information management means for accumulating and updating legal information and past cases, and constitutes a discussion means in which multiple artificial intelligences communicate with each other to make decisions based on this information. Furthermore, by providing an interface for inputting detailed case information and receiving a draft judgment generated by artificial intelligence, the invention provides a system including a judgment generation means in which artificial intelligence analyzes legal information and past cases and automatically generates a judgment based on agreement. This system improves the fairness and efficiency of trials and realizes objective judgments that eliminate human bias.

[0006] "Legal information" refers to data that includes the text of laws and regulations, such as statutes and ordinances, and their interpretations.

[0007] "Past case information" refers to data that includes precedents, judgments, and background information on cases handled by the courts in the past.

[0008] "Information management means" refers to a system or method for collecting, storing, and updating legal information and past case information as needed.

[0009] "Artificial intelligence" refers to a program or system that performs human intellectual activities that are mimicked by a computer.

[0010] A "discussion tool" is a method or system for multiple artificial intelligences to communicate with each other and make decisions based on the information they provide.

[0011] An "interface means" is a user interface mechanism for users to input information related to a trial and to receive a draft judgment generated by artificial intelligence.

[0012] A "judgment generation method" is a method or system in which artificial intelligence analyzes a case based on legal information and past case information and automatically generates a judgment based on agreement.

[0013] A "trial support system" is a comprehensive system designed to assist judges in making fair and objective judgments based on the law and past case information. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is a court support system that includes an information management means for accumulating legal information and past case information, a discussion means using multiple artificial intelligence agents, and an interface means for inputting case information and presenting draft judgments.

[0036] The server regularly updates legal information and past case data, functioning as a database that can be used by the AI ​​agent. This ensures that the AI ​​agent always has access to the latest information.

[0037] The terminal serves as an interface for users to input detailed information about the case, providing all the necessary information for the system. This includes the background of the case, relevant evidence, and information about those involved.

[0038] When a user inputs incident information through their device, the server uses that information to create a prompt for the AI ​​agent to begin a discussion. The AI ​​agent then conducts the discussion, forming opinions based on the law and past cases, and seeking a consensus.

[0039] The server monitors communication between AI agents and integrates the final draft judgment through a judgment generation mechanism. This process aims to produce a reasonable judgment based on solid legal grounds and past precedents.

[0040] As a concrete example, in a traffic accident trial, when a user inputs details of the accident and relevant laws into a terminal, the server provides this information to an AI agent, which then conducts a discussion based on appropriate past cases. Once the AI ​​agent's discussion concludes, the server generates a judgment and presents it to the user. This process allows for an unbiased, fair, and objective judgment.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server collects legal information and past case data into an information database and updates it regularly. This ensures that the AI ​​agent always has access to the latest information.

[0044] Step 2:

[0045] The user uses a device to enter case information related to the trial. This information should include details of the case, relevant evidence, and information about the people involved.

[0046] Step 3:

[0047] The terminal inputs case information, which is then sent to the server. The server uses this information to generate prompts for the AI ​​agent. The generated prompts should include background information on the case and legal issues.

[0048] Step 4:

[0049] The server delivers prompts to the AI ​​agents, and each agent begins a discussion based on them. The AI ​​agents form opinions by referring to legal information and past case data, and analyze the case from multiple perspectives.

[0050] Step 5:

[0051] The server monitors discussions between AI agents and facilitates agreement among them. It also supports information exchange and coordination between agents as needed.

[0052] Step 6:

[0053] After consensus is reached, the server aggregates the opinions of the AI ​​agents through a judgment generation mechanism and automatically generates a draft judgment. This draft judgment clearly outlines the legal basis.

[0054] Step 7:

[0055] The generated draft judgment is presented to the user. The user can review the judgment and, if necessary, provide additional questions or feedback from their device.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] When making legal decisions, it is difficult to make objective and fair decisions while always referring to the latest and most relevant legal knowledge and past case knowledge. Furthermore, there is the challenge of forming rational judgments that take into account discussions from different perspectives.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes an information management means for accumulating and updating legal knowledge and past case knowledge, a discussion means for multiple artificial intelligence components to communicate with each other and make decisions, and an interface means for inputting detailed case information and receiving a decision proposal generated by artificial intelligence. This makes it possible to make objective and rational decisions using the latest legal information.

[0061] "Legal knowledge" is a general term for information related to the law, the content of legal provisions, and data concerning their interpretation.

[0062] "Knowledge of past cases" refers to information about the details of past cases and judgments based on the law.

[0063] "Information management tools" are systems for efficiently collecting, updating, and maintaining legal knowledge and knowledge of past cases.

[0064] "Artificial intelligence components" refer to artificial intelligence models and software used to analyze legal knowledge and past cases, and to conduct discussions and make judgments.

[0065] "Discussion method" refers to the process by which multiple artificial intelligence components exchange opinions and make decisions.

[0066] An "interface means" is a mechanism that allows users to input detailed information about a case and receive a decision proposal generated by artificial intelligence.

[0067] A "decision-generating mechanism" is a process in which artificial intelligence, based on legal knowledge and knowledge of past cases, creates a final, consensus-based decision from detailed information of a given case.

[0068] A "monitoring mechanism" is a system for monitoring communication between artificial intelligence components and integrating rational decisions.

[0069] This court support system is designed to generate rational and objective judgments by comprehensively managing legal knowledge and past case knowledge, while utilizing artificial intelligence components.

[0070] The server collects legal knowledge and past case knowledge from specialized legal databases through information management means and updates it regularly. Specifically, the server retrieves legal information from external data sources via APIs and stores this information in a MySQL® database, thereby maintaining a constantly up-to-date knowledge base.

[0071] The device provides a web interface using React, allowing users to intuitively input detailed information about the case. This interface has forms for entering information such as the case background, those involved, and relevant evidence, enabling users to directly submit data to the system.

[0072] When a user enters incident information into the terminal, the server uses a generative AI model to generate a prompt message based on that information. This prompt message includes key points of the incident information and related laws, and is used to initiate a discussion with the AI ​​agent.

[0073] The AI ​​agent analyzes legal knowledge and past case knowledge to engage in discussions from different perspectives. The server monitors this interaction and integrates a rational and unbiased final decision. The user can receive the results through the interface.

[0074] As a concrete example, consider a case where a user inputs details of a traffic accident trial. The server generates a prompt message such as, "Regarding the traffic accident case, it occurred at point A, and the relevant law is Article X of the Traffic Act. Please refer to similar past cases and propose a judgment." Based on this information, the AI ​​agent makes a decision, allowing the server to present an appropriate judgment to the user.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server collects legal knowledge and past case knowledge from an external database and stores it in a MySQL database. The input consists of JSON data containing legal and case information obtained from an external API. This data is parsed, converted to the required format for the database, and then stored. Specifically, the server executes a scheduled job at 3 AM daily, retrieving data from the external data source and updating the database.

[0078] Step 2:

[0079] The user uses their device to input detailed information about the case. This input includes data such as background information, information about those involved, and evidence. The device uses this information to send it to the server, so a web form using React receives data from the user and transmits it to the server in real time. Specifically, the user fills in the required fields on the web form and confirms the information by pressing the submit button.

[0080] Step 3:

[0081] The server generates prompt messages for the AI ​​agent using a generative AI model based on the incident information received from the user. The input is incident information from the user, and the server creates prompt messages by combining this information with appropriate legal knowledge and case examples. Specifically, the server analyzes the incident information, searches the database for highly relevant laws and past cases, and adds them to the prompt messages.

[0082] Step 4:

[0083] The server sends the generated prompt to the AI ​​agent, initiating a discussion. Here, the AI ​​agent is given legal and case information to analyze as input, and an interim opinion based on agreement between the AI ​​agents is formed as output. Specifically, the AI ​​agents individually consider the prompt and exchange opinions through communication.

[0084] Step 5:

[0085] The server integrates the discussion results from the AI ​​agents and generates a final decision. The input consists of various perspectives provided by the AI ​​agents, which are then aggregated to arrive at a rational judgment. Specifically, the server organizes the discussion results, uses natural language processing to format them into a judgment document, and presents it to the user.

[0086] Step 6:

[0087] The user receives and reviews the final draft judgment via their device. The output includes the judgment document provided by the server, which can be visually reviewed through the interface. Specifically, the user can review the details of the draft judgment via the device's interface and provide feedback as needed.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] Responding quickly and appropriately to everyday legal issues and troubles is an important yet often challenging task for individuals and businesses. Current legal consultation services have limited access to experts and are often time-consuming and expensive, making the process of obtaining solutions burdensome. Therefore, there is a need for the development of a system that utilizes legal information and past cases to provide efficient and rapid legal consultations through artificial intelligence.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes data management means for accumulating and updating legal information and past case information, opinion exchange means for multiple artificial intelligences to interact with each other and make decisions, and user interface means for inputting legal questions and detailed information and receiving solutions generated by artificial intelligence. This enables users to quickly seek legal advice and obtain effective legal advice and solutions.

[0093] "Legal information" refers to general information related to the law, including laws, regulations, precedents, and interpretations and guidelines based thereon.

[0094] "Past case information" refers to data on actual legal cases and precedents, serving as concrete case studies.

[0095] "Data management means" refers to the function of a system that appropriately stores information and makes it available for updating and retrieval as needed.

[0096] "Artificial intelligence" is a technology that uses computer programs to mimic human intellectual abilities and perform data analysis and decision-making.

[0097] "Method of exchanging opinions" refers to the process by which multiple artificial intelligences exchange information with each other and make decisions collaboratively.

[0098] "User interface means" refers to all types of operation screens and input devices that allow users to input information into a system or receive output from a system.

[0099] A "result generation method" is a function in which artificial intelligence analyzes input information and generates solutions or results that should be presented to the user.

[0100] "Individuals" refers to ordinary users who are interested in legal issues and require routine consultation.

[0101] A "system" refers to a collection of components that integrate the aforementioned means and function as a whole.

[0102] The system for implementing this invention is constructed by combining data management means for managing legal information and past case information, user interface means for receiving user input, artificial intelligence-based opinion exchange means, and result generation means.

[0103] The server stores legal information and past case data, and develops artificial intelligence learning models using programming languages ​​and libraries such as Python and TENSORFLOW®. This allows the server to always perform data analysis based on the latest legal information. MySQL and PostgreSQL are used for database management.

[0104] The terminal serves as the user interface, providing a screen for users to input legal questions or details of problems. This interface is built using a web framework such as Django or Flask, providing users with an intuitive user experience.

[0105] When a user inputs details of a case or legal issue, the server generates this as a prompt, triggering the AI ​​agent to form an opinion. The AI ​​agent is composed of multiple models with different perspectives, and each agent exchanges opinions while conducting its own analysis to seek the optimal legal solution.

[0106] For example, if a user submits a request for advice regarding a traffic accident, the server will have an AI agent refer to relevant laws and past precedents, and based on the analysis results, it will provide the user with appropriate advice and an estimate of the settlement amount. In this way, users can quickly seek legal advice and obtain effective legal guidance.

[0107] As a concrete example, the following prompt may be generated:

[0108] "AI agent, regarding insurance claim negotiations in a traffic accident, please provide the best possible suggestions based on the details entered by user X, referencing past precedents."

[0109] This system creates an environment where legal consultations can be easily accessed, allowing users to deal with legal issues with peace of mind.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The terminal accepts legal questions and details of cases from the user as input. The user enters information about the legal issue in text format, and the terminal prepares to send that data to the server.

[0113] Step 2:

[0114] The server receives user input data sent from the terminal. The server analyzes this input data using natural language processing techniques to extract relevant keywords and legal categories. This analysis process identifies the scope of legal information and past case information that needs to be addressed.

[0115] Step 3:

[0116] The server provides the AI ​​agent with prompt sentences generated based on the analysis. These prompt sentences include instructions to refer to legal information and past case information, aligned with the extracted keywords and legal categories. The AI ​​agent then initiates an exchange of opinions among multiple artificial intelligence models.

[0117] Step 4:

[0118] The AI ​​agent references legal information and past case data from multiple perspectives, engaging in discussions while retrieving relevant information from the database. Utilizing a deep learning model based on TensorFlow, the AI ​​agent attempts to reach a consensus to determine the optimal legal solution based on the discussion.

[0119] Step 5:

[0120] The server receives the results of the discussion from the AI ​​agent and generates final solutions and advice. This output data, including specific advice and legally recommended actions, is ready to be presented to the user.

[0121] Step 6:

[0122] The terminal displays solutions received from the server to the user. Based on the advice and solutions presented, the user can decide what action to take next. In this way, the terminal supports the user in quickly addressing legal issues.

