Data structure of information related to programs, methods, information processing devices, and laws.

Abstract

By further reducing the burden of investigation work when making legally-based judgments, it becomes even easier to consider and draw conclusions from a legal perspective based on the investigation results. [Solution] The memory unit holds information on multiple legal content items. The program causes one or more computer processors to generate prompts for each of the multiple legal content items, including instructions to identify legal issues corresponding to the content of said content; to provide the content used for generating the prompts and the prompts generated for said content to a large-scale language model, and to receive the output of the large-scale language model to obtain the results of identifying legal issues for each content item; and to store the identified legal issues in the memory unit in association with the information of the content used for identification.

Description

Technical Field

[0001] The present disclosure relates to a program, a method, an information processing apparatus, and a data structure of information on legal-related content.

Background Art

[0002] Various stakeholders, such as ordinary consumers and businesses, are conducting procedures based on laws. Users in various positions, such as experts like lawyers and employees of business companies, conduct investigations of documents and the like through searches to collect judgment materials in order to make decisions based on legal viewpoints. Furthermore, judgments are made based on legal viewpoints regarding specific events, such as matters to be specified in the terms of use and evaluation of the propriety of customer attraction measures implemented along with the provision of services. Therefore, the investigation that is the premise of the judgment is important.

[0003] Patent Document 1 points out regarding document search that "document corpora including legal documents, patent documents, medical journals, etc. are searched using query expressions. ··· In many cases, when a user investigates a specific topic, the user can create a plurality of search queries. However, it may be difficult for the user to efficiently determine which search query will yield the most relevant search results and how completely that search query searches a specific topic. Therefore, many users may not trust their own document corpus search and may believe that the generated search results are of low reliability or that the generated search results are not sufficiently complete."

[0004] Patent Document 1 focuses on presenting search results as described above and poses as an issue that "another method is needed to graphically display electronic document search to improve the electronic document search experience."

[0005] Patent Document 1 describes "generating a Venn diagram including a first circle representing a first set of documents and a second circle representing a second set of documents, and displaying it on a graphic display device," "the first circle overlaps the second circle in an overlapping area representing common electronic documents present in the first set of documents and the second set of documents," "the sizes of the first circle and the second circle reflect the number of electronic documents in the first set of documents and the second set of documents, respectively," "the first circle overlaps the second circle in an overlapping area representing common electronic documents present in the first set of documents and the second set of documents," and "in response to user input, indicating the separation of the first circle and the second circle on a graphic display device, and generating a first visualization chart from the first circle and a second visualization chart from the second circle." [Prior art documents] [Patent Documents]

[0006] [Patent Document 1] Japanese Patent Publication No. 2017-010580 [Overview of the project] [Problems that the invention aims to solve]

[0007] On the other hand, when conducting research such as searches to examine specific cases and reach conclusions in order to make decisions from a legal perspective, as described above, searching by keyword will result in a list of content corresponding to that keyword. If the person conducting the research is unfamiliar with the area, it is difficult to gauge the importance of each piece of content listed in this way, such as legal books and web articles. This necessitates viewing each book individually to confirm the details of each piece of content, which places a heavy burden on the research work.

[0008] Therefore, in making legally-based judgments, there is a need for technology that can further reduce the burden of investigation work, thereby making it easier to consider and draw conclusions from a legal perspective based on the investigation results. [Means for solving the problem]

[0009] According to one embodiment shown in this disclosure, a program is provided for operating a computer having one or more computer processors. The program stores information on multiple legal content items in a memory unit. The program causes one or more computer processors to perform the following steps: generate prompts for each of the multiple legal content items, including instructions to identify legal issues corresponding to the content of said content; provide the content for which the prompts were generated and the prompts generated for said content to a large language model, and receive the output of the large language model to obtain the result of identifying legal issues for each of the content items; and store the identified legal issues in the memory unit in association with the information of the content for which they were identified. [Effects of the Invention]

[0010] According to this disclosure, it is possible to construct a database in which reference relationships are defined for parts of multiple legally related content of the same kind. This allows users to search for multiple legally related content based on the database with defined reference relationships, depending on the investigation operation, in order to make legally-based decisions. This streamlines the search process until search results are obtained, thereby further improving the efficiency of the investigation. Consequently, it becomes easier for users to consider and draw conclusions from a legal perspective based on the investigation results. [Brief explanation of the drawing]

[0011] [Figure 1]Figure 1 shows the configuration of System 1. [Figure 2] Figure 2 shows the configuration of server 20. [Figure 3] Figure 3 shows the configuration of terminal 10. [Figure 4] Figure 4 shows the data structure of the user database 211. [Figure 5] Figure 5 shows the data structure of the legal content database 212. [Figure 6] Figure 6 shows the data structure of the content usage history database 213. [Figure 7] Figure 7 shows the data structure of graph-type data structure 214. [Figure 8] Figure 8 shows the process flow for creating a graph database using information from multiple legal content sources. [Figure 9] Figure 9 shows the processing flow using a graph database related to laws. [Figure 10] Figure 10 is an example of an operation screen that displays the results of generating answers to user questions while referring to a graph-type data structure 214 related to the law. [Figure 11] Figure 11 shows the process flow for identifying the relevant laws and regulations in response to the user's question and presenting those laws and regulations to the user. [Figure 12] Figure 12 shows an example of an operation screen that presents the user with the relevant laws and regulations identified in response to the user's question. [Figure 13] Figure 13 illustrates the process flow for generating a database that identifies legal issues from information on multiple legally related content. [Figure 14] Figure 14 shows the process flow for searching for content related to legal issues that address a user's question. [Figure 15] Figure 15 shows an example of an operation screen that responds to a user's question by searching various databases based on the corresponding legal issues.

Best Mode for Carrying Out the Invention

[0012] Hereinafter, embodiments of the present disclosure will be described while referring to the drawings. In the following description, the same parts are denoted by the same reference numerals. Their names and functions are also the same. Therefore, detailed descriptions thereof will not be repeated.

[0013] <Outline of Embodiment> In the first embodiment, regarding legal content, a method for generating a data structure that defines a reference relationship between these contents based on the text (names of laws and regulations, etc.) included in laws, judicial precedents, legal books, and legal guidelines, and a method for generating an answer to a user's question using the graph-type data structure generated thereby will be described.

[0014] <1.1 Overall System Configuration Diagram> FIG. 1 is a diagram showing the configuration of system 1.

[0015] System 1 shown in FIG. 1 includes server 20, user terminal 10, server 91 for case search service, server 92 for law search service, server 93 for book browsing service, server 94 for information media service, server 95 for artificial intelligence (large language model) service (hereinafter, may also be referred to as "server 95 for large language model service"), server 96 of a business operator, server 97 of an SNS, and user terminal 10A. These devices are communicatively connected via network 80.

[0016] In the illustrated example, terminals such as terminal 10 and terminal 10A are shown as the terminals used by the users of the services provided by server 20, but each user is operating a terminal.

[0017] In this embodiment, each device (terminal device, server, etc.) can also be considered as an information processing device. That is, the collection of each device can be considered as a single "information processing device," and System 1 may be formed as a collection of multiple devices. The way in which the multiple functions required to realize System 1 according to this embodiment are distributed to one or more hardware can be appropriately determined in view of the processing capacity of each hardware and / or the specifications required for System 1.

[0018] Terminal 10 is a device operated by the user. In this embodiment, the user who conducts legal research, makes judgments, etc., operates Terminal 10. Terminal 10 and Terminal 10A have similar functional configurations. Terminal 10 is implemented, for example, as follows. • Handheld mobile devices such as smartphones and tablets • Stationary PCs (Personal Computers), Laptop PCs • Wearable devices worn by the user (watch-type, glasses-type, etc.) Terminal 10 includes a communication interface (IF) 12, an input device 13, an output device 14, memory 15, storage 16, and a processor 19.

[0019] The communication interface 12 is an interface for inputting and outputting signals so that terminal 10 can communicate with an external device.

[0020] The input device 13 is a device for receiving input operations from the user (for example, a touch panel, touchpad, pointing device such as a mouse, keyboard, etc.).

[0021] The output device 14 is a device (such as a display or speaker) for presenting information to the user.

[0022] Memory 15 is for temporarily storing programs and data processed by programs, etc., and is a volatile memory such as DRAM (Dynamic Random Access Memory).

[0023] Storage 16 is for storing data, and can be, for example, flash memory or an HDD (Hard Disk Drive).

[0024] The processor 19 is hardware for executing the instruction set described in the program, and consists of an arithmetic unit, registers, peripheral circuits, etc.