[0123] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0124] The system according to the present invention integrates legal information, past case information, and user sentiment information to support court judgments. Specifically, the server maintains a legal database and a case database, ensuring that the AI ​​agent always has access to the latest information.

[0125] The user inputs case information related to the trial via a terminal. The terminal incorporates an emotion engine that recognizes the user's emotions based on their input and responses. This emotion information is analyzed in real time and sent to a server, which then provides it to an AI agent.

[0126] The server also generates specific prompts based on case information and delivers them to the AI ​​agent. The AI ​​agent develops arguments based on legal information and past cases to form a draft judgment. In this process, user sentiment information is also analyzed and considered as a factor influencing the draft judgment. The sentiment engine's data aims to ensure that the judgment is not only legally just but also acceptable to the user.

[0127] For example, if a user expresses strong emotions while entering details of a case on their device, the emotion engine analyzes this information and reports it to the server. The AI ​​agent takes this emotional data into account and generates a draft judgment that maintains legal validity while also considering the emotional aspects. The draft judgment presented to the user thus achieves a balance between legal accuracy and emotional understanding.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The user uses a device to input case information related to the trial, while an emotion engine built into the device monitors the user's input and responses. The emotion engine analyzes the user's emotions from facial expressions, voice tone, and nuances in the text, and generates emotion information in real time.

[0131] Step 2:

[0132] The terminal sends user emotion information generated by the emotion engine, along with incident information entered by the user, to the server. The server receives this information and organizes the details and related information of the incident.

[0133] Step 3:

[0134] Based on the incident information received by the server, prompts are generated to be provided to the AI ​​agent. These prompts include background information on the incident, the issues at issue, and information about the user's feelings.

[0135] Step 4:

[0136] The server generates prompts which are then delivered to the AI ​​agents, who begin discussions based on these prompts. Each agent forms an opinion and analyzes the case from multiple perspectives, referring to legal information, past cases, and sentiment information.

[0137] Step 5:

[0138] The server monitors the progress of discussions among AI agents, ensuring that all data, including emotional information, is appropriately considered and adjusted to reach a consensus. This facilitates the generation of draft judgments that balance legal validity with emotional aspects.

[0139] Step 6:

[0140] After an agreement is reached, the server generates a final draft judgment through a judgment generation system. This draft judgment is based on legal grounds but also reflects the user's emotional understanding.

[0141] Step 7:

[0142] The server presents the user with a draft judgment it has generated, which the user then reviews. If necessary, the user can provide additional questions or feedback on the judgment from their device.

[0143] (Example 2)

[0144] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0145] Conventional court support systems primarily generate judgments based on legal information and past case data, but they have limitations in reaching consensus that reflects the emotional needs of users. As a result, even if a judgment is legally justified, it may be unacceptable to the parties involved. Furthermore, there has been a lack of methods for artificial intelligence with different perspectives to collaborate in discussions and generate more comprehensive judgments.

[0146] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0147] In this invention, the server includes information processing means for storing and updating legal-related data and past case data, sentiment analysis means for analyzing user input information and extracting emotional information, and communication means for integrating the extracted emotional information and input case information and transferring the data. This makes it possible to generate more acceptable draft judgments that take into account the user's emotions in addition to legal justification. Furthermore, multiple generation AI agents with different perspectives discuss with each other and form draft judgments while also considering emotional information, thereby achieving comprehensive and convincing consensus building.

[0148] "Legal data" refers to databases containing information on legal documents, precedents, and laws, providing the foundational information necessary for trials and agreement formation.

[0149] "Past case data" refers to records of past trials and lawsuits, and includes case information that should be used as a reference for precedents.

[0150] "Information processing means" refers to methods and devices for accumulating legal data and past case data and updating them with the latest information.

[0151] "Emotional analysis methods" refer to techniques and devices for extracting and analyzing emotional information from user input and behavior.

[0152] "Communication methods" refer to techniques and devices for integrating analyzed emotional information and incident information, and for sending and receiving data between servers and agents.

[0153] A "generative AI agent" refers to artificial intelligence that performs legal analysis based on input information and forms draft judgments without human intervention.

[0154] "Judgment generation means" refers to methods and devices for generating AI agents to create draft judgments using legal data, past case data, and emotional information.

[0155] "Interface means" refers to methods or devices that present the generated draft judgment to the user and allow the user to receive the result.

[0156] "Emotional information" refers to information that indicates the user's emotional state and psychological reactions, and is a factor that influences the resulting draft judgment.

[0157] One embodiment of this invention is a court support system that integrates legal information and user emotional information to generate a just and acceptable draft judgment.

[0158] The server first stores legal data and historical case data. This data is stored on a database management system and updated regularly. For example, using an SQL database enables rapid and efficient large-scale data processing. Furthermore, the server has a generative AI agent that generates draft judgments based on prompts. This generative AI agent uses a generative AI model based on natural language processing technology.

[0159] The terminal is used by users to input information related to the trial. It implements sentiment analysis software that analyzes emotional information obtained from text input and user actions. The analysis results are sent to the server in real time. For sentiment analysis, natural language processing libraries and APIs, such as those written in Python, are used.

[0160] The user enters details of the trial via their device and receives a draft judgment as a result. The information entered by the user is analyzed by the server, and prompt messages are generated for use by the AI ​​agent. An example of such a prompt message would be, "Considering past court cases and laws regarding noise problems, and given that the emotion engine has reported the user's strong anger, please propose a solution to the problem."

[0161] This system integrates legal and emotional information, making it possible to generate draft judgments that balance legal legitimacy with user acceptance.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] The user enters detailed information about the trial via the terminal. This information includes the circumstances of the case and details of the parties involved. This information is entered into the terminal as data necessary for sentiment analysis. The entered data forms the basis for analysis in the next step.

[0165] Step 2:

[0166] The sentiment analysis software embedded in the terminal extracts emotional information from the user's input. Specifically, it analyzes the content and context of the text using natural language processing techniques to determine the user's emotional state (e.g., anger, sadness, joy). The emotional information obtained through this analysis becomes data sent to the server. The input is the user's text information, and the output is the analyzed emotional data.

[0167] Step 3:

[0168] The terminal sends analyzed emotional information and detailed incident information to the server as data. Specifically, the terminal converts this information into JSON format and makes a POST request to the server's API endpoint using a secure communication protocol (e.g., HTTPS). The input is the data obtained within the terminal, and the output is a data packet prepared for transmission to the server.

[0169] Step 4:

[0170] The server generates a prompt based on the emotional and incident information received from the terminal. This prompt is structured as a command statement for the generation AI model to execute. Specifically, it creates a prompt that takes into account the context of the incident and the emotional state, while referring to legal information and past cases. The input is the received user information, and the output is the prompt statement passed to the generation AI model.

[0171] Step 5:

[0172] The generative AI agent generates a draft judgment based on prompts received from the server. Specifically, the AI ​​agent refers to legal databases and past case data to generate the optimal response to the requested prompt in natural language. This process involves data analysis and text generation using a generative AI model. The input is the prompt from the server, and the output is the generated draft judgment document.

[0173] Step 6:

[0174] The server sends the generated draft judgment to the user's terminal. Specifically, it formats the draft judgment into a format that is easy for the user to understand and sends it back to the terminal as a notification. The input is the result generated by the AI ​​agent, and the output is the draft judgment presented to the user.

[0175] Step 7:

[0176] The user receives and reviews a draft judgment sent from the server via their terminal. Specifically, the content of the draft judgment is displayed on the screen, and additional feedback is entered as needed. This feedback may be further sent to the system. The input is the draft judgment from the server, and the output is the user's acknowledgment of receipt and feedback information.

[0177] (Application Example 2)

[0178] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0179] When individuals seek legal advice within their families, there is a challenge in providing a judgment that comprehensively considers both legal issues and emotional factors. To address this challenge, it is necessary to make judgments that take into account not only legal information and past cases, but also the emotional state of the person seeking advice.

[0180] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0181] In this invention, the server includes data management means for accumulating and updating legal information and past case information; discussion means for multiple information processing devices to exchange information with each other and make decisions; operation means for analyzing the user's emotional state and receiving a draft judgment generated by the information processing device; and judgment formulation means for the information processing device to generate a judgment based on legal information and past case information, taking into account the user's emotional state. This makes it possible to provide legally appropriate information while taking into consideration the user's emotions when they face legal problems in their home.

[0182] "Legal information" is a term that refers to all legally relevant information, such as laws, regulations, and precedents.

[0183] "Past case information" refers to data that includes details of past legal cases and judgments.

[0184] "Data management means" refers to the mechanisms and technologies for collecting, organizing, and updating information.

[0185] An "information processing device" refers to computing resources and systems used to process data and output results.

[0186] "Discussion methods" refer to methods by which information processing devices share information with each other, exchange opinions, and make decisions.

[0187] "Emotional state" is a concept that describes the psychological or emotional response that a user exhibits in a particular situation.

[0188] "Operating means" refers to the interface used by the user to input information and receive output from the system.

[0189] "Methods for formulating judgments" refer to systems that use legal information and past case information to formulate appropriate judgments based on specific criteria.

[0190] This invention is a system that uses an information processing device installed in the home to provide useful information to users when they seek legal advice. The information processing device collects and updates legal information and past case information, and analyzes the user's emotional state. The server stores the legal information and past case information using data management means and maintains it in an up-to-date state. In addition, multiple information processing devices exchange information with each other to generate draft judgments that take into account the user's emotional state.

[0191] Specifically, when a user conducts legal consultation via a voice input device, sentiment analysis software on the terminal analyzes the user's voice and evaluates their emotional state. For example, the voice data is converted to text using the Google® Cloud Speech-to-Text API, and that text is then analyzed using natural language processing technology. This sentiment information is transmitted to a server in real time and used in the process of formulating judgments.

[0192] The AI ​​model uses the BERT model from the Transformers library to retrieve relevant information from a legal database and generate a draft judgment. This information is managed using data management software such as MySQL. The system then presents this draft judgment to the user, providing a satisfactory answer.

[0193] As a concrete example, if a dispute arises within the family regarding a contract between friends, the sentiment analysis software analyzes the user's anxiety, and the server uses laws and past precedents to provide legally appropriate and emotionally sensitive advice. An example of a prompt used for the generative AI model would be:

[0194] "The user is showing signs of high stress. Please provide empathetic advice based on legal information regarding the contract and past cases."

[0195] This system can provide support that includes emotional care while addressing legal issues faced within the family.

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] Users seek legal advice through a voice input device. During this process, users describe their legal questions and situations verbally. The input data is recorded as audio.

[0199] Step 2:

[0200] The device uses the Google Cloud Speech-to-Text API to convert audio data into text data. This process analyzes the audio signal and converts it into a string based on a language model. The output is the audio converted into text data.

[0201] Step 3:

[0202] The terminal inputs the converted text data into sentiment analysis software to evaluate the user's emotional state. Here, natural language processing techniques are used to analyze the emotional characteristics of the language and identify the emotional state the user is exhibiting, such as "stress" or "anxiety." The output is the analyzed emotional information.

[0203] Step 4:

[0204] The terminal sends the analyzed emotion information to the server. The server receives the emotion information and prepares it to be used as a parameter for other data processing.

[0205] Step 5:

[0206] The server retrieves legal data and past case information from a MySQL database. The input here consists of keywords and topics related to legal consultations. The server uses this information to extract relevant legal data.

[0207] Step 6:

[0208] The server generates prompts for a generative AI model (e.g., Transformers' BERT model) based on acquired legal and emotional information. These prompts include context corresponding to the user's emotional state. By inputting these prompts into the generative AI model, a draft judgment is generated that considers both legal information and emotional considerations.

[0209] Step 7:

[0210] The server sends the generated draft judgment to the terminal. The terminal presents this information to the user and can also provide supplementary information according to the user's needs. The output is emotionally sensitive legal advice.

[0211] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0212] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0213] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0214] [Second Embodiment]

[0215] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0216] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0217] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0218] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0219] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0220] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0221] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0222] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0223] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0224] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0225] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0226] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0227] This invention is a court support system that includes an information management means for accumulating legal information and past case information, a discussion means using multiple artificial intelligence agents, and an interface means for inputting case information and presenting draft judgments.

[0228] The server regularly updates legal information and past case data, functioning as a database that can be used by the AI ​​agent. This ensures that the AI ​​agent always has access to the latest information.

[0229] The terminal serves as an interface for users to input detailed information about the case, providing all the necessary information for the system. This includes the background of the case, relevant evidence, and information about those involved.

[0230] When a user inputs incident information through their device, the server uses that information to create a prompt for the AI ​​agent to begin a discussion. The AI ​​agent then conducts the discussion, forming opinions based on the law and past cases, and seeking a consensus.

[0231] The server monitors communication between AI agents and integrates the final draft judgment through a judgment generation mechanism. This process aims to produce a reasonable judgment based on solid legal grounds and past precedents.

[0232] As a concrete example, in a traffic accident trial, when a user inputs details of the accident and relevant laws into a terminal, the server provides this information to an AI agent, which then conducts a discussion based on appropriate past cases. Once the AI ​​agent's discussion concludes, the server generates a judgment and presents it to the user. This process allows for an unbiased, fair, and objective judgment.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] The server collects legal information and past case data into an information database and updates it regularly. This ensures that the AI ​​agent always has access to the latest information.

[0236] Step 2:

[0237] The user uses a device to enter case information related to the trial. This information should include details of the case, relevant evidence, and information about the people involved.

[0238] Step 3:

[0239] The terminal inputs case information, which is then sent to the server. The server uses this information to generate prompts for the AI ​​agent. The generated prompts should include background information on the case and legal issues.

[0240] Step 4:

[0241] The server delivers prompts to the AI ​​agents, and each agent begins a discussion based on them. The AI ​​agents form opinions by referring to legal information and past case data, and analyze the case from multiple perspectives.

[0242] Step 5:

[0243] The server monitors discussions between AI agents and facilitates agreement among them. It also supports information exchange and coordination between agents as needed.

[0244] Step 6:

[0245] After consensus is reached, the server aggregates the opinions of the AI ​​agents through a judgment generation mechanism and automatically generates a draft judgment. This draft judgment clearly outlines the legal basis.

[0246] Step 7:

[0247] The generated draft judgment is presented to the user. The user can review the judgment and, if necessary, provide additional questions or feedback from their device.

[0248] (Example 1)

[0249] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0250] When making legal decisions, it is difficult to make objective and fair decisions while always referring to the latest and most relevant legal knowledge and past case knowledge. Furthermore, there is the challenge of forming rational judgments that take into account discussions from different perspectives.

[0251] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0252] In this invention, the server includes an information management means for accumulating and updating legal knowledge and past case knowledge, a discussion means for multiple artificial intelligence components to communicate with each other and make decisions, and an interface means for inputting detailed case information and receiving a decision proposal generated by artificial intelligence. This makes it possible to make objective and rational decisions using the latest legal information.

[0253] "Legal knowledge" is a general term for information related to the law, the content of legal provisions, and data concerning their interpretation.

[0254] "Knowledge of past cases" refers to information about the details of past cases and judgments based on the law.

[0255] "Information management tools" are systems for efficiently collecting, updating, and maintaining legal knowledge and knowledge of past cases.

[0256] "Artificial intelligence components" refer to artificial intelligence models and software used to analyze legal knowledge and past cases, and to conduct discussions and make judgments.

[0257] "Discussion method" refers to the process by which multiple artificial intelligence components exchange opinions and make decisions.

[0258] An "interface means" is a mechanism that allows users to input detailed information about a case and receive a decision proposal generated by artificial intelligence.

[0259] A "decision-generating mechanism" is a process in which artificial intelligence, based on legal knowledge and knowledge of past cases, creates a final, consensus-based decision from detailed information of a given case.

[0260] A "monitoring mechanism" is a system for monitoring communication between artificial intelligence components and integrating rational decisions.

[0261] This court support system is designed to generate rational and objective judgments by comprehensively managing legal knowledge and past case knowledge, while utilizing artificial intelligence components.

[0262] The server collects legal knowledge and past case knowledge from specialized legal databases through information management means and updates it regularly. Specifically, the server retrieves legal information from external data sources via APIs and stores this information in a MySQL database, thereby maintaining a constantly up-to-date knowledge base.

[0263] The device provides a web interface using React, allowing users to intuitively input detailed information about the case. This interface has forms for entering information such as the case background, those involved, and relevant evidence, enabling users to directly submit data to the system.

[0264] When a user enters incident information into the terminal, the server uses a generative AI model to generate a prompt message based on that information. This prompt message includes key points of the incident information and related laws, and is used to initiate a discussion with the AI ​​agent.

[0265] The AI ​​agent analyzes legal knowledge and past case knowledge to engage in discussions from different perspectives. The server monitors this interaction and integrates a rational and unbiased final decision. The user can receive the results through the interface.

[0266] As a concrete example, consider a case where a user inputs details of a traffic accident trial. The server generates a prompt message such as, "Regarding the traffic accident case, it occurred at point A, and the relevant law is Article X of the Traffic Act. Please refer to similar past cases and propose a judgment." Based on this information, the AI ​​agent makes a decision, allowing the server to present an appropriate judgment to the user.

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] The server collects legal knowledge and past case knowledge from an external database and stores it in a MySQL database. The input consists of JSON data containing legal and case information obtained from an external API. This data is parsed, converted to the required format for the database, and then stored. Specifically, the server executes a scheduled job at 3 AM daily, retrieving data from the external data source and updating the database.

[0270] Step 2:

[0271] The user uses their device to input detailed information about the case. This input includes data such as background information, information about those involved, and evidence. The device uses this information to send it to the server, so a web form using React receives data from the user and transmits it to the server in real time. Specifically, the user fills in the required fields on the web form and confirms the information by pressing the submit button.

[0272] Step 3:

[0273] The server generates prompt messages for the AI ​​agent using a generative AI model based on the incident information received from the user. The input is incident information from the user, and the server creates prompt messages by combining this information with appropriate legal knowledge and case examples. Specifically, the server analyzes the incident information, searches the database for highly relevant laws and past cases, and adds them to the prompt messages.

[0274] Step 4:

[0275] The server sends the generated prompt to the AI ​​agent, initiating a discussion. Here, the AI ​​agent is given legal and case information to analyze as input, and an interim opinion based on agreement between the AI ​​agents is formed as output. Specifically, the AI ​​agents individually consider the prompt and exchange opinions through communication.

[0276] Step 5:

[0277] The server integrates the discussion results from the AI ​​agents and generates a final decision. The input consists of various perspectives provided by the AI ​​agents, which are then aggregated to arrive at a rational judgment. Specifically, the server organizes the discussion results, uses natural language processing to format them into a judgment document, and presents it to the user.

[0278] Step 6:

[0279] The user receives and reviews the final draft judgment via their device. The output includes the judgment document provided by the server, which can be visually reviewed through the interface. Specifically, the user can review the details of the draft judgment via the device's interface and provide feedback as needed.

[0280] (Application Example 1)

[0281] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0282] Responding quickly and appropriately to daily legal problems and troubles is an important but often not easy task for individuals and companies. Current legal consultation services have limited access to experts and are often time-consuming and costly, so it is burdensome to obtain solutions. Therefore, there is a need to develop a system that can utilize legal information and past cases to achieve efficient and rapid legal consultation by artificial intelligence.

[0283] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0284] In this invention, the server includes data management means for accumulating and updating legal information and past case information, opinion exchange means for multiple artificial intelligences to interact with each other to make decisions, and user interface means for inputting legal questions and detailed information and receiving solutions generated by artificial intelligence. As a result, users can quickly conduct legal consultations and obtain effective legal advice and solutions.

[0285] "Legal information" refers to general information related to laws, such as statutes, regulations, case laws, and interpretations and guidelines based on them.

[0286] "Past case information" refers to data on actual legal cases and case laws that have occurred and serves as a specific case study.

[0287] "Data management means" is a function of a system that appropriately accumulates information and enables it to be updated and retrieved as needed.

[0288] "Artificial intelligence" is a technology that mimics human intellectual abilities using computer programs and performs data analysis and decision-making.

[0289] "Method of exchanging opinions" refers to the process by which multiple artificial intelligences exchange information with each other and make decisions collaboratively.

[0290] "User interface means" refers to all types of operation screens and input devices that allow users to input information into a system or receive output from a system.

[0291] A "result generation method" is a function in which artificial intelligence analyzes input information and generates solutions or results that should be presented to the user.

[0292] "Individuals" refers to ordinary users who are interested in legal issues and require routine consultation.

[0293] A "system" refers to a collection of components that integrate the aforementioned means and function as a whole.

[0294] The system for implementing this invention is constructed by combining data management means for managing legal information and past case information, user interface means for receiving user input, artificial intelligence-based opinion exchange means, and result generation means.

[0295] The server stores legal information and past case data, and develops artificial intelligence learning models using programming languages ​​and libraries such as Python and TensorFlow. This allows the server to always perform data analysis based on the latest legal information. MySQL and PostgreSQL are used for database management.

[0296] The terminal serves as the user interface, providing a screen for users to input legal questions or details of problems. This interface is built using a web framework such as Django or Flask, providing users with an intuitive user experience.

[0297] When a user inputs details of a case or legal issue, the server generates this as a prompt, triggering the AI ​​agent to form an opinion. The AI ​​agent is composed of multiple models with different perspectives, and each agent exchanges opinions while conducting its own analysis to seek the optimal legal solution.

[0298] For example, if a user submits a request for advice regarding a traffic accident, the server will have an AI agent refer to relevant laws and past precedents, and based on the analysis results, it will provide the user with appropriate advice and an estimate of the settlement amount. In this way, users can quickly seek legal advice and obtain effective legal guidance.

[0299] As a concrete example, the following prompt may be generated:

[0300] "AI agent, regarding insurance claim negotiations in a traffic accident, please provide the best possible suggestions based on the details entered by user X, referencing past precedents."

[0301] This system creates an environment where legal consultations can be easily accessed, allowing users to deal with legal issues with peace of mind.

[0302] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0303] Step 1:

[0304] The terminal accepts legal questions and details of cases from the user as input. The user enters information about the legal issue in text format, and the terminal prepares to send that data to the server.

[0305] Step 2:

[0306] The server receives the user's input data sent from the terminal. The server analyzes this input data using natural language processing technology and extracts relevant keywords and legal categories. Through this analysis process, the scope of legal information and past case information to be addressed is identified.

[0307] Step 3:

[0308] The server provides the prompt text generated based on the analysis to the AI agent. The prompt text includes instructions for referring to legal information and past case information along with the extracted keywords and legal categories. The AI agent then initiates an exchange of opinions among multiple artificial intelligence models upon receiving this.

[0309] Step 4:

[0310] The AI agent refers to legal information and past case information from multiple perspectives and conducts discussions while retrieving relevant information within the database. The AI agent attempts to reach a consensus for determining the optimal legal solution based on the discussions by leveraging a deep learning model using TensorFlow.

[0311] Step 5:

[0312] The server receives the result of the discussion from the AI agent and generates the final solution or advice. This output data includes specific advice and recommended actions based on the law, and is prepared for presentation to the user.

[0313] Step 6:

[0314] The terminal displays the solution received from the server to the user. The user can then determine the actions to take next based on the presented advice and solution. Thereby, the terminal provides support for the user to respond quickly to legal issues.

[0315] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0316] The system according to the present invention integrates legal information, past case information, and user sentiment information to support court judgments. Specifically, the server maintains a legal database and a case database, ensuring that the AI ​​agent always has access to the latest information.

[0317] The user inputs case information related to the trial via a terminal. The terminal incorporates an emotion engine that recognizes the user's emotions based on their input and responses. This emotion information is analyzed in real time and sent to a server, which then provides it to an AI agent.

[0318] The server also generates specific prompts based on case information and delivers them to the AI ​​agent. The AI ​​agent develops arguments based on legal information and past cases to form a draft judgment. In this process, user sentiment information is also analyzed and considered as a factor influencing the draft judgment. The sentiment engine's data aims to ensure that the judgment is not only legally just but also acceptable to the user.

[0319] For example, if a user expresses strong emotions while entering details of a case on their device, the emotion engine analyzes this information and reports it to the server. The AI ​​agent takes this emotional data into account and generates a draft judgment that maintains legal validity while also considering the emotional aspects. The draft judgment presented to the user thus achieves a balance between legal accuracy and emotional understanding.

[0320] The following describes the processing flow.

[0321] Step 1:

[0322] The user uses a device to input case information related to the trial, while an emotion engine built into the device monitors the user's input and responses. The emotion engine analyzes the user's emotions from facial expressions, voice tone, and nuances in the text, and generates emotion information in real time.

[0323] Step 2:

[0324] The terminal sends user emotion information generated by the emotion engine, along with incident information entered by the user, to the server. The server receives this information and organizes the details and related information of the incident.

[0325] Step 3:

[0326] Based on the incident information received by the server, prompts are generated to be provided to the AI ​​agent. These prompts include background information on the incident, the issues at issue, and information about the user's feelings.