[0025] Server 20 is a device that provides a service to users by receiving legal questions and answering them while presenting multiple pieces of relevant legal content. In this embodiment, Server 20 receives legal questions from the user of Terminal 10 in a chat format as free text and outputs answers to the received questions. In generating answers to such questions, Server 20 may also have Server 95 of the artificial intelligence (large-scale language model) service generate the answers, and provides the service by responding to the user with the generated results.

[0026] In this embodiment, the server 20 provides services to the following types of users. • Users who perform legal duties and provide answers to legal consultations, similar to the legal departments of business companies. • Users who do not necessarily have dedicated legal staff, such as business divisions within a company. • Users who provide legal consultations to clients as a service, such as law firms. Server 20 may accept requests for legal consultations from clients to experts by matching users who provide legal consultation services as professionals, such as law firms, with users who request legal consultations from experts such as lawyers, such as businesses or individuals. For example, the content of the consultation may be posted on a bulletin board that is accessible to third parties, and the expert's response may also be made public, or the content of the consultation may be kept confidential and not disclosed to third parties, allowing the consultation to take place privately between the client and the expert. Server 20 may also store such client consultation content and expert response content.

[0027] The server 20 includes a communication interface 22, an input / output interface 23, memory 25, storage 26, and a processor 29.

[0028] Communication IF22 is an interface for inputting and outputting signals so that the server 20 can communicate with external devices.

[0029] Input / Output IF23 functions as an interface between an input device for receiving user input operations and an output device for presenting information to the user.

[0030] Memory 25 is for temporarily storing programs and data processed by programs, etc., and is a volatile memory such as DRAM (Dynamic Random Access Memory).

[0031] Storage 26 is for storing data, and can be, for example, flash memory or an HDD (Hard Disk Drive).

[0032] The processor 29 is hardware for executing the instruction set described in the program, and consists of an arithmetic unit, registers, peripheral circuits, etc.

[0033] Server 91 of the case law search service has a database of case law, allowing users to search for judgments made by courts and other authorities.

[0034] Server 92 of the legal search service has a database of laws and regulations and allows users to search for those laws and regulations.

[0035] The book browsing service server 93 provides a service that makes electronic content such as magazines and books available for viewing. For example, users can access ebooks in fields such as law by paying a fixed fee on a regular basis.

[0036] Server 94 of the Information Media Services provides information services, such as collecting and making available blog posts, Q&A sites, news articles, and IR information. Server 94 of the Information Media Services also stores information on interpretations based on laws and regulations, as well as guidelines from government agencies and other organizations prepared to disseminate operational rules.

[0037] Server 95 of the Large-Scale Language Model Service is a server that executes language processing tasks using language models built through learning processes including artificial intelligence (AI). An LLM (Large Language Model) is a model that has been pre-trained on large amounts of data (such as text data), for example, a large amount of web content on the internet, or a large amount of data stored in a designated database, and can perform various language processing tasks by being given a task.

[0038] The server 95 of the large-scale language model service accepts prompt input in the form of text, images, audio, etc., and generates and responds with answers to those prompts. Examples of LLMs include GPT-3 and GPT-4 developed by OpenAI, and BERT developed by Google.

[0039] Server 96, operated by the service provider, stores data generated as a result of the service provider's business activities. Access to this data is restricted, with viewing permissions set for users belonging to the service provider and external users not belonging to the service provider, depending on the type of data.

[0040] SNS Server 97 provides services that facilitate interaction between users, such as a service that allows users to mutually view each other's posts. For example, there are services that can be viewed via internet search even without a user account on the SNS, and services that allow users with a user account to view each other's posts.

[0041] <1.2 Functional Configuration of Server 20> Figure 2 shows the configuration of server 20. As shown in Figure 2, server 20 functions as a communication unit 201, a storage unit 202, and a control unit 203.

[0042] The communications unit 201 performs processing to enable the server 20 to communicate with external devices.

[0043] The memory unit 202 stores various databases, including a user database 211, a legal content database 212, a content usage history database 213, a graph-type data structure 214, a prompt database 218, and a legal terminology database 219.

[0044] User database 211 is a database that manages information for each user. Further details will be provided later.

[0045] Legal Content Database 212 is a database that holds information on legal content. Further details will be provided later.

[0046] Content Usage History Database 213 is a database of user usage history for legal content. Further details will be provided later.

[0047] The graph-type data structure 214 illustrates a graph-type data structure in which reference relationships are defined for a portion of each content.

[0048] The prompt database 218 is a database that manages prompt templates to be sent to the server 95 of the large-scale language model service.

[0049] Legal Terminology Database 219 is a database of legal terms. Legal terms include, for example, the names of various legal issues as defined in laws and regulations ("claims," ​​"debts," "damages," "risk allocation," etc.), and terms used in case law.

[0050] The control unit 203 is realized when the processor 29 reads a program stored in the memory unit 202 and executes instructions contained in the program. By operating according to the program, the control unit 203 performs the functions shown as the receive control module 2041, transmit control module 2042, user management module 2043, query processing module 2044, LLM utilization module 2045, and data structure definition module 2046.

[0051] The receive control module 2041 controls the process by which the server 20 receives signals from external devices according to a communication protocol.

[0052] The transmission control module 2042 controls the process by which the server 20 transmits signals to external devices according to a communication protocol.

[0053] The user management module 2043 is a module for managing information for each user using System 1. Specifically, the user management module 2043 accepts registration of each user's information and updates the user database 211.

[0054] The question processing module 2044 is a program module that, in response to a question entered by a user, searches for content managed in the legal content database 212 based on a graph-type data structure 214 and generates an answer to the question.

[0055] The LLM utilization module 2045 is a program module that generates prompts and sends the generated prompts to the server 95 of the large-scale language model service, thereby generating responses to the prompts.

[0056] The data structure definition module 2046 is a program module that defines a graph-type data structure 214 based on the content managed in the legal content database 212.

[0057] <1.3 Configuration of Terminal 10> Figure 3 shows the configuration of terminal 10.

[0058] As shown in Figure 3, terminal 10 includes multiple antennas (antenna 111, antenna 112), communication units corresponding to each antenna (first communication unit 120, second communication unit 121), an input device 130 (including a touch-sensitive device 131), a display 132, an audio processing unit 140, a microphone 141, a speaker 142, a position information sensor 150, a camera 160, a motion sensor 170, a storage unit 180, and a control unit 190. Terminal 10 also has functions and configurations not specifically shown in Figure 3 (for example, a battery for maintaining power, a power supply circuit for controlling the supply of power from the battery to each circuit, etc.). As shown in Figure 3, each block included in terminal 10 is electrically connected by a bus or the like.

[0059] Antenna 111 radiates signals emitted by terminal 10 as radio waves. Antenna 111 also receives radio waves from space and provides the received signals to first communication unit 120.

[0060] Antenna 112 radiates signals emitted by terminal 10 as radio waves. Antenna 112 also receives radio waves from space and provides the received signals to the second communication unit 121.

[0061] The first communication unit 120 performs modulation and demodulation processing, etc., for the terminal 10 to transmit and receive signals via the antenna 111 in order to communicate with other wireless devices. The second communication unit 121 also performs modulation and demodulation processing, etc., for the terminal 10 to transmit and receive signals via the antenna 112 in order to communicate with other wireless devices. The first communication unit 120 and the second communication unit 121 are a communication module that includes a tuner, an RSSI (Received Signal Strength Indicator) calculation circuit, a CRC (Cyclic Redundancy Check) calculation circuit, a high-frequency circuit, etc. The first communication unit 120 and the second communication unit 121 perform modulation and demodulation, frequency conversion, etc., of the wireless signals transmitted and received by the terminal 10, and provide the received signal to the control unit 190.

[0062] The input device 130 has a mechanism for receiving user input operations. Specifically, the input device 130 is configured as a touchscreen and includes a touch-sensitive device 131. The touch-sensitive device 131 receives user input operations of the terminal 10. The touch-sensitive device 131 detects the user's contact position with the touch panel, for example, by using a capacitive touch panel. The touch-sensitive device 131 outputs a signal indicating the user's contact position detected by the touch panel to the control unit 190 as an input operation.

[0063] The display 132 displays data such as images, videos, and text in accordance with the control of the control unit 190. The display 132 is implemented by, for example, an LCD or an organic EL display.