[0327] Step 4:

[0328] The server generates prompts which are then delivered to the AI ​​agents, who begin discussions based on these prompts. Each agent forms an opinion and analyzes the case from multiple perspectives, referring to legal information, past cases, and sentiment information.

[0329] Step 5:

[0330] The server monitors the progress of discussions among AI agents, ensuring that all data, including emotional information, is appropriately considered and adjusted to reach a consensus. This facilitates the generation of draft judgments that balance legal validity with emotional aspects.

[0331] Step 6:

[0332] After an agreement is reached, the server generates a final draft judgment through a judgment generation system. This draft judgment is based on legal grounds but also reflects the user's emotional understanding.

[0333] Step 7:

[0334] The server presents the user with a draft judgment it has generated, which the user then reviews. If necessary, the user can provide additional questions or feedback on the judgment from their device.

[0335] (Example 2)

[0336] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0337] Conventional court support systems primarily generate judgments based on legal information and past case data, but they have limitations in reaching consensus that reflects the emotional needs of users. As a result, even if a judgment is legally justified, it may be unacceptable to the parties involved. Furthermore, there has been a lack of methods for artificial intelligence with different perspectives to collaborate in discussions and generate more comprehensive judgments.

[0338] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0339] In this invention, the server includes information processing means for storing and updating legal-related data and past case data, sentiment analysis means for analyzing user input information and extracting emotional information, and communication means for integrating the extracted emotional information and input case information and transferring the data. This makes it possible to generate more acceptable draft judgments that take into account the user's emotions in addition to legal justification. Furthermore, multiple generation AI agents with different perspectives discuss with each other and form draft judgments while also considering emotional information, thereby achieving comprehensive and convincing consensus building.

[0340] "Legal data" refers to databases containing information on legal documents, precedents, and laws, providing the foundational information necessary for trials and agreement formation.

[0341] "Past case data" refers to records of past trials and lawsuits, and includes case information that should be used as a reference for precedents.

[0342] "Information processing means" refers to methods and devices for accumulating legal data and past case data and updating them with the latest information.

[0343] "Emotional analysis methods" refer to techniques and devices for extracting and analyzing emotional information from user input and behavior.

[0344] "Communication methods" refer to techniques and devices for integrating analyzed emotional information and incident information, and for sending and receiving data between servers and agents.

[0345] A "generative AI agent" refers to artificial intelligence that performs legal analysis based on input information and forms draft judgments without human intervention.

[0346] "Judgment generation means" refers to methods and devices for generating AI agents to create draft judgments using legal data, past case data, and emotional information.

[0347] "Interface means" refers to methods or devices that present the generated draft judgment to the user and allow the user to receive the result.

[0348] "Emotional information" refers to information that indicates the user's emotional state and psychological reactions, and is a factor that influences the resulting draft judgment.

[0349] One embodiment of this invention is a court support system that integrates legal information and user emotional information to generate a just and acceptable draft judgment.

[0350] The server first stores legal data and historical case data. This data is stored on a database management system and updated regularly. For example, using an SQL database enables rapid and efficient large-scale data processing. Furthermore, the server has a generative AI agent that generates draft judgments based on prompts. This generative AI agent uses a generative AI model based on natural language processing technology.

[0351] The terminal is used by users to input information related to the trial. It implements sentiment analysis software that analyzes emotional information obtained from text input and user actions. The analysis results are sent to the server in real time. For sentiment analysis, natural language processing libraries and APIs, such as those written in Python, are used.

[0352] The user enters details of the trial via their device and receives a draft judgment as a result. The information entered by the user is analyzed by the server, and prompt messages are generated for use by the AI ​​agent. An example of such a prompt message would be, "Considering past court cases and laws regarding noise problems, and given that the emotion engine has reported the user's strong anger, please propose a solution to the problem."

[0353] This system integrates legal and emotional information, making it possible to generate draft judgments that balance legal legitimacy with user acceptance.

[0354] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0355] Step 1:

[0356] The user enters detailed information about the trial via the terminal. This information includes the circumstances of the case and details of the parties involved. This information is entered into the terminal as data necessary for sentiment analysis. The entered data forms the basis for analysis in the next step.

[0357] Step 2:

[0358] The sentiment analysis software embedded in the terminal extracts emotional information from the user's input. Specifically, it analyzes the content and context of the text using natural language processing techniques to determine the user's emotional state (e.g., anger, sadness, joy). The emotional information obtained through this analysis becomes data sent to the server. The input is the user's text information, and the output is the analyzed emotional data.

[0359] Step 3:

[0360] The terminal sends analyzed emotional information and detailed incident information to the server as data. Specifically, the terminal converts this information into JSON format and makes a POST request to the server's API endpoint using a secure communication protocol (e.g., HTTPS). The input is the data obtained within the terminal, and the output is a data packet prepared for transmission to the server.

[0361] Step 4:

[0362] The server generates a prompt based on the emotional and incident information received from the terminal. This prompt is structured as a command statement for the generation AI model to execute. Specifically, it creates a prompt that takes into account the context of the incident and the emotional state, while referring to legal information and past cases. The input is the received user information, and the output is the prompt statement passed to the generation AI model.

[0363] Step 5:

[0364] The generative AI agent generates a draft judgment based on prompts received from the server. Specifically, the AI ​​agent refers to legal databases and past case data to generate the optimal response to the requested prompt in natural language. This process involves data analysis and text generation using a generative AI model. The input is the prompt from the server, and the output is the generated draft judgment document.

[0365] Step 6:

[0366] The server sends the generated draft judgment to the user's terminal. Specifically, it formats the draft judgment into a format that is easy for the user to understand and sends it back to the terminal as a notification. The input is the result generated by the AI ​​agent, and the output is the draft judgment presented to the user.

[0367] Step 7:

[0368] The user receives and reviews a draft judgment sent from the server via their terminal. Specifically, the content of the draft judgment is displayed on the screen, and additional feedback is entered as needed. This feedback may be further sent to the system. The input is the draft judgment from the server, and the output is the user's acknowledgment of receipt and feedback information.

[0369] (Application Example 2)

[0370] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0371] When individuals seek legal advice within their families, there is a challenge in providing a judgment that comprehensively considers both legal issues and emotional factors. To address this challenge, it is necessary to make judgments that take into account not only legal information and past cases, but also the emotional state of the person seeking advice.

[0372] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0373] In this invention, the server includes data management means for accumulating and updating legal information and past case information; discussion means for multiple information processing devices to exchange information with each other and make decisions; operation means for analyzing the user's emotional state and receiving a draft judgment generated by the information processing device; and judgment formulation means for the information processing device to generate a judgment based on legal information and past case information, taking into account the user's emotional state. This makes it possible to provide legally appropriate information while taking into consideration the user's emotions when they face legal problems in their home.

[0374] "Legal information" is a term that refers to all legally relevant information, such as laws, regulations, and precedents.

[0375] "Past case information" refers to data that includes details of past legal cases and judgments.

[0376] "Data management means" refers to the mechanisms and technologies for collecting, organizing, and updating information.

[0377] An "information processing device" refers to computing resources and systems used to process data and output results.

[0378] "Discussion methods" refer to methods by which information processing devices share information with each other, exchange opinions, and make decisions.

[0379] "Emotional state" is a concept that describes the psychological or emotional response that a user exhibits in a particular situation.

[0380] "Operating means" refers to the interface used by the user to input information and receive output from the system.

[0381] "Methods for formulating judgments" refer to systems that use legal information and past case information to formulate appropriate judgments based on specific criteria.

[0382] This invention is a system that uses an information processing device installed in the home to provide useful information to users when they seek legal advice. The information processing device collects and updates legal information and past case information, and analyzes the user's emotional state. The server stores the legal information and past case information using data management means and maintains it in an up-to-date state. In addition, multiple information processing devices exchange information with each other to generate draft judgments that take into account the user's emotional state.

[0383] Specifically, when a user conducts legal consultation via a voice input device, sentiment analysis software on the terminal analyzes the user's voice and evaluates their emotional state. For example, the voice data is converted to text using the Google Cloud Speech-to-Text API, and that text is then analyzed using natural language processing technology. This sentiment information is transmitted to a server in real time and used in the process of formulating judgments.

[0384] The AI ​​model uses the BERT model from the Transformers library to retrieve relevant information from a legal database and generate a draft judgment. This information is managed using data management software such as MySQL. The system then presents this draft judgment to the user, providing a satisfactory answer.

[0385] As a concrete example, if a dispute arises within the family regarding a contract between friends, the sentiment analysis software analyzes the user's anxiety, and the server uses laws and past precedents to provide legally appropriate and emotionally sensitive advice. An example of a prompt used for the generative AI model would be:

[0386] "The user is showing signs of high stress. Please provide empathetic advice based on legal information regarding the contract and past cases."

[0387] This system can provide support that includes emotional care while addressing legal issues faced within the family.

[0388] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0389] Step 1:

[0390] Users seek legal advice through a voice input device. During this process, users describe their legal questions and situations verbally. The input data is recorded as audio.

[0391] Step 2:

[0392] The device uses the Google Cloud Speech-to-Text API to convert audio data into text data. This process analyzes the audio signal and converts it into a string based on a language model. The output is the audio converted into text data.

[0393] Step 3:

[0394] The terminal inputs the converted text data into sentiment analysis software to evaluate the user's emotional state. Here, natural language processing techniques are used to analyze the emotional characteristics of the language and identify the emotional state the user is exhibiting, such as "stress" or "anxiety." The output is the analyzed emotional information.

[0395] Step 4:

[0396] The terminal sends the analyzed emotion information to the server. The server receives the emotion information and prepares it to be used as a parameter for other data processing.

[0397] Step 5:

[0398] The server retrieves legal data and past case information from a MySQL database. The input here consists of keywords and topics related to legal consultations. The server uses this information to extract relevant legal data.

[0399] Step 6:

[0400] The server generates prompts for a generative AI model (e.g., Transformers' BERT model) based on acquired legal and emotional information. These prompts include context corresponding to the user's emotional state. By inputting these prompts into the generative AI model, a draft judgment is generated that considers both legal information and emotional considerations.

[0401] Step 7:

[0402] The server sends the generated draft judgment to the terminal. The terminal presents this information to the user and can also provide supplementary information according to the user's needs. The output is emotionally sensitive legal advice.

[0403] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0404] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0405] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0406] [Third Embodiment]

[0407] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0408] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0409] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0410] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0411] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0412] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0413] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0414] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0415] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0416] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0417] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0418] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0419] This invention is a court support system that includes an information management means for accumulating legal information and past case information, a discussion means using multiple artificial intelligence agents, and an interface means for inputting case information and presenting draft judgments.

[0420] The server regularly updates legal information and past case data, functioning as a database that can be used by the AI ​​agent. This ensures that the AI ​​agent always has access to the latest information.

[0421] The terminal serves as an interface for users to input detailed information about the case, providing all the necessary information for the system. This includes the background of the case, relevant evidence, and information about those involved.

[0422] When a user inputs incident information through their device, the server uses that information to create a prompt for the AI ​​agent to begin a discussion. The AI ​​agent then conducts the discussion, forming opinions based on the law and past cases, and seeking a consensus.

[0423] The server monitors communication between AI agents and integrates the final draft judgment through a judgment generation mechanism. This process aims to produce a reasonable judgment based on solid legal grounds and past precedents.

[0424] As a concrete example, in a traffic accident trial, when a user inputs details of the accident and relevant laws into a terminal, the server provides this information to an AI agent, which then conducts a discussion based on appropriate past cases. Once the AI ​​agent's discussion concludes, the server generates a judgment and presents it to the user. This process allows for an unbiased, fair, and objective judgment.

[0425] The following describes the processing flow.

[0426] Step 1:

[0427] The server collects legal information and past case data into an information database and updates it regularly. This ensures that the AI ​​agent always has access to the latest information.

[0428] Step 2:

[0429] The user uses a device to enter case information related to the trial. This information should include details of the case, relevant evidence, and information about the people involved.

[0430] Step 3:

[0431] The terminal inputs case information, which is then sent to the server. The server uses this information to generate prompts for the AI ​​agent. The generated prompts should include background information on the case and legal issues.

[0432] Step 4:

[0433] The server delivers prompts to the AI ​​agents, and each agent begins a discussion based on them. The AI ​​agents form opinions by referring to legal information and past case data, and analyze the case from multiple perspectives.

[0434] Step 5:

[0435] The server monitors discussions between AI agents and facilitates agreement among them. It also supports information exchange and coordination between agents as needed.

[0436] Step 6:

[0437] After consensus is reached, the server aggregates the opinions of the AI ​​agents through a judgment generation mechanism and automatically generates a draft judgment. This draft judgment clearly outlines the legal basis.

[0438] Step 7:

[0439] The generated draft judgment is presented to the user. The user can review the judgment and, if necessary, provide additional questions or feedback from their device.

[0440] (Example 1)

[0441] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0442] When making legal decisions, it is difficult to make objective and fair decisions while always referring to the latest and most relevant legal knowledge and past case knowledge. Furthermore, there is the challenge of forming rational judgments that take into account discussions from different perspectives.

[0443] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0444] In this invention, the server includes an information management means for accumulating and updating legal knowledge and past case knowledge, a discussion means for multiple artificial intelligence components to communicate with each other and make decisions, and an interface means for inputting detailed case information and receiving a decision proposal generated by artificial intelligence. This makes it possible to make objective and rational decisions using the latest legal information.

[0445] "Legal knowledge" is a general term for information related to the law, the content of legal provisions, and data concerning their interpretation.

[0446] "Knowledge of past cases" refers to information about the details of past cases and judgments based on the law.

[0447] "Information management tools" are systems for efficiently collecting, updating, and maintaining legal knowledge and knowledge of past cases.

[0448] "Artificial intelligence components" refer to artificial intelligence models and software used to analyze legal knowledge and past cases, and to conduct discussions and make judgments.

[0449] "Discussion method" refers to the process by which multiple artificial intelligence components exchange opinions and make decisions.

[0450] An "interface means" is a mechanism that allows users to input detailed information about a case and receive a decision proposal generated by artificial intelligence.

[0451] A "decision-generating mechanism" is a process in which artificial intelligence, based on legal knowledge and knowledge of past cases, creates a final, consensus-based decision from detailed information of a given case.

[0452] A "monitoring mechanism" is a system for monitoring communication between artificial intelligence components and integrating rational decisions.

[0453] This court support system is designed to generate rational and objective judgments by comprehensively managing legal knowledge and past case knowledge, while utilizing artificial intelligence components.

[0454] The server collects legal knowledge and past case knowledge from specialized legal databases through information management means and updates it regularly. Specifically, the server retrieves legal information from external data sources via APIs and stores this information in a MySQL database, thereby maintaining a constantly up-to-date knowledge base.

[0455] The device provides a web interface using React, allowing users to intuitively input detailed information about the case. This interface has forms for entering information such as the case background, those involved, and relevant evidence, enabling users to directly submit data to the system.

[0456] When a user enters incident information into the terminal, the server uses a generative AI model to generate a prompt message based on that information. This prompt message includes key points of the incident information and related laws, and is used to initiate a discussion with the AI ​​agent.

[0457] The AI ​​agent analyzes legal knowledge and past case knowledge to engage in discussions from different perspectives. The server monitors this interaction and integrates a rational and unbiased final decision. The user can receive the results through the interface.

[0458] As a concrete example, consider a case where a user inputs details of a traffic accident trial. The server generates a prompt message such as, "Regarding the traffic accident case, it occurred at point A, and the relevant law is Article X of the Traffic Act. Please refer to similar past cases and propose a judgment." Based on this information, the AI ​​agent makes a decision, allowing the server to present an appropriate judgment to the user.

[0459] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0460] Step 1:

[0461] The server collects legal knowledge and past case knowledge from an external database and stores it in a MySQL database. The input consists of JSON data containing legal and case information obtained from an external API. This data is parsed, converted to the required format for the database, and then stored. Specifically, the server executes a scheduled job at 3 AM daily, retrieving data from the external data source and updating the database.

[0462] Step 2:

[0463] The user uses their device to input detailed information about the case. This input includes data such as background information, information about those involved, and evidence. The device uses this information to send it to the server, so a web form using React receives data from the user and transmits it to the server in real time. Specifically, the user fills in the required fields on the web form and confirms the information by pressing the submit button.

[0464] Step 3:

[0465] The server generates prompt messages for the AI ​​agent using a generative AI model based on the incident information received from the user. The input is incident information from the user, and the server creates prompt messages by combining this information with appropriate legal knowledge and case examples. Specifically, the server analyzes the incident information, searches the database for highly relevant laws and past cases, and adds them to the prompt messages.

[0466] Step 4:

[0467] The server sends the generated prompt to the AI ​​agent, initiating a discussion. Here, the AI ​​agent is given legal and case information to analyze as input, and an interim opinion based on agreement between the AI ​​agents is formed as output. Specifically, the AI ​​agents individually consider the prompt and exchange opinions through communication.

[0468] Step 5:

[0469] The server integrates the discussion results from the AI ​​agents and generates a final decision. The input consists of various perspectives provided by the AI ​​agents, which are then aggregated to arrive at a rational judgment. Specifically, the server organizes the discussion results, uses natural language processing to format them into a judgment document, and presents it to the user.

[0470] Step 6:

[0471] The user receives and reviews the final draft judgment via their device. The output includes the judgment document provided by the server, which can be visually reviewed through the interface. Specifically, the user can review the details of the draft judgment via the device's interface and provide feedback as needed.

[0472] (Application Example 1)

[0473] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0474] Responding quickly and appropriately to everyday legal issues and troubles is an important yet often challenging task for individuals and businesses. Current legal consultation services have limited access to experts and are often time-consuming and expensive, making the process of obtaining solutions burdensome. Therefore, there is a need for the development of a system that utilizes legal information and past cases to provide efficient and rapid legal consultations through artificial intelligence.

[0475] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0476] In this invention, the server includes data management means for accumulating and updating legal information and past case information, opinion exchange means for multiple artificial intelligences to interact with each other and make decisions, and user interface means for inputting legal questions and detailed information and receiving solutions generated by artificial intelligence. This enables users to quickly seek legal advice and obtain effective legal advice and solutions.

[0477] "Legal information" refers to general information related to the law, including laws, regulations, precedents, and interpretations and guidelines based thereon.

[0478] "Past case information" refers to data on actual legal cases and precedents, serving as concrete case studies.

[0479] "Data management means" refers to the function of a system that appropriately stores information and makes it available for updating and retrieval as needed.

[0480] "Artificial intelligence" is a technology that uses computer programs to mimic human intellectual abilities and perform data analysis and decision-making.

[0481] "Method of exchanging opinions" refers to the process by which multiple artificial intelligences exchange information with each other and make decisions collaboratively.

[0482] "User interface means" refers to all types of operation screens and input devices that allow users to input information into a system or receive output from a system.

[0483] A "result generation method" is a function in which artificial intelligence analyzes input information and generates solutions or results that should be presented to the user.

[0484] "Individuals" refers to ordinary users who are interested in legal issues and require routine consultation.

[0485] A "system" refers to a collection of components that integrate the aforementioned means and function as a whole.

[0486] The system for implementing this invention is constructed by combining data management means for managing legal information and past case information, user interface means for receiving user input, artificial intelligence-based opinion exchange means, and result generation means.

[0487] The server stores legal information and past case data, and develops artificial intelligence learning models using programming languages ​​and libraries such as Python and TensorFlow. This allows the server to always perform data analysis based on the latest legal information. MySQL and PostgreSQL are used for database management.

[0488] The terminal serves as the user interface, providing a screen for users to input legal questions or details of problems. This interface is built using a web framework such as Django or Flask, providing users with an intuitive user experience.

[0489] When a user inputs details of a case or legal issue, the server generates this as a prompt, triggering the AI ​​agent to form an opinion. The AI ​​agent is composed of multiple models with different perspectives, and each agent exchanges opinions while conducting its own analysis to seek the optimal legal solution.

[0490] For example, if a user submits a request for advice regarding a traffic accident, the server will have an AI agent refer to relevant laws and past precedents, and based on the analysis results, it will provide the user with appropriate advice and an estimate of the settlement amount. In this way, users can quickly seek legal advice and obtain effective legal guidance.

[0491] As a concrete example, the following prompt may be generated:

[0492] "AI agent, regarding insurance claim negotiations in a traffic accident, please provide the best possible suggestions based on the details entered by user X, referencing past precedents."

[0493] This system creates an environment where legal consultations can be easily accessed, allowing users to deal with legal issues with peace of mind.

[0494] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0495] Step 1:

[0496] The terminal accepts legal questions and details of cases from the user as input. The user enters information about the legal issue in text format, and the terminal prepares to send that data to the server.

[0497] Step 2:

[0498] The server receives user input data sent from the terminal. The server analyzes this input data using natural language processing techniques to extract relevant keywords and legal categories. This analysis process identifies the scope of legal information and past case information that needs to be addressed.

[0499] Step 3:

[0500] The server provides the AI ​​agent with prompt sentences generated based on the analysis. These prompt sentences include instructions to refer to legal information and past case information, aligned with the extracted keywords and legal categories. The AI ​​agent then initiates an exchange of opinions among multiple artificial intelligence models.

[0501] Step 4:

[0502] The AI ​​agent references legal information and past case data from multiple perspectives, engaging in discussions while retrieving relevant information from the database. Utilizing a deep learning model based on TensorFlow, the AI ​​agent attempts to reach a consensus to determine the optimal legal solution based on the discussion.

[0503] Step 5:

[0504] The server receives the results of the discussion from the AI ​​agent and generates final solutions and advice. This output data, including specific advice and legally recommended actions, is ready to be presented to the user.

[0505] Step 6:

[0506] The terminal displays solutions received from the server to the user. Based on the advice and solutions presented, the user can decide what action to take next. In this way, the terminal supports the user in quickly addressing legal issues.

[0507] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0508] The system according to the present invention integrates legal information, past case information, and user sentiment information to support court judgments. Specifically, the server maintains a legal database and a case database, ensuring that the AI ​​agent always has access to the latest information.

[0509] The user inputs case information related to the trial via a terminal. The terminal incorporates an emotion engine that recognizes the user's emotions based on their input and responses. This emotion information is analyzed in real time and sent to a server, which then provides it to an AI agent.

[0510] The server also generates specific prompts based on case information and delivers them to the AI ​​agent. The AI ​​agent develops arguments based on legal information and past cases to form a draft judgment. In this process, user sentiment information is also analyzed and considered as a factor influencing the draft judgment. The sentiment engine's data aims to ensure that the judgment is not only legally just but also acceptable to the user.

[0511] For example, if a user expresses strong emotions while entering details of a case on their device, the emotion engine analyzes this information and reports it to the server. The AI ​​agent takes this emotional data into account and generates a draft judgment that maintains legal validity while also considering the emotional aspects. The draft judgment presented to the user thus achieves a balance between legal accuracy and emotional understanding.

[0512] The following describes the processing flow.

[0513] Step 1:

[0514] The user uses a device to input case information related to the trial, while an emotion engine built into the device monitors the user's input and responses. The emotion engine analyzes the user's emotions from facial expressions, voice tone, and nuances in the text, and generates emotion information in real time.

[0515] Step 2:

[0516] The terminal sends user emotion information generated by the emotion engine, along with incident information entered by the user, to the server. The server receives this information and organizes the details and related information of the incident.

[0517] Step 3:

[0518] Based on the incident information received by the server, prompts are generated to be provided to the AI ​​agent. These prompts include background information on the incident, the issues at issue, and information about the user's feelings.

[0519] Step 4:

[0520] The server generates prompts which are then delivered to the AI ​​agents, who begin discussions based on these prompts. Each agent forms an opinion and analyzes the case from multiple perspectives, referring to legal information, past cases, and sentiment information.

[0521] Step 5:

[0522] The server monitors the progress of discussions among AI agents, ensuring that all data, including emotional information, is appropriately considered and adjusted to reach a consensus. This facilitates the generation of draft judgments that balance legal validity with emotional aspects.

[0523] Step 6:

[0524] After an agreement is reached, the server generates a final draft judgment through a judgment generation system. This draft judgment is based on legal grounds but also reflects the user's emotional understanding.

[0525] Step 7:

[0526] The server presents the user with a draft judgment it has generated, which the user then reviews. If necessary, the user can provide additional questions or feedback on the judgment from their device.

[0527] (Example 2)

[0528] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0529] Conventional court support systems primarily generate judgments based on legal information and past case data, but they have limitations in reaching consensus that reflects the emotional needs of users. As a result, even if a judgment is legally justified, it may be unacceptable to the parties involved. Furthermore, there has been a lack of methods for artificial intelligence with different perspectives to collaborate in discussions and generate more comprehensive judgments.

[0530] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0531] In this invention, the server includes information processing means for storing and updating legal-related data and past case data, sentiment analysis means for analyzing user input information and extracting emotional information, and communication means for integrating the extracted emotional information and input case information and transferring the data. This makes it possible to generate more acceptable draft judgments that take into account the user's emotions in addition to legal justification. Furthermore, multiple generation AI agents with different perspectives discuss with each other and form draft judgments while also considering emotional information, thereby achieving comprehensive and convincing consensus building.