[0064] The audio processing unit 140 modulates and demodulates the audio signal. The audio processing unit 140 modulates the signal received from the microphone 141 and provides the modulated signal to the control unit 190. The audio processing unit 140 also provides the audio signal to the speaker 142. The audio processing unit 140 is implemented, for example, by an audio processing processor. The microphone 141 receives an audio input and provides the audio signal corresponding to that audio input to the audio processing unit 140. The speaker 142 converts the audio signal received from the audio processing unit 140 into sound and outputs the sound to the outside of the terminal 10.

[0065] The location information sensor 150 is a sensor that detects the location of the terminal 10, and is, for example, a GPS (Global Positioning System) module. A GPS module is a receiving device used in a satellite positioning system. In a satellite positioning system, signals are received from at least three or four satellites, and the current location of the terminal 10, which is equipped with a GPS module, is detected based on the received signals.

[0066] Camera 160 is a device that receives light using a photodetector and outputs it as an image. Camera 160 is, for example, a depth camera that can detect the distance from camera 160 to the object being photographed.

[0067] The motion sensor 170 includes an acceleration sensor, an angular velocity sensor, etc., and detects the movement of the terminal 10.

[0068] The storage unit 180 is composed of, for example, flash memory and stores data and programs used by the terminal 10. The various types of information stored in the storage unit 180 will be described later.

[0069] The control unit 190 controls the operation of the terminal 10 by reading the program stored in the memory unit 180 and executing the instructions contained in the program. The control unit 190 is, for example, an application processor. By operating according to the program, the control unit 190 performs the functions of an operation reception unit 191, a transmission / reception unit 192, a data processing unit 193, a notification control unit 194, and a memory control unit 195.

[0070] The operation reception unit 191 processes input operations from the user to an input device such as a touch-sensitive device 131. Based on the coordinate information of the touch-sensitive device 131 where the user's finger or the like has made contact, the operation reception unit 191 determines the type of operation, such as whether the user's operation is a flick operation, a tap operation, or a drag (swipe) operation.

[0071] The transmitting / receiving unit 192 performs processing to enable the terminal 10 to send and receive data with an external device such as a server 20 in accordance with a communication protocol.

[0072] The data processing unit 193 performs calculations on the data received as input by the terminal 10 according to the program and outputs the calculation results to memory or other locations.

[0073] The notification control unit 194 performs the following processes: displaying the display image on the display 132, outputting sound to the speaker 142, and generating vibrations.

[0074] The memory control unit 195 controls the storage of data to the memory unit 180.

[0075] The various types of information stored by the memory unit 180 will now be explained. In a given situation, the memory unit 180 stores various types of information, such as user information 181.

[0076] User information 181 is information about a user who uses the services of server 20.

[0077] <2 Data Structure> Figure 4 shows the data structure of the user database 211. The user database 211 includes the following fields: "User ID", "Name", "Email Address", "Business ID", "Department", "Position", "Date of Employment", "Date of Resignation", and "Qualifications Held".

[0078] The "User ID" field is information that identifies each user.

[0079] The "Name" field contains information indicating the user's name.

[0080] The "Email Address" field contains the user's email address information for contact purposes.

[0081] Specifically, the item "email address" contains email address information, which serves as user identification information for accepting user logins to services provided by server 20.

[0082] The "Business ID" field is information that identifies the organization to which the user belongs.

[0083] Specifically, the "Business ID" field is information that identifies the organization to which the user belongs, and the following types of organizations are possible: • Business company ·Law firm The "Department" field contains information about the department to which the user belongs.

[0084] Specifically, the item "Department" may include the following information as the department to which the user belongs: • Departments such as the legal department that are expected to perform legal duties. • Departments such as business divisions and sales divisions that do not plan to have someone dedicated to legal affairs. The "Job Title" field contains information about the user's job title.

[0085] Specifically, the "Job Title" field may include the following information regarding the user's job title: • Positions with decision-making authority (e.g., management) • A position that does not have decision-making authority but demonstrates expertise and a role in supporting the management department. • Not holding a managerial position. The "Date of Joining" field contains information about the date the user became a member of the organization.

[0086] Specifically, the item "Date of Joining the Company" may include the following information: • Start date if you are already a member • A date when they are scheduled to join the organization but are not yet affiliated (They may have been hired but not yet have access to the organization's information because their start date has not yet arrived). • While affiliated with the company, the start date is not yet confirmed (the start date may still be being finalized). The "Resignation Date" field contains information about the date on which the user left the organization and resigned.

[0087] Specifically, the item "Retirement Date" may include the following information: • Date of resignation (Upon resignation, access to organizational information may be lost) • Currently employed The "Qualifications Held" item contains information indicating the qualifications held by the user.

[0088] Specifically, the item "Qualifications Held" may include the following qualifications, which serve as information to identify the qualifications held by the user. • National qualifications that demonstrate expertise, such as lawyer, patent attorney, and certified public accountant. • Qualifications recognized by businesses, general incorporated associations, and other organizations as proof of expertise. Figure 5 shows the data structure of the legal content database 212.

[0089] The "Content ID" field is information that identifies each piece of legally relevant content.

[0090] The "Type" field contains information about the type of legal content.

[0091] The "Type" item may include the following as types of legal content: ·Laws Case law • Books on law • Websites containing legal information The item "Source" refers to the information about the source from which the content is provided.

[0092] The item "Sources" may include the following as sources: • Server 92 for the legal search service • Server 91 of the case law search service • Server 93 of the book browsing service that provides access to legal books. • Information media service server 94, which contains legal information. The "Title" field contains information about the name of the content.

[0093] The "Title" field may include the following as the name of the content: In the case of laws and regulations, the name of the law, the article number, the headings attached to the articles, etc. • In the case of precedents, the case number, the name of the case, etc. • In the case of books on law, the title of the book, etc. • In the case of legal guidelines, the name of the guideline, etc. The item "Content Tags" contains information about the names of the tags assigned to the content.

[0094] The item "Content Tags" may include the following as information about tags assigned to the content: • The large-scale language modeling service server 95 extracts tags representing the content by summarizing data indicating the content using keywords, a certain number of characters, etc. • Server 20 maintains a list of tags to be assigned to the entire content in advance, and based on the content, the server 95 of the large-scale language model service identifies tags from the tags included in the list. The item "Data" contains information about the content's data file.

[0095] The item "Tagged Location" refers to information about the location where tags are set, using the names of laws, precedents, legal books, legal guidelines, etc., as tags, at any point in the data that makes up the content.

[0096] The item "Tagging Location" may include the following as locations where tags are set: In the case of laws and regulations, tags may be set in association with specific clause numbers, etc. In the case of case law, the judgment can be divided into multiple blocks based on the paragraphs, headings, arguments of both parties, and the sections where the court's judgments on each issue are described, and tags can be set at any point in the text within those blocks. In the case of legal books, the book may be divided into multiple blocks based on chapters, headings, paragraphs, etc., and tags may be set at any point in the text within those blocks. • In the case of legal guidelines, the guideline text may be divided into multiple blocks in the same manner as above, and tags may be set at any point in the text within those blocks. For example, if a book on law contains names such as the names of laws and regulations, the names of articles, or the numbers of precedents, server 20 may set tags on the parts where such names are written.

[0097] The "Name Tag" field contains information about the name of the tag that has been set.

[0098] The item "Name Tag" may include the following as tags attached to the name: • Name of the law, article number, etc. • Name of the case number, etc. of the precedent • Titles of legal books • Name of the legal guidelines The "Name Tag Score" item contains information about the evaluation results for the tags that indicate the name.

[0099] More specifically, the "Name Tag Score" item is an evaluation of tags based on the history of content viewing in various services provided by the legal search service server 92, the case law search service server 91, the book browsing service server 93, the information media service server 94, etc., where tags and content are associated.

[0100] Server 20 may use the results of the evaluation of name tags in this manner to determine the priority of tags corresponding to search terms when a user performs a search (for example, the higher the evaluation of a tag, the higher the priority given to it as a tag corresponding to the user's search), and search for legal content based on the tags with the highest priority.

[0101] The "Name Tag Associated Content ID" field is information that identifies the content whose reference relationship is defined based on where the tag is set, when the same tag is set in multiple content items for the tag set in the "Name Tag" field.

[0102] In the illustrated example, the item "Name Tag Associated Content ID" shows the result of defining reference relationships between content containing the name of a specific law, where each piece of content related to multiple laws contains the name of that specific law.

[0103] The item "Location of Issue Tagging" refers to information about the location where legal issues are tagged and set as tags in arbitrary parts of the data that make up the content.

[0104] The item "Issue Tag" contains information about the names of the tags assigned to the issues.

[0105] The item "Issue Tag Score" contains information on the evaluation results for tags that indicate issues.

[0106] More specifically, the "Issue Tag Score" item is determined by classifying legal books by issue on server 93 of the book browsing service, etc., accepting operations to search for books to be viewed based on issues, and evaluating the issue tags based on the user's browsing history of legal books in response to the search results.

[0107] The item "Issue Tag Associated Content ID" is information that identifies the content that defines the reference relationship based on where the issue tag is set, when the same issue tag is set in multiple pieces of content, as set in the item "Issue Tag".

[0108] Figure 6 shows the data structure of the content usage history database 213.

[0109] The "Search ID" field is information that identifies each search performed to access legal content.

[0110] The "Search User ID" field identifies the user who searched for legal content.

[0111] The item "Search User ID" may be associated with the item "User ID" in user database 211.

[0112] The "Search Terms" field contains information about the search terms used to find the content.

[0113] More specifically, the "search term" field is a word specified by the user, or a sentence entered by the user in natural language as a question.

[0114] The item "Search Issue Tag" contains information about the names of the issue tags assigned to the search terms entered by the user.

[0115] More specifically, the item "Search Issue Tag" is either a tag for an issue extracted by matching the search term entered by the user with a list of issue tags, or information on a legal issue set by the server 95 of the large-scale language model service, etc.

[0116] The "Search Date and Time" field contains information about the timing of the search.

[0117] The "Source" item is database information that is referenced when a user performs a search for content.

[0118] The item "Information Source" may include the following as databases referenced by the search: • Server 92 of the legal search service • Server 91 of the case law search service • Server 93 for the book browsing service • Information Media Services Server 94 The "Viewed Content ID" field is information that identifies the content viewed by the user.

[0119] The item "Viewed Content ID" may be associated with the item "Content ID" in the legal content database 212.

[0120] The item "Content Viewed Location" contains information about the specific locations where the user viewed content.

[0121] More specifically, the item "Content Viewed" refers to information about the sections (pages; it may also refer to pages viewed for a certain period of time or longer) within a book related to law.

[0122] Figure 7 shows the data structure of graph-type data structure 214.

[0123] In the example shown in Figure 7, it is indicated that information for a set of text 214A, which includes the relevant section of a specific law contained in document A, is stored in the legal content database 212, associated with document A (e.g., a legal book). Similarly, it is indicated that information for a set of text 214B, which includes the relevant section of a specific law contained in document B, is stored in the legal content database 212, associated with document B (e.g., guidelines), as the name of the set of text 214B.

[0124] In this example, a reference relationship (edge) is defined between parts of document A and document B that contain the same legal provision (document 214A, document 214B). In this way, the data structure definition module 2046 generates a graph-type data structure 214 by defining nodes for the parts of the legal content database 212 that contain the names of laws, precedents, legal books, guidelines, etc., and defining nodes and edges based on these names. The example shown illustrates the definition of a reference relationship between two documents, A and B, but it is not limited to just two.

[0125] <3. Operation (First Embodiment)> Figure 8 shows the process flow for creating a graph database using information from multiple legal content sources.

[0126] In step S821, the data structure definition module 2046 of the server 20 obtains information on legal content and information that can be used as tagging candidates by referring to the legal content database 212, the legal terminology database 219, etc.

[0127] In this way, the server 20 stores information on multiple legal content in the legal content database 212 of the storage unit 202.

[0128] In step S823, the data structure definition module 2046 of server 20 identifies a portion of the legal content that includes information identifying laws and regulations, information identifying precedents, information identifying legal books, and information identifying legal guidelines.

[0129] Thus, in each of the multiple legal content items, the data structure definition module 2046 is defined as follows: Information that identifies laws and regulations, Information that identifies a case, Information identifying legal books, Information identifying legal guidelines, Identify a portion that includes at least one of the following.

[0130] More specifically, the data structure definition module 2046 is: As information identifying the law, at least one of the law's name or provision, Information that identifies a case includes the case number, Information that identifies a legal book includes the title of the legal book, Information that identifies legal guidelines, such as the name of the guideline, It may also be necessary to identify a portion that includes at least one of the following.

[0131] More specifically, the data structure definition module 2046 is: As information identifying the law, at least one of the law's name or provision, Information that identifies a case includes the case number, Information that identifies a legal book includes the title of the legal book, Information that identifies legal guidelines, such as the name of the guideline, It is also possible to identify a certain amount of text as a part that contains at least one of the following.

[0132] In step S825, the data structure definition module 2046 of the server 20 associates the identified portion of the content with the content itself and stores it in the legal content database 212.

[0133] In this way, the data structure definition module 2046 stores the portion identified in step S823 in the storage unit 202 in association with the content related to that identification.

[0134] Here, the data structure definition module 2046 may evaluate a portion of the content based on the portion associated with the content, according to the usage of the search results in a service that accepts operations to search for content (for example, the server 91 for the case law search service, the server 92 for the legal search service, the server 93 for the book browsing service, the server 94 for the information media service, etc.). For example, regarding tags associated with content, the tags may be evaluated according to the search history of those tags in the book browsing service mentioned above (e.g., the evaluation value is increased for frequently searched tags and for tags that lead to books being viewed through searches). The data structure definition module 2046 may store the evaluation results of the evaluated portion in the legal content database 212 in association with the content (for example, the item "Name Tag Score"). For example, when the server 20 identifies a tag corresponding to a user's question in a graph-structured database, it may select a tag according to its evaluation value (e.g., prioritizing tags with high evaluation values ​​as tags corresponding to the user's question), and then search the graph-structured database using the selected tag. This improves the response time of tag-based searches using a graph structure compared to searching each piece of content in the legal content database 212 using keywords, while further enhancing the accuracy of search results (presenting legal content suitable for the user).

[0135] By processing steps S823 and S825 described above, it is possible to identify the locations where the names of laws, precedents, books, guidelines, etc., are mentioned, and to assign tags based on these names to the content.

[0136] In step S827, the data structure definition module 2046 of the server 20 extracts identical portions of multiple content items. The data structure definition module 2046 defines reference relationships for the portions of each content item that have been extracted as containing identical items.

[0137] More specifically, in defining reference relationships, the data structure definition module 2046 may define reference relationships between content by extracting content that has a part of the same type, and by extracting content with the same name relating to the identified part.

[0138] For example, if the same case law number is described in both the first and second ebooks, the section in the first ebook where the case law number is described and the section in the second ebook where the case law number is described are extracted as being of the same type. The data structure definition module 2046 defines reference relationships between the extracted sections in the first and second ebooks where the above-mentioned case law number is described.

[0139] In this way, the data structure definition module 2046 constructs a graph-structured database by extracting content with similar identified parts from multiple content items and defining reference relationships between the content items for each extracted content item. For example, the data structure definition module 2046 may store the reference relationships defined for multiple content items in the legal content database 212.

[0140] Here, the data structure definition module 2046 may define reference relationships between portions of text, which are fixed amounts of text, from multiple contents.

[0141] This allows for the generation of a graph-type data structure that defines reference relationships between multiple pieces of content when those pieces of content contain the same names of laws, precedents, books, and guidelines. For example, if a graph-type data structure is generated based on the name of a law, searching for that law's name can display search results from multiple sources, such as the original text of the law, corresponding precedents, books that mention the law, and guidelines that mention the law. Because the search is performed using a graph-type data structure, the responsiveness of the search can be further improved compared to sequentially searching these sources using the keywords mentioned above.

[0142] In step S829, the data structure definition module 2046 of the server 20 outputs information about the reference relationships defined for multiple contents to the terminal 10. Alternatively, the data structure definition module 2046 may choose not to output the information to the terminal 10 after defining the reference relationships between the contents, or it may present the information about the reference relationships defined between the contents upon request from the terminal 10.

[0143] In step S811, terminal 10 displays information indicating the content reference relationships.

[0144] Figure 9 shows the processing flow using a graph database related to laws.

[0145] In step S921, the question processing module 2044 of the server 20 receives input of a legal question and outputs an operation screen to the terminal 10 that displays the answer.

[0146] As described above, Server 20 holds a data structure for information on legal content (legal content database 212, graph-type data structure 214). Server 20 stores information on multiple legal content items in its storage unit 202. In the storage unit 202, Server 20 stores the results of defining reference relationships between content items for each of the multiple content items, where at least one of the following is included: information that identifies laws and regulations, information that identifies precedents, information that identifies legal books, and information that identifies legal guidelines.

[0147] In step S911, terminal 10 receives input from the user regarding legal questions.