[0532] "Legal data" refers to databases containing information on legal documents, precedents, and laws, providing the foundational information necessary for trials and agreement formation.

[0533] "Past case data" refers to records of past trials and lawsuits, and includes case information that should be used as a reference for precedents.

[0534] "Information processing means" refers to methods and devices for accumulating legal data and past case data and updating them with the latest information.

[0535] "Emotional analysis methods" refer to techniques and devices for extracting and analyzing emotional information from user input and behavior.

[0536] "Communication methods" refer to techniques and devices for integrating analyzed emotional information and incident information, and for sending and receiving data between servers and agents.

[0537] A "generative AI agent" refers to artificial intelligence that performs legal analysis based on input information and forms draft judgments without human intervention.

[0538] "Judgment generation means" refers to methods and devices for generating AI agents to create draft judgments using legal data, past case data, and emotional information.

[0539] "Interface means" refers to methods or devices that present the generated draft judgment to the user and allow the user to receive the result.

[0540] "Emotional information" refers to information that indicates the user's emotional state and psychological reactions, and is a factor that influences the resulting draft judgment.

[0541] One embodiment of this invention is a court support system that integrates legal information and user emotional information to generate a just and acceptable draft judgment.

[0542] The server first stores legal data and historical case data. This data is stored on a database management system and updated regularly. For example, using an SQL database enables rapid and efficient large-scale data processing. Furthermore, the server has a generative AI agent that generates draft judgments based on prompts. This generative AI agent uses a generative AI model based on natural language processing technology.

[0543] The terminal is used by users to input information related to the trial. It implements sentiment analysis software that analyzes emotional information obtained from text input and user actions. The analysis results are sent to the server in real time. For sentiment analysis, natural language processing libraries and APIs, such as those written in Python, are used.

[0544] The user enters details of the trial via their device and receives a draft judgment as a result. The information entered by the user is analyzed by the server, and prompt messages are generated for use by the AI ​​agent. An example of such a prompt message would be, "Considering past court cases and laws regarding noise problems, and given that the emotion engine has reported the user's strong anger, please propose a solution to the problem."

[0545] This system integrates legal and emotional information, making it possible to generate draft judgments that balance legal legitimacy with user acceptance.

[0546] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0547] Step 1:

[0548] The user enters detailed information about the trial via the terminal. This information includes the circumstances of the case and details of the parties involved. This information is entered into the terminal as data necessary for sentiment analysis. The entered data forms the basis for analysis in the next step.

[0549] Step 2:

[0550] The sentiment analysis software embedded in the terminal extracts emotional information from the user's input. Specifically, it analyzes the content and context of the text using natural language processing techniques to determine the user's emotional state (e.g., anger, sadness, joy). The emotional information obtained through this analysis becomes data sent to the server. The input is the user's text information, and the output is the analyzed emotional data.

[0551] Step 3:

[0552] The terminal sends analyzed emotional information and detailed incident information to the server as data. Specifically, the terminal converts this information into JSON format and makes a POST request to the server's API endpoint using a secure communication protocol (e.g., HTTPS). The input is the data obtained within the terminal, and the output is a data packet prepared for transmission to the server.

[0553] Step 4:

[0554] The server generates a prompt based on the emotional and incident information received from the terminal. This prompt is structured as a command statement for the generation AI model to execute. Specifically, it creates a prompt that takes into account the context of the incident and the emotional state, while referring to legal information and past cases. The input is the received user information, and the output is the prompt statement passed to the generation AI model.

[0555] Step 5:

[0556] The generative AI agent generates a draft judgment based on prompts received from the server. Specifically, the AI ​​agent refers to legal databases and past case data to generate the optimal response to the requested prompt in natural language. This process involves data analysis and text generation using a generative AI model. The input is the prompt from the server, and the output is the generated draft judgment document.

[0557] Step 6:

[0558] The server sends the generated draft judgment to the user's terminal. Specifically, it formats the draft judgment into a format that is easy for the user to understand and sends it back to the terminal as a notification. The input is the result generated by the AI ​​agent, and the output is the draft judgment presented to the user.

[0559] Step 7:

[0560] The user receives and reviews a draft judgment sent from the server via their terminal. Specifically, the content of the draft judgment is displayed on the screen, and additional feedback is entered as needed. This feedback may be further sent to the system. The input is the draft judgment from the server, and the output is the user's acknowledgment of receipt and feedback information.

[0561] (Application Example 2)

[0562] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0563] When individuals seek legal advice within their families, there is a challenge in providing a judgment that comprehensively considers both legal issues and emotional factors. To address this challenge, it is necessary to make judgments that take into account not only legal information and past cases, but also the emotional state of the person seeking advice.

[0564] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0565] In this invention, the server includes data management means for accumulating and updating legal information and past case information; discussion means for multiple information processing devices to exchange information with each other and make decisions; operation means for analyzing the user's emotional state and receiving a draft judgment generated by the information processing device; and judgment formulation means for the information processing device to generate a judgment based on legal information and past case information, taking into account the user's emotional state. This makes it possible to provide legally appropriate information while taking into consideration the user's emotions when they face legal problems in their home.

[0566] "Legal information" is a term that refers to all legally relevant information, such as laws, regulations, and precedents.

[0567] "Past case information" refers to data that includes details of past legal cases and judgments.

[0568] "Data management means" refers to the mechanisms and technologies for collecting, organizing, and updating information.

[0569] An "information processing device" refers to computing resources and systems used to process data and output results.

[0570] "Discussion methods" refer to methods by which information processing devices share information with each other, exchange opinions, and make decisions.

[0571] "Emotional state" is a concept that describes the psychological or emotional response that a user exhibits in a particular situation.

[0572] "Operating means" refers to the interface used by the user to input information and receive output from the system.

[0573] "Methods for formulating judgments" refer to systems that use legal information and past case information to formulate appropriate judgments based on specific criteria.

[0574] This invention is a system that uses an information processing device installed in the home to provide useful information to users when they seek legal advice. The information processing device collects and updates legal information and past case information, and analyzes the user's emotional state. The server stores the legal information and past case information using data management means and maintains it in an up-to-date state. In addition, multiple information processing devices exchange information with each other to generate draft judgments that take into account the user's emotional state.

[0575] Specifically, when a user conducts legal consultation via a voice input device, sentiment analysis software on the terminal analyzes the user's voice and evaluates their emotional state. For example, the voice data is converted to text using the Google Cloud Speech-to-Text API, and that text is then analyzed using natural language processing technology. This sentiment information is transmitted to a server in real time and used in the process of formulating judgments.

[0576] The AI ​​model uses the BERT model from the Transformers library to retrieve relevant information from a legal database and generate a draft judgment. This information is managed using data management software such as MySQL. The system then presents this draft judgment to the user, providing a satisfactory answer.

[0577] As a concrete example, if a dispute arises within the family regarding a contract between friends, the sentiment analysis software analyzes the user's anxiety, and the server uses laws and past precedents to provide legally appropriate and emotionally sensitive advice. An example of a prompt used for the generative AI model would be:

[0578] "The user is showing signs of high stress. Please provide empathetic advice based on legal information regarding the contract and past cases."

[0579] This system can provide support that includes emotional care while addressing legal issues faced within the family.

[0580] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0581] Step 1:

[0582] Users seek legal advice through a voice input device. During this process, users describe their legal questions and situations verbally. The input data is recorded as audio.

[0583] Step 2:

[0584] The device uses the Google Cloud Speech-to-Text API to convert audio data into text data. This process analyzes the audio signal and converts it into a string based on a language model. The output is the audio converted into text data.

[0585] Step 3:

[0586] The terminal inputs the converted text data into sentiment analysis software to evaluate the user's emotional state. Here, natural language processing techniques are used to analyze the emotional characteristics of the language and identify the emotional state the user is exhibiting, such as "stress" or "anxiety." The output is the analyzed emotional information.

[0587] Step 4:

[0588] The terminal sends the analyzed emotion information to the server. The server receives the emotion information and prepares it to be used as a parameter for other data processing.

[0589] Step 5:

[0590] The server retrieves legal data and past case information from a MySQL database. The input here consists of keywords and topics related to legal consultations. The server uses this information to extract relevant legal data.

[0591] Step 6:

[0592] The server generates prompts for a generative AI model (e.g., Transformers' BERT model) based on acquired legal and emotional information. These prompts include context corresponding to the user's emotional state. By inputting these prompts into the generative AI model, a draft judgment is generated that considers both legal information and emotional considerations.

[0593] Step 7:

[0594] The server sends the generated draft judgment to the terminal. The terminal presents this information to the user and can also provide supplementary information according to the user's needs. The output is emotionally sensitive legal advice.

[0595] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0596] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0597] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0598] [Fourth Embodiment]

[0599] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0600] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0601] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0602] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0603] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0604] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0605] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0606] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0607] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0608] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0609] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0610] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0611] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0612] This invention is a court support system that includes an information management means for accumulating legal information and past case information, a discussion means using multiple artificial intelligence agents, and an interface means for inputting case information and presenting draft judgments.

[0613] The server regularly updates legal information and past case data, functioning as a database that can be used by the AI ​​agent. This ensures that the AI ​​agent always has access to the latest information.

[0614] The terminal serves as an interface for users to input detailed information about the case, providing all the necessary information for the system. This includes the background of the case, relevant evidence, and information about those involved.

[0615] When a user inputs incident information through their device, the server uses that information to create a prompt for the AI ​​agent to begin a discussion. The AI ​​agent then conducts the discussion, forming opinions based on the law and past cases, and seeking a consensus.

[0616] The server monitors communication between AI agents and integrates the final draft judgment through a judgment generation mechanism. This process aims to produce a reasonable judgment based on solid legal grounds and past precedents.

[0617] As a concrete example, in a traffic accident trial, when a user inputs details of the accident and relevant laws into a terminal, the server provides this information to an AI agent, which then conducts a discussion based on appropriate past cases. Once the AI ​​agent's discussion concludes, the server generates a judgment and presents it to the user. This process allows for an unbiased, fair, and objective judgment.

[0618] The following describes the processing flow.

[0619] Step 1:

[0620] The server collects legal information and past case data into an information database and updates it regularly. This ensures that the AI ​​agent always has access to the latest information.

[0621] Step 2:

[0622] The user uses a device to enter case information related to the trial. This information should include details of the case, relevant evidence, and information about the people involved.

[0623] Step 3:

[0624] The terminal inputs case information, which is then sent to the server. The server uses this information to generate prompts for the AI ​​agent. The generated prompts should include background information on the case and legal issues.

[0625] Step 4:

[0626] The server delivers prompts to the AI ​​agents, and each agent begins a discussion based on them. The AI ​​agents form opinions by referring to legal information and past case data, and analyze the case from multiple perspectives.

[0627] Step 5:

[0628] The server monitors discussions between AI agents and facilitates agreement among them. It also supports information exchange and coordination between agents as needed.

[0629] Step 6:

[0630] After consensus is reached, the server aggregates the opinions of the AI ​​agents through a judgment generation mechanism and automatically generates a draft judgment. This draft judgment clearly outlines the legal basis.

[0631] Step 7:

[0632] The generated draft judgment is presented to the user. The user can review the judgment and, if necessary, provide additional questions or feedback from their device.

[0633] (Example 1)

[0634] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0635] When making legal decisions, it is difficult to make objective and fair decisions while always referring to the latest and most relevant legal knowledge and past case knowledge. Furthermore, there is the challenge of forming rational judgments that take into account discussions from different perspectives.

[0636] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0637] In this invention, the server includes an information management means for accumulating and updating legal knowledge and past case knowledge, a discussion means for multiple artificial intelligence components to communicate with each other and make decisions, and an interface means for inputting detailed case information and receiving a decision proposal generated by artificial intelligence. This makes it possible to make objective and rational decisions using the latest legal information.

[0638] "Legal knowledge" is a general term for information related to the law, the content of legal provisions, and data concerning their interpretation.

[0639] "Knowledge of past cases" refers to information about the details of past cases and judgments based on the law.

[0640] "Information management tools" are systems for efficiently collecting, updating, and maintaining legal knowledge and knowledge of past cases.

[0641] "Artificial intelligence components" refer to artificial intelligence models and software used to analyze legal knowledge and past cases, and to conduct discussions and make judgments.

[0642] "Discussion method" refers to the process by which multiple artificial intelligence components exchange opinions and make decisions.

[0643] An "interface means" is a mechanism that allows users to input detailed information about a case and receive a decision proposal generated by artificial intelligence.

[0644] A "decision-generating mechanism" is a process in which artificial intelligence, based on legal knowledge and knowledge of past cases, creates a final, consensus-based decision from detailed information of a given case.

[0645] A "monitoring mechanism" is a system for monitoring communication between artificial intelligence components and integrating rational decisions.

[0646] This court support system is designed to generate rational and objective judgments by comprehensively managing legal knowledge and past case knowledge, while utilizing artificial intelligence components.

[0647] The server collects legal knowledge and past case knowledge from specialized legal databases through information management means and updates it regularly. Specifically, the server retrieves legal information from external data sources via APIs and stores this information in a MySQL database, thereby maintaining a constantly up-to-date knowledge base.

[0648] The device provides a web interface using React, allowing users to intuitively input detailed information about the case. This interface has forms for entering information such as the case background, those involved, and relevant evidence, enabling users to directly submit data to the system.

[0649] When a user enters incident information into the terminal, the server uses a generative AI model to generate a prompt message based on that information. This prompt message includes key points of the incident information and related laws, and is used to initiate a discussion with the AI ​​agent.

[0650] The AI ​​agent analyzes legal knowledge and past case knowledge to engage in discussions from different perspectives. The server monitors this interaction and integrates a rational and unbiased final decision. The user can receive the results through the interface.

[0651] As a concrete example, consider a case where a user inputs details of a traffic accident trial. The server generates a prompt message such as, "Regarding the traffic accident case, it occurred at point A, and the relevant law is Article X of the Traffic Act. Please refer to similar past cases and propose a judgment." Based on this information, the AI ​​agent makes a decision, allowing the server to present an appropriate judgment to the user.

[0652] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0653] Step 1:

[0654] The server collects legal knowledge and past case knowledge from an external database and stores it in a MySQL database. The input consists of JSON data containing legal and case information obtained from an external API. This data is parsed, converted to the required format for the database, and then stored. Specifically, the server executes a scheduled job at 3 AM daily, retrieving data from the external data source and updating the database.

[0655] Step 2:

[0656] The user uses their device to input detailed information about the case. This input includes data such as background information, information about those involved, and evidence. The device uses this information to send it to the server, so a web form using React receives data from the user and transmits it to the server in real time. Specifically, the user fills in the required fields on the web form and confirms the information by pressing the submit button.

[0657] Step 3:

[0658] The server generates prompt messages for the AI ​​agent using a generative AI model based on the incident information received from the user. The input is incident information from the user, and the server creates prompt messages by combining this information with appropriate legal knowledge and case examples. Specifically, the server analyzes the incident information, searches the database for highly relevant laws and past cases, and adds them to the prompt messages.

[0659] Step 4:

[0660] The server sends the generated prompt to the AI ​​agent, initiating a discussion. Here, the AI ​​agent is given legal and case information to analyze as input, and an interim opinion based on agreement between the AI ​​agents is formed as output. Specifically, the AI ​​agents individually consider the prompt and exchange opinions through communication.

[0661] Step 5:

[0662] The server integrates the discussion results from the AI ​​agents and generates a final decision. The input consists of various perspectives provided by the AI ​​agents, which are then aggregated to arrive at a rational judgment. Specifically, the server organizes the discussion results, uses natural language processing to format them into a judgment document, and presents it to the user.

[0663] Step 6:

[0664] The user receives and reviews the final draft judgment via their device. The output includes the judgment document provided by the server, which can be visually reviewed through the interface. Specifically, the user can review the details of the draft judgment via the device's interface and provide feedback as needed.

[0665] (Application Example 1)

[0666] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0667] Responding quickly and appropriately to everyday legal issues and troubles is an important yet often challenging task for individuals and businesses. Current legal consultation services have limited access to experts and are often time-consuming and expensive, making the process of obtaining solutions burdensome. Therefore, there is a need for the development of a system that utilizes legal information and past cases to provide efficient and rapid legal consultations through artificial intelligence.

[0668] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0669] In this invention, the server includes data management means for accumulating and updating legal information and past case information, opinion exchange means for multiple artificial intelligences to interact with each other and make decisions, and user interface means for inputting legal questions and detailed information and receiving solutions generated by artificial intelligence. This enables users to quickly seek legal advice and obtain effective legal advice and solutions.

[0670] "Legal information" refers to general information related to the law, including laws, regulations, precedents, and interpretations and guidelines based thereon.

[0671] "Past case information" refers to data on actual legal cases and precedents, serving as concrete case studies.

[0672] "Data management means" refers to the function of a system that appropriately stores information and makes it available for updating and retrieval as needed.

[0673] "Artificial intelligence" is a technology that uses computer programs to mimic human intellectual abilities and perform data analysis and decision-making.

[0674] "Method of exchanging opinions" refers to the process by which multiple artificial intelligences exchange information with each other and make decisions collaboratively.

[0675] "User interface means" refers to all types of operation screens and input devices that allow users to input information into a system or receive output from a system.

[0676] A "result generation method" is a function in which artificial intelligence analyzes input information and generates solutions or results that should be presented to the user.

[0677] "Individuals" refers to ordinary users who are interested in legal issues and require routine consultation.

[0678] A "system" refers to a collection of components that integrate the aforementioned means and function as a whole.

[0679] The system for implementing this invention is constructed by combining data management means for managing legal information and past case information, user interface means for receiving user input, artificial intelligence-based opinion exchange means, and result generation means.

[0680] The server stores legal information and past case data, and develops artificial intelligence learning models using programming languages ​​and libraries such as Python and TensorFlow. This allows the server to always perform data analysis based on the latest legal information. MySQL and PostgreSQL are used for database management.

[0681] The terminal serves as the user interface, providing a screen for users to input legal questions or details of problems. This interface is built using a web framework such as Django or Flask, providing users with an intuitive user experience.

[0682] When a user inputs details of a case or legal issue, the server generates this as a prompt, triggering the AI ​​agent to form an opinion. The AI ​​agent is composed of multiple models with different perspectives, and each agent exchanges opinions while conducting its own analysis to seek the optimal legal solution.

[0683] For example, if a user submits a request for advice regarding a traffic accident, the server will have an AI agent refer to relevant laws and past precedents, and based on the analysis results, it will provide the user with appropriate advice and an estimate of the settlement amount. In this way, users can quickly seek legal advice and obtain effective legal guidance.

[0684] As a concrete example, the following prompt may be generated:

[0685] "AI agent, regarding insurance claim negotiations in a traffic accident, please provide the best possible suggestions based on the details entered by user X, referencing past precedents."

[0686] This system creates an environment where legal consultations can be easily accessed, allowing users to deal with legal issues with peace of mind.

[0687] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0688] Step 1:

[0689] The terminal accepts legal questions and details of cases from the user as input. The user enters information about the legal issue in text format, and the terminal prepares to send that data to the server.

[0690] Step 2:

[0691] The server receives user input data sent from the terminal. The server analyzes this input data using natural language processing techniques to extract relevant keywords and legal categories. This analysis process identifies the scope of legal information and past case information that needs to be addressed.

[0692] Step 3:

[0693] The server provides the AI ​​agent with prompt sentences generated based on the analysis. These prompt sentences include instructions to refer to legal information and past case information, aligned with the extracted keywords and legal categories. The AI ​​agent then initiates an exchange of opinions among multiple artificial intelligence models.

[0694] Step 4:

[0695] The AI ​​agent references legal information and past case data from multiple perspectives, engaging in discussions while retrieving relevant information from the database. Utilizing a deep learning model based on TensorFlow, the AI ​​agent attempts to reach a consensus to determine the optimal legal solution based on the discussion.

[0696] Step 5:

[0697] The server receives the results of the discussion from the AI ​​agent and generates final solutions and advice. This output data, including specific advice and legally recommended actions, is ready to be presented to the user.

[0698] Step 6:

[0699] The terminal displays solutions received from the server to the user. Based on the advice and solutions presented, the user can decide what action to take next. In this way, the terminal supports the user in quickly addressing legal issues.

[0700] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0701] The system according to the present invention integrates legal information, past case information, and user sentiment information to support court judgments. Specifically, the server maintains a legal database and a case database, ensuring that the AI ​​agent always has access to the latest information.

[0702] The user inputs case information related to the trial via a terminal. The terminal incorporates an emotion engine that recognizes the user's emotions based on their input and responses. This emotion information is analyzed in real time and sent to a server, which then provides it to an AI agent.

[0703] The server also generates specific prompts based on case information and delivers them to the AI ​​agent. The AI ​​agent develops arguments based on legal information and past cases to form a draft judgment. In this process, user sentiment information is also analyzed and considered as a factor influencing the draft judgment. The sentiment engine's data aims to ensure that the judgment is not only legally just but also acceptable to the user.

[0704] For example, if a user expresses strong emotions while entering details of a case on their device, the emotion engine analyzes this information and reports it to the server. The AI ​​agent takes this emotional data into account and generates a draft judgment that maintains legal validity while also considering the emotional aspects. The draft judgment presented to the user thus achieves a balance between legal accuracy and emotional understanding.

[0705] The following describes the processing flow.

[0706] Step 1:

[0707] The user uses a device to input case information related to the trial, while an emotion engine built into the device monitors the user's input and responses. The emotion engine analyzes the user's emotions from facial expressions, voice tone, and nuances in the text, and generates emotion information in real time.

[0708] Step 2:

[0709] The terminal sends user emotion information generated by the emotion engine, along with incident information entered by the user, to the server. The server receives this information and organizes the details and related information of the incident.

[0710] Step 3:

[0711] Based on the incident information received by the server, prompts are generated to be provided to the AI ​​agent. These prompts include background information on the incident, the issues at issue, and information about the user's feelings.

[0712] Step 4:

[0713] The server generates prompts which are then delivered to the AI ​​agents, who begin discussions based on these prompts. Each agent forms an opinion and analyzes the case from multiple perspectives, referring to legal information, past cases, and sentiment information.

[0714] Step 5:

[0715] The server monitors the progress of discussions among AI agents, ensuring that all data, including emotional information, is appropriately considered and adjusted to reach a consensus. This facilitates the generation of draft judgments that balance legal validity with emotional aspects.

[0716] Step 6:

[0717] After an agreement is reached, the server generates a final draft judgment through a judgment generation system. This draft judgment is based on legal grounds but also reflects the user's emotional understanding.

[0718] Step 7:

[0719] The server presents the user with a draft judgment it has generated, which the user then reviews. If necessary, the user can provide additional questions or feedback on the judgment from their device.

[0720] (Example 2)

[0721] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0722] Conventional court support systems primarily generate judgments based on legal information and past case data, but they have limitations in reaching consensus that reflects the emotional needs of users. As a result, even if a judgment is legally justified, it may be unacceptable to the parties involved. Furthermore, there has been a lack of methods for artificial intelligence with different perspectives to collaborate in discussions and generate more comprehensive judgments.

[0723] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0724] In this invention, the server includes information processing means for storing and updating legal-related data and past case data, sentiment analysis means for analyzing user input information and extracting emotional information, and communication means for integrating the extracted emotional information and input case information and transferring the data. This makes it possible to generate more acceptable draft judgments that take into account the user's emotions in addition to legal justification. Furthermore, multiple generation AI agents with different perspectives discuss with each other and form draft judgments while also considering emotional information, thereby achieving comprehensive and convincing consensus building.

[0725] "Legal data" refers to databases containing information on legal documents, precedents, and laws, providing the foundational information necessary for trials and agreement formation.

[0726] "Past case data" refers to records of past trials and lawsuits, and includes case information that should be used as a reference for precedents.

[0727] "Information processing means" refers to methods and devices for accumulating legal data and past case data and updating them with the latest information.

[0728] "Emotional analysis methods" refer to techniques and devices for extracting and analyzing emotional information from user input and behavior.

[0729] "Communication methods" refer to techniques and devices for integrating analyzed emotional information and incident information, and for sending and receiving data between servers and agents.

[0730] A "generative AI agent" refers to artificial intelligence that performs legal analysis based on input information and forms draft judgments without human intervention.

[0731] "Judgment generation means" refers to methods and devices for generating AI agents to create draft judgments using legal data, past case data, and emotional information.

[0732] "Interface means" refers to methods or devices that present the generated draft judgment to the user and allow the user to receive the result.

[0733] "Emotional information" refers to information that indicates the user's emotional state and psychological reactions, and is a factor that influences the resulting draft judgment.

[0734] One embodiment of this invention is a court support system that integrates legal information and user emotional information to generate a just and acceptable draft judgment.

[0735] The server first stores legal data and historical case data. This data is stored on a database management system and updated regularly. For example, using an SQL database enables rapid and efficient large-scale data processing. Furthermore, the server has a generative AI agent that generates draft judgments based on prompts. This generative AI agent uses a generative AI model based on natural language processing technology.