[0148] In step S923, the question processing module 2044 of server 20 assigns a tag representing the content of the question to the question entered by the user of terminal 10, with the help of the server 95 of the large-scale language model service. For example, the question processing module 2044 generates a prompt that includes, along with the content of the question, at least one of the following: an instruction to summarize the content of the question entered by the user, an instruction to refer to legal terms such as the legal terminology database 219 regarding the content of the question and assign a tag to it. The question processing module 2044 sends the prompt to the server 95 of the large-scale language model service via the LLM utilization module 2045, and assigns a tag to the question by receiving an output result corresponding to the prompt from the server 95 of the large-scale language model service.

[0149] In step S925, the question processing module 2044 of the server 20 uses the tags assigned to the question to refer to the graph-type data structure 214 and search for tagged content. This reduces the processing required to search for tagged content compared to searching a database of table-type content, and further improves the response speed of search results.

[0150] In step S927, the question processing module 2044 of server 20 obtains the generated answer by instructing the server 95 of the large-scale language model service to generate an answer to the question based on the search results. Specifically, the question processing module 2044 generates a prompt that includes the search results (legal content searched based on tags assigned to the question) retrieved by referring to the graph-type data structure 214 and the question entered by the user in steps S911 and S923, and sends the generated prompt to the server 95 of the large-scale language model service, thereby receiving the answer generated by the server 95 of the large-scale language model service.

[0151] Furthermore, the question processing module 2044 may, in response to a legal question input from the user, output the search results of the content retrieved in step S925 to the user's terminal 10 without further generating an answer to the question by the server 95 of the large-scale language model service. This allows the system to respond to the user's question with legal content retrieved using the graph-type data structure 214, reducing the processing required for the search and enabling a faster response of search results compared to sequentially searching each data in a table-type content database.

[0152] Thus, the server 20 has a data structure 214 for information on legal content, which is used in the process of responding to a search request to retrieve information on multiple legal content items. This structure identifies a portion (tag) in the database that corresponds to the search request and responds to the search request based on the reference relationships between the content items for the identified portion.

[0153] Here, as described above, the data related to the data structure is used in a process where, in response to a search request, the server 95 of the large-scale language model service receives a question input from the user and generates an answer to the question. This search request searches for a portion of multiple contents that corresponds to the question, and the server responds with search results for multiple contents based on the reference relationships between the contents for that portion. The large-scale language model then generates an answer to the question based on the search results for multiple contents and the question from the user.

[0154] In step S929, the server 20 outputs an answer to the question to the terminal 10 based on the tag corresponding to the question.

[0155] In step S913, terminal 10 displays the answer to the question.

[0156] <4. Screen Example (First Embodiment)> Figure 10 is an example of an operation screen that displays the results of generating answers to user questions while referring to a graph-type data structure 214 related to the law.

[0157] The user interface 1000 displays the results of a search for laws, precedents, legal books, guidelines, etc., in response to a question, while referring to the graph-type data structure 214.

[0158] The operation screen 1000 corresponds to each process, such as step S921 in Figure 9, step S1121 in Figure 11, and step S1321 in Figure 13.

[0159] The question designation unit 1002 is an operating component that receives the user's designation of the question content.

[0160] More specifically, the question specification unit 1002 accepts questions from the user in natural language.

[0161] The transmission operation unit 1004 is an operation component that receives an operation to generate an answer to a question entered by the user.

[0162] In the illustrated example, the transmission operation unit 1004 transmits the question entered in the question specification unit 1002 to the server 20 in response to the user's operation.

[0163] The account display area 1006 is an area that displays account information for users who use the services provided by server 20.

[0164] In the illustrated example, the account display area 1006 also displays the user's affiliation, but it may also display the user's account pricing plan to make it easier to recognize the differences in available features depending on the pricing plan (for example, limitations on the number of questions that can be entered, and limitations on the scope of laws, precedents, legal books, guidelines, etc. that can be referenced).

[0165] The answer display area 1008 is an area that displays the answer generated by the server 20 in response to the question entered by the user.

[0166] The question content display area 1010 is an area that displays the content of the question entered by the user in the question specification unit 1002.

[0167] In the illustrated example, the question content display area 1010 displays the question for which the server 20 has generated an answer, making it easy to recognize which question the answer is for. For example, if an answer is generated each time a question is entered, even when scrolling back through past questions, it is easy to confirm which question the answer is for.

[0168] The question tag display area 1012 is an area that displays the tags assigned to the entered question.

[0169] The question tag display area 1012 corresponds to the processing shown in step S923 of Figure 9.

[0170] The legal information display area 1014 is an area that displays the legal information retrieved by referring to the graph-type data structure 214 in response to the entered question.

[0171] In the illustrated example, the legal information display area 1014 displays the name of the law and the article number, and also includes a "View Details" button. In response to the user's operation of the "View Details" button, terminal 10 may display the details of the law (for example, the original text of the article) within the operation screen 1000 (for example, by expanding the area of ​​the legal information display area 1014), or it may open a separate window to allow the user to view the details of the law.

[0172] The case law display area 1016 is an area that displays case law retrieved by referring to the graph-type data structure 214 in response to the entered question.

[0173] In the illustrated example, the case law display area 1016 displays the case law number and includes a "View Details" button. Terminal 10 may also display the details of the searched case law (such as an overview of the case, as described later) on the operation screen 1000 or elsewhere, in response to the user's operation of the "View Details" button.

[0174] The legal books display area 1018 is an area that displays legal books found by referring to the graph-type data structure 214 in response to the entered question.

[0175] In the illustrated example, the legal book display area 1018 presents the user with details of the searched legal book (for example, after receiving a user action such as "View Details" shown in the legal information display area 1014, etc.). The legal book display area 1018 displays an overview of the book, such as the title, publisher, and author, while the book details display area 1020, etc., described later, displays an excerpt from the book's text.

[0176] The book details display area 1020 is an area that displays a portion of a legal book extracted according to the searched tags.

[0177] In the illustrated example, the book details display area 1020 displays a portion of the information displayed in the evidence display area 1022, which will be described later, as well as the surrounding text. For example, in a legal book, the text may be divided into multiple blocks by chapters, paragraphs, etc., and the blocks containing the text indicated in the "Tagged Locations" item of the legal content database 212 may be extracted.

[0178] The evidence display area 1022 is an area that displays the location in legal textbooks where the corresponding information is found.

[0179] In the illustrated example, the evidence display area 1022 highlights the text shown in the "Tagged Location" item of the legal content database 212 (for example, by highlighting it with a marker or making it bold), making it easy to identify the location of the text corresponding to the tag.

[0180] The guideline display area 1024 is an area that displays the guidelines found by referring to the graph-type data structure 214 in response to the entered question.

[0181] In the illustrated example, the guideline display area 1024 displays the name of the guideline and includes a "View Details" button. Terminal 10 may also display the details of the case within the operation screen 1000 or elsewhere to allow the user to view the details of the searched guideline in response to the user's operation of the "View Details" button.

[0182] <5 Operation (Second Embodiment)> Next, a second embodiment will be described.

[0183] When a legal consultation is received, it can sometimes be difficult to identify the relevant law from the consultation content alone. This is because legal provisions are often defined using abstract language, and even if search terms are defined based on the consultation content, the results may not correspond to the wording of the legal provision. Therefore, the usual procedure involves searching for cases that correspond to the consultation content and then identifying the relevant law and provision from those cases. For the person providing the consultation, especially in an unfamiliar field, this investigation procedure can be time-consuming.

[0184] Therefore, in the second embodiment, we will describe a technology that accepts natural language input and presents relevant laws and regulations.

[0185] Specifically, in the second embodiment, Accepting questions from users in natural language, The server 95 of the large-scale language model service will be used to identify which law the question pertains to (for example, a specific law such as the Companies Act). Legal books and web articles related to law are pre-tagged (the tagging of such legal books may be performed by the server 95 of the large-scale language model service), and the content of legal books, etc., is searched for those whose content matches the tags (searching by keyword, vectorizing the question text and the content of legal books, etc., and then searching for vectors of law-related content based on the vectors), To obtain laws and regulations cited within the text of searched content, Perform.

[0186] Figure 11 shows the process flow for identifying the relevant laws and regulations in response to the user's question and presenting those laws and regulations to the user.

[0187] In step S1121, the question processing module 2044 of the server 20 receives input of a legal question and outputs an operation screen to the terminal 10 that displays the answer.

[0188] Server 20 stores information on multiple legal content in its storage unit 202, including at least one of the following: legal books, legal guidelines, and articles from legal information media. Storage unit 202 stores information on multiple legal books, and each of these legal books is associated with a tag indicating its content.