[0736] The terminal is used by users to input information related to the trial. It implements sentiment analysis software that analyzes emotional information obtained from text input and user actions. The analysis results are sent to the server in real time. For sentiment analysis, natural language processing libraries and APIs, such as those written in Python, are used.

[0737] The user enters details of the trial via their device and receives a draft judgment as a result. The information entered by the user is analyzed by the server, and prompt messages are generated for use by the AI ​​agent. An example of such a prompt message would be, "Considering past court cases and laws regarding noise problems, and given that the emotion engine has reported the user's strong anger, please propose a solution to the problem."

[0738] This system integrates legal and emotional information, making it possible to generate draft judgments that balance legal legitimacy with user acceptance.

[0739] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0740] Step 1:

[0741] The user enters detailed information about the trial via the terminal. This information includes the circumstances of the case and details of the parties involved. This information is entered into the terminal as data necessary for sentiment analysis. The entered data forms the basis for analysis in the next step.

[0742] Step 2:

[0743] The sentiment analysis software embedded in the terminal extracts emotional information from the user's input. Specifically, it analyzes the content and context of the text using natural language processing techniques to determine the user's emotional state (e.g., anger, sadness, joy). The emotional information obtained through this analysis becomes data sent to the server. The input is the user's text information, and the output is the analyzed emotional data.

[0744] Step 3:

[0745] The terminal sends analyzed emotional information and detailed incident information to the server as data. Specifically, the terminal converts this information into JSON format and makes a POST request to the server's API endpoint using a secure communication protocol (e.g., HTTPS). The input is the data obtained within the terminal, and the output is a data packet prepared for transmission to the server.

[0746] Step 4:

[0747] The server generates a prompt based on the emotional and incident information received from the terminal. This prompt is structured as a command statement for the generation AI model to execute. Specifically, it creates a prompt that takes into account the context of the incident and the emotional state, while referring to legal information and past cases. The input is the received user information, and the output is the prompt statement passed to the generation AI model.

[0748] Step 5:

[0749] The generative AI agent generates a draft judgment based on prompts received from the server. Specifically, the AI ​​agent refers to legal databases and past case data to generate the optimal response to the requested prompt in natural language. This process involves data analysis and text generation using a generative AI model. The input is the prompt from the server, and the output is the generated draft judgment document.

[0750] Step 6:

[0751] The server sends the generated draft judgment to the user's terminal. Specifically, it formats the draft judgment into a format that is easy for the user to understand and sends it back to the terminal as a notification. The input is the result generated by the AI ​​agent, and the output is the draft judgment presented to the user.

[0752] Step 7:

[0753] The user receives and reviews a draft judgment sent from the server via their terminal. Specifically, the content of the draft judgment is displayed on the screen, and additional feedback is entered as needed. This feedback may be further sent to the system. The input is the draft judgment from the server, and the output is the user's acknowledgment of receipt and feedback information.

[0754] (Application Example 2)

[0755] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0756] When individuals seek legal advice within their families, there is a challenge in providing a judgment that comprehensively considers both legal issues and emotional factors. To address this challenge, it is necessary to make judgments that take into account not only legal information and past cases, but also the emotional state of the person seeking advice.

[0757] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0758] In this invention, the server includes data management means for accumulating and updating legal information and past case information; discussion means for multiple information processing devices to exchange information with each other and make decisions; operation means for analyzing the user's emotional state and receiving a draft judgment generated by the information processing device; and judgment formulation means for the information processing device to generate a judgment based on legal information and past case information, taking into account the user's emotional state. This makes it possible to provide legally appropriate information while taking into consideration the user's emotions when they face legal problems in their home.

[0759] "Legal information" is a term that refers to all legally relevant information, such as laws, regulations, and precedents.

[0760] "Past case information" refers to data that includes details of past legal cases and judgments.

[0761] "Data management means" refers to the mechanisms and technologies for collecting, organizing, and updating information.

[0762] An "information processing device" refers to computing resources and systems used to process data and output results.

[0763] "Discussion methods" refer to methods by which information processing devices share information with each other, exchange opinions, and make decisions.

[0764] "Emotional state" is a concept that describes the psychological or emotional response that a user exhibits in a particular situation.

[0765] "Operating means" refers to the interface used by the user to input information and receive output from the system.

[0766] "Methods for formulating judgments" refer to systems that use legal information and past case information to formulate appropriate judgments based on specific criteria.

[0767] This invention is a system that uses an information processing device installed in the home to provide useful information to users when they seek legal advice. The information processing device collects and updates legal information and past case information, and analyzes the user's emotional state. The server stores the legal information and past case information using data management means and maintains it in an up-to-date state. In addition, multiple information processing devices exchange information with each other to generate draft judgments that take into account the user's emotional state.

[0768] Specifically, when a user conducts legal consultation via a voice input device, sentiment analysis software on the terminal analyzes the user's voice and evaluates their emotional state. For example, the voice data is converted to text using the Google Cloud Speech-to-Text API, and that text is then analyzed using natural language processing technology. This sentiment information is transmitted to a server in real time and used in the process of formulating judgments.

[0769] The AI ​​model uses the BERT model from the Transformers library to retrieve relevant information from a legal database and generate a draft judgment. This information is managed using data management software such as MySQL. The system then presents this draft judgment to the user, providing a satisfactory answer.

[0770] As a concrete example, if a dispute arises within the family regarding a contract between friends, the sentiment analysis software analyzes the user's anxiety, and the server uses laws and past precedents to provide legally appropriate and emotionally sensitive advice. An example of a prompt used for the generative AI model would be:

[0771] "The user is showing signs of high stress. Please provide empathetic advice based on legal information regarding the contract and past cases."

[0772] This system can provide support that includes emotional care while addressing legal issues faced within the family.

[0773] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0774] Step 1:

[0775] Users seek legal advice through a voice input device. During this process, users describe their legal questions and situations verbally. The input data is recorded as audio.

[0776] Step 2:

[0777] The device uses the Google Cloud Speech-to-Text API to convert audio data into text data. This process analyzes the audio signal and converts it into a string based on a language model. The output is the audio converted into text data.

[0778] Step 3:

[0779] The terminal inputs the converted text data into sentiment analysis software to evaluate the user's emotional state. Here, natural language processing techniques are used to analyze the emotional characteristics of the language and identify the emotional state the user is exhibiting, such as "stress" or "anxiety." The output is the analyzed emotional information.

[0780] Step 4:

[0781] The terminal sends the analyzed emotion information to the server. The server receives the emotion information and prepares it to be used as a parameter for other data processing.

[0782] Step 5:

[0783] The server retrieves legal data and past case information from a MySQL database. The input here consists of keywords and topics related to legal consultations. The server uses this information to extract relevant legal data.

[0784] Step 6:

[0785] The server generates prompts for a generative AI model (e.g., Transformers' BERT model) based on acquired legal and emotional information. These prompts include context corresponding to the user's emotional state. By inputting these prompts into the generative AI model, a draft judgment is generated that considers both legal information and emotional considerations.

[0786] Step 7:

[0787] The server sends the generated draft judgment to the terminal. The terminal presents this information to the user and can also provide supplementary information according to the user's needs. The output is emotionally sensitive legal advice.

[0788] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0789] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0790] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0791] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0792] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0793] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0794] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0795] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0796] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0797] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0798] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0799] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0800] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0801] 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.

[0802] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0803] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0804] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0805] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0806] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0807] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0808] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0809] The following is further disclosed regarding the embodiments described above.

[0810] (Claim 1)

[0811] An information management system that stores and updates legal information and past case information,

[0812] A discussion mechanism in which multiple artificial intelligences communicate with each other to make decisions,

[0813] An interface means for inputting detailed information about a case and receiving a draft judgment generated by artificial intelligence,

[0814] A judgment generation means that uses artificial intelligence to process detailed information of a case based on legal information and past case information to generate a judgment based on agreement,

[0815] A court support system including this.

[0816] (Claim 2)

[0817] The judgment generation means is a system according to claim 1, which includes the step of using artificial intelligence to analyze legal and past case information as a prompt to reach a consensus.

[0818] (Claim 3)

[0819] The system according to claim 1, wherein the discussion means includes an opinion formation process by multiple artificial intelligences having different perspectives, and each artificial intelligence provides an opinion from a different perspective by referring to past cases.

[0820] "Example 1"

[0821] (Claim 1)

[0822] A means of managing information that accumulates and updates legal knowledge and knowledge of past cases,

[0823] A discussion mechanism in which multiple artificial intelligence components communicate with each other to make decisions,

[0824] An interface means for inputting detailed case information and receiving a decision proposal generated by artificial intelligence,

[0825] A judgment generation means that uses artificial intelligence to process detailed information of a case and generate a consensus-based judgment based on legal knowledge and knowledge of past cases,

[0826] A monitoring means that monitors communication between artificial intelligence components and integrates rational decisions,

[0827] A system that includes this.

[0828] (Claim 2)

[0829] The system according to claim 1, wherein the decision generation means includes a step in which artificial intelligence uses records as prompts and analyzes legal and past case knowledge to reach a consensus.

[0830] (Claim 3)

[0831] The system according to claim 1, wherein the discussion means includes an opinion formation process by multiple artificial intelligence components having different perspectives, and each artificial intelligence component provides an opinion from a different perspective by referring to past cases.

[0832] "Application Example 1"

[0833] (Claim 1)

[0834] A data management system that accumulates and updates legal information and past case information,

[0835] A means of exchanging opinions in which multiple artificial intelligences interact with each other to make decisions,

[0836] A user interface that allows users to input legal questions and details and receive solutions generated by artificial intelligence.

[0837] A result generation means that uses artificial intelligence to process input details based on legal information and past case information to generate a consensus-based solution,

[0838] A means for individuals to seek everyday legal advice, with artificial intelligence providing solutions and countermeasures.

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, wherein the result generation means includes a step in which artificial intelligence uses legal records as prompts and analyzes legal and past case information to reach a consensus.

[0842] (Claim 3)

[0843] The system according to claim 1, wherein the means for exchanging opinions includes an opinion formation process by multiple artificial intelligences with different perspectives, and each artificial intelligence provides solutions from diverse perspectives by referring to past cases.

[0844] "Example 2 of combining an emotion engine"

[0845] (Claim 1)

[0846] Information processing means for storing and updating legal data and past case data,

[0847] A means of emotional analysis that analyzes user input information and extracts emotional information,

[0848] A communication method for integrating extracted emotional information and input incident information, and transferring the data,

[0849] A judgment generation means in which a generating AI agent integrates legal data, past case data, and emotional information to generate a judgment proposal,

[0850] An interface means for providing the generated decision proposal to the user,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, which includes a process in which a generating AI agent uses case information and emotional information to generate a proposed judgment from legal and past case data.

[0854] (Claim 3)

[0855] The system according to claim 1, comprising the step of having multiple generative AI agents with different perspectives discuss with each other, form a judgment, and generate a decision proposal that includes emotional information.

[0856] "Application example 2 of combining emotional engines"

[0857] (Claim 1)

[0858] A data management system that accumulates and updates legal information and past case information,

[0859] A means of discussion in which multiple information processing devices exchange information with each other to make decisions,

[0860] An operating means that analyzes the emotional state of the user and receives a draft judgment generated by an information processing device,

[0861] A judgment formulation means that generates a judgment based on legal information and past case information, taking into account the emotional state of the user, and an information processing device,

[0862] A system that includes this.

[0863] (Claim 2)

[0864] The judgment formulation means includes the process of an information processing device using legal records as prompts to analyze legal information and past case information to reach a consensus that takes emotional factors into consideration, according to claim 1.

[0865] (Claim 3)

[0866] The system according to claim 1, wherein the discussion means includes an opinion-forming process by multiple information processing devices having different viewpoints, each information processing device providing an opinion from a different perspective by referring to past cases and taking emotional information into consideration. [Explanation of Symbols]

[0867] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An information management system that stores and updates legal information and past case information, A discussion mechanism in which multiple artificial intelligences communicate with each other to make decisions, An interface means for inputting detailed information about a case and receiving a draft judgment generated by artificial intelligence, A judgment generation means that uses artificial intelligence to process detailed information of a case based on legal information and past case information to generate a judgment based on agreement, A court support system including this.

2. The judgment generation means includes the step of using artificial intelligence to analyze legal and past case information to reach a consensus.

3. The system according to claim 1, wherein the discussion means includes an opinion formation process by multiple artificial intelligences having different viewpoints, and each artificial intelligence provides an opinion from a different perspective by referring to past cases.

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