[0189] For example, server 20 may set tags indicating the content of legal books by referring to the classification set for each book in server 93 of the book browsing service (classifications entered by a human, such as the service operator or the book publisher), or server 95 of the large-scale language modeling service may assign tags indicating the content of legal books (for example, by sending a prompt to server 95 of the large-scale language modeling service to summarize the content of legal books).

[0190] In step S1111, terminal 10 receives input from the user regarding legal questions.

[0191] In step S1123, the question processing module 2044 of server 20 assigns a tag corresponding to the question entered by the user, using the server 95 of the large-scale language model service.

[0192] The question processing module 2044 assigns tags to questions by providing a prompt to the server 95 of the large-scale language model service that includes instructions to assign legal tags to the questions. The module then obtains information on the tag assignment results output by the server 95 of the large-scale language model service, and assigns tags to the questions based on the obtained information on the tag assignment results.

[0193] Alternatively, the question processing module 2044 may assign tags to questions by inputting the questions entered by the user in step S1111 into a trained model that has been trained to output tags to questions based on the results of tagging questions.

[0194] In addition, the question processing module 2044 also maintains a list of tags to be assigned to questions based on keywords contained in the question text, and may assign tags to the question by comparing the question entered in step S1111 with this list.

[0195] In step S1125, the question processing module 2044 of the server 20 uses the tags assigned to the question to refer to the database and search for tagged content.

[0196] In this way, the question processing module 2044 searches for information from multiple legal content sources based on the entered question. Here, the question processing module 2044 searches for information from at least one of the following: legal books, legal guidelines, or articles from legal information media. The question processing module 2044 may also search for legal books based on tags assigned to the question and tags associated with legal books.

[0197] Furthermore, in a service that allows users to view multiple legal books (the server 93 for the book viewing service), the scores of tags associated with legal books are determined (legal content database 212, content usage history database 213). The question processing module 2044 may search for legal books based on the scores of the tags associated with them. For example, when searching for tags in the database that correspond to the tags assigned to the question, tags with higher scores may be given priority in the search results.

[0198] As described above, the server 20 stores information on multiple legal content items in the storage unit 202. In the storage unit 202, for each of the multiple content items, the results of defining reference relationships between content items that are of the same type, including at least one of the following: information that identifies laws and regulations, information that identifies precedents, information that identifies legal books, and information that identifies legal guidelines, are stored as a legal data structure (legal content database 212, graph-type data structure 214). The question processing module 2044 retrieves information on multiple legal content items related to a question by referring to the legal data structure.

[0199] In step S1127, the question processing module 2044 of the server 20 identifies information about laws and regulations cited in the search results content. For example, the question processing module 2044 may search for content based on the tags assigned to the question and information associated with legal books held in the legal content database 212 (such as the "Title" and "Content Tag" items in the legal content database 212), and identify information about laws and regulations included in the searched content (for example, the "Tagged Location" and "Name Tag" items in the legal content database 212).

[0200] In step S1129, the question processing module 2044 of the server 20 presents the identified legal information to the user of the terminal 10.

[0201] In step S1113, terminal 10 displays information on laws and regulations corresponding to the question.

[0202] <6. Screen Example (Second Embodiment)> Figure 12 shows an example of an operation screen that presents the user with the relevant laws and regulations identified in response to the user's question.

[0203] The legal details display area 1026 is an area that displays a portion of the legal information extracted according to the searched tags for the searched legal information.

[0204] In the illustrated example, the legal details display area 1026 displays the legal provisions corresponding to the searched tag. For example, the server 20 may display only the provisions corresponding to the question, rather than all of the provisions contained in the legal text.

[0205] Case details display area 1028 is an area that displays summaries of multiple case precedents extracted according to the searched tags.

[0206] In the illustrated example, the case details display area 1028 displays the results of generating summaries of multiple case precedents, which were retrieved by referring to the graph-type data structure 214 according to the tags corresponding to the question, using the server 95 of the large-scale language model service (for example, the results of generating summaries of case precedents by prompting the generation of an overview of the case and an overview of the conclusion within a predetermined number of characters).

[0207] <7 Operation (Third Embodiment)> Next, a third embodiment will be described.

[0208] Typically, when conducting legal research, searching for legal content using keywords will result in a list of relevant articles. However, especially in areas unfamiliar to the researcher, it can be difficult to determine the importance of the listed articles (e.g., legal books), requiring the researcher to meticulously read through each individual article, which is a significant burden.

[0209] Therefore, in the third embodiment, the system accepts a question in natural language and presents the user with issues related to the entered question. For each issue, the system presents the user with organized content such as legal books.

[0210] Specifically, in the third embodiment, To construct a graph-type data structure for the issues (for example, the issues of each piece of legal content may be identified by the server 95 of the large-scale language model service. For each issue, legal content such as laws, precedents, legal books, and guidelines are associated and constructed as a graph-type data structure), The system accepts questions from users in natural language. Based on a database constructed to hold information on legal content based on the issue at hand, the system identifies the issue corresponding to the question (for example, identifying the issue from keywords contained in the question, or vectorizing the question and searching for the issue based on the vector of legal content). Based on the issues corresponding to the questions and a graph-type data structure for those issues, the response will be to provide relevant laws, precedents, legal books, guidelines, etc. Perform.

[0211] Figure 13 illustrates the process flow for generating a database that identifies legal issues from information on multiple legally related content.

[0212] In step S1321, the data structure definition module 2046 of the server 20 obtains information on legal content by referring to the legal content database 212, etc.

[0213] Thus, the server 20 stores information on multiple legal content in its storage unit 202. Specifically, the server 20 stores information on multiple legal content in its storage unit 202, including at least one of the following: information on laws and regulations, information on precedents, information on legal books, and information on legal guidelines.

[0214] In step S1323, the data structure definition module 2046 of the server 20 generates a prompt that includes instructions to identify legal issues corresponding to the content of the legally relevant content. The data structure definition module 2046 may also generate a prompt that includes instructions to extract the portion of the content that formed the basis for identifying the legal issues corresponding to the content.

[0215] In step S1325, the data structure definition module 2046 of server 20 provides the generated prompt to the server 95 of the large-scale language model service, thereby obtaining the results of identifying legal issues regarding the content from the server 95 of the large-scale language model service.

[0216] In this way, the data structure definition module 2046 provides the content for prompt generation and the prompt generated for that content to the server 95 of the large-scale language model service, and by receiving the output results from the server 95 of the large-scale language model service, it obtains the results of identifying the legal issues for each piece of content.

[0217] In step S1327, the data structure definition module 2046 of the server 20 updates the legal content database 212 by associating the content with the identified legal issues.

[0218] Here, the data structure definition module 2046 may associate the extracted portion of the content with the identified legal issues and store them in the legal content database 212.

[0219] Furthermore, based on the legal issues associated with the content, the system may evaluate the legal issues in accordance with the usage of search results in a service that accepts searches for content (for example, a book browsing service server 93), and store the evaluation results of the legal issues in association with the content in a legal content database 212 (item "issue tag score").

[0220] In step S1329, the data structure definition module 2046 of the server 20 identifies a portion of the content relating to the same issue for multiple content items, defines a reference relationship for the identified portion, and updates the database. The data structure definition module 2046 outputs the information of the reference relationships defined for the issues of the multiple content items to the terminal 10.

[0221] Thus, the data structure definition module 2046 may define a graph-type data structure 214 by identifying parts of multiple content related to the same issue and defining reference relationships between the identified parts of the multiple content.

[0222] Furthermore, it is not necessary to update the database defining the reference relationships and then output the update to terminal 10; instead, the system may respond to a request from terminal 10 by indicating the status of the defined reference relationships in the database.

[0223] In step S1311, terminal 10 displays information indicating the reference relationships of content related to the issue.

[0224] Figure 14 shows the process flow for searching for content related to legal issues that address a user's question.

[0225] In step S1421, the question processing module 2044 of the server 20 receives input of a legal question and outputs an operation screen to the terminal 10 that displays the answer.

[0226] Server 20 stores information on multiple legal content items in its legal content database 212 and graph-type data structure 214 in its storage unit 202, and stores information on legal issues associated with each of the multiple content items.

[0227] The graph-type data structure 214 is used to receive legal questions from users, analyze the input questions to identify the legal issues corresponding to the questions, and, based on the identified legal issues, search for legal content related to those issues by referring to the information on legal issues stored in the graph-type data structure 214.

[0228] In the graph-type data structure 214, each of the multiple content items is configured to store information about the legal issue associated with the content and a portion of the content corresponding to that legal issue. In the graph-type data structure 214, reference relationships are defined between multiple content portions relating to the same issue. The graph-type data structure 214 is used in processes that search for multiple content items for which reference relationships are defined regarding a specified issue, based on those reference relationships.

[0229] In step S1411, terminal 10 receives input from the user regarding legal questions.

[0230] In step S1423, the question processing module 2044 of the server 20 identifies the legal issues corresponding to the input question by analyzing the input question. More specifically, the question processing module 2044 generates a prompt for the input question that includes instructions to identify the legal issues related to the question (for example, the prompt may include instructions to refer to a list of issues), and provides the generated prompt to the server 95 of the large-scale language model service. The server 95 then receives the output result of the large-scale language model service and identifies the legal issues related to the question.

[0231] In step S1425, the question processing module 2044 of the server 20 uses the issue identified for the question to refer to the legal content database 212 and the graph-type data structure 214 to search for content tagged with that issue.

[0232] In this way, the question processing module 2044 retrieves information on legal content related to a given legal issue by referring to information on the legal issue stored in the memory unit 202, based on the issue corresponding to the identified question.

[0233] In step S1427, the question processing module 2044 of the server 20 outputs the search results for content using issue tags to the terminal 10, including a portion of the content corresponding to the issue (the item "Issue Tagged Location" in the legal content database 212).

[0234] Here, the question processing module 2044 may provide the server 95 of the large-scale language model service with a prompt that includes the search results for content using the issue tags and the user's question received in steps S1411 and S1423, thereby causing the server 95 of the large-scale language model service to generate an answer to the user's question while referring to the search results for content using the issue tags, and outputting the generated answer to the terminal 10.

[0235] In step S1413, terminal 10 displays search results for legal content along with the issues corresponding to the question.

[0236] <8. Screen Example (Third Embodiment)> Figure 15 shows an example of an operation screen that responds to a user's question by searching various databases based on the corresponding legal issues.

[0237] The issue display area 1030 is an area that displays information about the issue corresponding to the question.

[0238] The issue display area 1030 corresponds to the process shown in step S1427 of Figure 14.

[0239] The issue summary display area 1032 is an area that displays a summary of the search results that were found as issues corresponding to the question.

[0240] In the illustrated example, the issue summary display area 1032 displays information about the issue retrieved by referring to the graph-type data structure 214 in response to the question, and the results of the answer generated by the large-scale language model service server 95 based on the search results and the question.

[0241] The individual issue display area 1034 is an area that displays the details of one of several issues.

[0242] Reference display area 1036 is the area that displays the source material (a legal book in the illustrated example) that supports the issue at hand.

[0243] In the illustrated example, the reference display area 1036 shows multiple legal books that were found by searching for a tag corresponding to the issue.

[0244] The evidence display area 1038 is an area that displays the section in legal textbooks that contains descriptions corresponding to the issue tags.

[0245] The evidence display area 1040 is an area that displays the section in legal textbooks that contains descriptions corresponding to the issue tags.

[0246] <Variation> The embodiments described above may also be combined in various ways.

[0247] Furthermore, each embodiment described above may be modified as follows.

[0248] (1) Sources of information on which answers to questions are based In the above description of the embodiment, a graph-type data structure is defined for laws, precedents, legal books, and legal guidelines, and an example is described in which a user's question is answered by searching using this graph-type data structure and outputting an answer that includes the search results.

[0249] In addition, similar to the information held by the operator's server 96, based on information that is not necessarily publicly disclosed to an unspecified large number of people, a graph-type data structure may be defined in the same manner as described above, and information of the same type as that held by the operator's server 96 may be retrieved based on the graph-type data structure for a question, and an answer to the question may be output. For example, within the operator's company, when the results of judgments made based on legal points (e.g., based on points of the Premiums and Gifts Display Law, etc.) regarding a specific case are accumulated, it may be possible to present a summary of the cases within the operator's company in the same manner as presenting the case precedents such as FIG. 12 above.

[0250] In addition to the above, similar to the SNS server 97, based on information that may be publicly disclosed to an unspecified large number of people, a graph-type data structure may be defined in the same manner as described above, and it may be used for reference when answering a user's question. For example, it may be possible to quote as the basis for an answer to a question the content posted by the account of a specific person.

[0251] The network is composed of various mobile communication systems constructed by the Internet, LAN, wireless base stations, etc. For example, the network includes 3G, 4G, 5G mobile communication systems, LTE (Long Term Evolution), a wireless network (e.g., Wi-Fi (registered trademark)) that can be connected to the Internet by a predetermined access point, etc. When connecting wirelessly, communication protocols such as Z-Wave (registered trademark), ZigBee (registered trademark), Bluetooth (registered trademark), etc. are included. When connecting by wire, the network also includes those directly connected by a USB (Universal Serial Bus) cable, etc.

[0252] Note that all or part of each hardware configuration may be distributed and provided to a plurality of computers, and by connecting them to each other via a network, a computer can be virtually realized. Thus, a computer is a concept that includes not only a single housing or a computer housed in a case but also a virtualized computer system.

[0253] A database refers to a relational database, which is for managing a table format table, called a master, defined structurally by rows and columns, by associating data sets with each other. In a database, a table is called a table, a master, a column of a table is called a column, and a row of a table is called a record. In a relational database, relationships between tables and masters can be set and associated.

[0254] Normally, each table and each master is set with a column that serves as a primary key for uniquely identifying a record, but setting a primary key for a column is not essential. The control unit can cause the processor 901 to add, delete, and update records in a specific table and master stored in the storage unit according to various programs.

[0255] Also, by storing data, various programs, and various databases in the storage unit, the information processing apparatus and information processing system according to the present disclosure can be regarded as being manufactured.

[0256] Note that the database and master in the present disclosure may include any data structure (list, dictionary, associative array, object, etc.) in which information is structurally defined. The data structure shall also include data that can be regarded as a data structure by combining data with functions, classes, methods, etc. described in any programming language.

[0257] Furthermore, each of the above-mentioned configurations, functions, processing units, processing means, etc., may be implemented in hardware, either partially or entirely, by designing them as integrated circuits, for example. The present invention can also be implemented by software program code that realizes the functions of the embodiment. In this case, a storage medium on which the program code is recorded is provided to a computer, and the processor of that computer reads the program code stored in the storage medium. In this case, the program code read from the storage medium itself realizes the functions of the embodiment described above, and the program code itself and the storage medium on which it is stored constitute the present invention. Examples of storage media used to supply such program code include flexible disks, CD-ROMs, DVD-ROMs, hard disks, SSDs, optical disks, magneto-optical disks, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, and the like.

[0258] Furthermore, the program code that implements the functions described in this embodiment can be implemented in a wide range of programming or scripting languages, such as assembler, C / C++, Perl, Shell, PHP, and Java (registered trademark).

[0259] Furthermore, the program code for the software that implements the functions of the embodiment may be distributed via a network and stored in a storage means such as a computer's hard disk or memory, or in a storage medium such as a CD-RW or CD-R, and the computer's processor may read and execute the program code stored in the storage means or storage medium.

[0260] The functions realized by the components described herein may be implemented in a circuit or processing circuitry, including general-purpose processors, application-specific processors, integrated circuits, ASICs (Application Specific Integrated Circuits), CPUs (a Central Processing Unit), conventional circuits, and / or combinations thereof, programmed to realize the functions described herein. A processor is considered to be a circuit or processing circuitry, including transistors and other circuits. A processor may be a programmed processor that executes a program stored in memory.

[0261] In this specification, circuitry, unit, and means are hardware programmed to perform or execute the functions described herein. Such hardware may be any hardware disclosed herein, or any hardware known to be programmed to perform or execute the functions described herein.

[0262] If the hardware is a processor that is considered to be a type of circuitry, then the circuitry, means, or unit is a combination of hardware and software used to constitute the hardware and / or processor.

[0263] While several embodiments of this disclosure have been described above, these embodiments can be implemented in a variety of other forms, and various omissions, substitutions, and modifications are permitted without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents.

[0264] (Note) The details described in each of the above embodiments are noted below.

[0265] (Appendix of the First Embodiment)

[0266] (Appendix 1) A program for operating a computer having one or more computer processors, wherein in a storage unit, information on a plurality of contents related to laws is held, the program causes one or more computer processors to, in each of the plurality of contents related to laws, information specifying a statute, information specifying a judicial precedent, information specifying a legal book, information specifying guidelines related to laws, perform a step of specifying at least a part including any of them, perform a step of storing, in the storage unit, the part specified in the specifying step in association with the content related to the specification, perform a step of extracting parts of the same type from the plurality of contents where the specified parts are the same, and defining a reference relationship between the contents for each of the extracted contents, perform a step of storing, in the storage unit, the reference relationship defined for the plurality of contents, a program.

[0267] (Appendix 2) In the specifying step, as information specifying a statute, at least any of the name or article of the statute, as information specifying a judicial precedent, the number of the judicial precedent, as information specifying a legal book, the title of the legal book, as information specifying guidelines related to laws, the name of the guidelines, a program according to Appendix 1, which specifies at least a part including any of them.

[0268] (Appendix 3) The program described in Appendix 2 defines reference relationships between content by extracting content with identical parts in the step of defining reference relationships, and then extracting content with the same name relating to the identified parts.

[0269] (Note 4) In the identification step, As information identifying the law, at least one of the law's name or provision, Information that identifies a case includes the case number, Information that identifies a legal book includes the title of the legal book, Information that identifies legal guidelines, such as the name of the guideline, Identify a certain amount of text that contains at least one of the following as a portion: The program described in Appendix 3 defines reference relationships between a fixed amount of text from multiple pieces of content in the step of defining reference relationships.

[0270] (Note 5) In addition, one or more computer processors, A step of evaluating a portion of the content based on its association with the content, in accordance with the usage of the search results in a service that accepts searches for content, A program described in any of the appendices 1 to 4, which performs the steps of: storing the evaluation results obtained by evaluating a portion of the content in a memory unit in association with the content; and

[0271] (Note 6) A data structure for information related to legal content, The memory unit stores information on multiple legal-related content. In the memory unit, for each of the multiple contents, Information that identifies laws and regulations, Information that identifies a case, Information identifying legal books, Information identifying legal guidelines, A portion containing at least one of these maintains the results of defining reference relationships between content of the same kind, A data structure for legal content information used in a process that, in response to a search request to retrieve information from multiple legal content pieces, identifies a portion corresponding to the search request and responds to the search request based on the reference relationships between the content pieces for the identified portion.

[0272] (Note 7) The data related to the data structure It is used in a process where, in order to receive a question input from the user and generate an answer to the question using a large-scale language model, it responds to a search request by searching for a portion of multiple content that corresponds to the question, and then returns search results for multiple content based on the reference relationships between the content for that portion. The data structure described in Appendix 6 involves a large-scale language model generating answers to questions based on search results from multiple content sources and user questions.

[0273] (Note 8) A method for operating a computer having one or more computer processors, The memory unit stores information on multiple legal-related content. The program runs on one or more computer processors. In each of the multiple pieces of legal content, Information that identifies laws and regulations, Information that identifies a case, Information identifying legal books, Information identifying legal guidelines, A step of identifying a portion that includes at least one of the following, The steps include: storing a portion identified in the identification step in a storage unit in association with the content related to the identification; The steps include: extracting content with similar parts from multiple pieces of content, and defining the reference relationships between the extracted content pieces; A method for performing the steps of storing defined reference relationships for multiple contents in a memory unit.

[0274] (Note 9) A method for operating a computer having one or more computer processors, The memory unit stores information on multiple legal-related content. In the memory unit, for each of the multiple contents, Information that identifies laws and regulations, Information that identifies a case, Information identifying legal books, Information identifying legal guidelines, A portion containing at least one of these maintains the results of defining reference relationships between content of the same kind, One or more computer processors, In response to a search request to retrieve information from multiple legal content, the system identifies a portion of the content that corresponds to the search request. A method for responding to search requests based on reference relationships between content for a specific portion.

[0275] (Note 10) An information processing device, The memory unit stores information on multiple legal-related content. The program runs on one or more computer processors. In each of the multiple pieces of legal content, Information that identifies laws and regulations, Information that identifies a case, Information identifying legal books, Information identifying legal guidelines, A step of identifying a portion that includes at least one of the following, The steps include: storing a portion identified in the identification step in a storage unit in association with the content related to the identification; The steps include: extracting content with similar parts from multiple pieces of content, and defining the reference relationships between the extracted content pieces; An information processing device that performs the steps of storing defined reference relationships for multiple contents in a memory unit.

[0276] (Note 11) An information processing device, The memory unit stores information on multiple legal-related content. In the memory unit, for each of the multiple contents, Information that identifies laws and regulations, Information that identifies a case, Information identifying legal books, Information identifying legal guidelines, A portion containing at least one of these maintains the results of defining reference relationships between content of the same kind, The control unit of the information processing device, In response to a search request to retrieve information from multiple legal content, the system identifies a portion of the content that corresponds to the search request. An information processing device that responds to search requests based on reference relationships between content for a specified portion.

[0277] (Notes on the second embodiment)

[0278] (Note 1) A program for operating a computer having one or more computer processors, The memory unit stores information on multiple legal-related content. The program runs on one or more computer processors. Steps to receive legal questions from users, The process involves searching for information on multiple legal-related content based on the entered question, Steps include identifying information on laws and regulations cited in the content of the search results, A program that performs the steps of presenting the user with information on identified laws and regulations.

[0279] (Note 2) The memory unit holds information on multiple legal content, including at least one of the following: legal books, legal guidelines, and articles from legal information media. The program described in Appendix 1, which searches for information from at least one of the following sources in the search step: legal books, legal guidelines, or articles from legal information media.

[0280] (Note 3) The memory unit holds information on multiple legal books, and each of the legal books is associated with a tag that indicates the content of that legal book. In the search step, The input questions will be tagged, A program described in either Appendix 1 or 2 that searches for legal books based on tags assigned to a question and tags associated with legal books.

[0281] (Note 4) In a service that allows users to browse multiple legal books, the score of tags associated with legal books is determined. The program described in Appendix 3 searches for legal books based on the score of tags associated with those legal books during the search step.

[0282] (Note 5) In the search step, tags are assigned to the questions, and a prompt is given to the large language model that includes instructions to assign legal tags to the questions. The data structure described in any of the appendices 3 to 4 is obtained by acquiring information on the tag assignment results output by the large language model, and then tags are assigned to the questions based on the acquired information on the tag assignment results.

[0283] (Note 6) The memory unit stores information on multiple legal-related content. In the memory unit, for each of the multiple contents, Information that identifies laws and regulations, Information that identifies a case, Information identifying legal books, Information identifying legal guidelines, A portion of the data that includes at least one of the following defines the reference relationships between similar content, and maintains this as a data structure related to the law. A program described in any of Appendix 1 to 5 that, in the search step, retrieves information on multiple legal content items related to a question by referring to legal data structures.

[0284] (Note 7) A method for operating a computer having one or more computer processors, The memory unit stores information on multiple legal-related content. The method involves one or more computer processors, Steps to receive legal questions from users, The process involves searching for information on multiple legal-related content based on the entered question, Steps include identifying information on laws and regulations cited in the content of the search results, A step and method for presenting information on identified laws and regulations to the user.

[0285] (Note 8) An information processing device, The memory unit stores information on multiple legal-related content. The control unit of the information processing device, Steps to receive legal questions from users, The process involves searching for information on multiple legal-related content based on the entered question, Steps include identifying information on laws and regulations cited in the content of the search results, An information processing device that performs the steps of presenting information on identified laws and regulations to the user.

[0286] (Notes on the third embodiment)

[0287] (Note 1) A program for operating a computer having one or more computer processors, The memory unit stores information on multiple legal-related content. The program runs on one or more computer processors. For each of several legally related pieces of content, a prompt is generated that includes instructions to identify the legal issues corresponding to the content of said piece; The process involves providing the content used to generate prompts and the prompts generated for that content to a large-scale language model, receiving the output of the large-scale language model, and obtaining results that identify the legal issues for each piece of content. The steps include: storing the identified legal issue in memory, associating it with the information of the content concerned; and executing the following steps. program.

[0288] (Note 2) In the memory unit, information on multiple legal-related contents is to be stored, Information on laws and regulations, Information on case law, Information on legal books, We hold information on legal guidelines, at least one of the following: In the generation step, a prompt is generated that includes instructions to extract a portion of the content that forms the basis for identifying the legal issues corresponding to the content, The program described in Appendix 1 stores in the memory a portion of the content extracted from the content in association with the identified legal issue, in the step of storing the issue in the memory.

[0289] (Note 3) The program described in Appendix 2, which, in the step of storing the issue in the memory unit, identifies parts of multiple content related to the same issue, defines reference relationships between the identified parts of the multiple content, and stores them in the memory unit.

[0290] (Note 4) In addition, one or more computer processors, Based on the legal issues associated with the content, the process involves evaluating the issues in accordance with how the search results are used in a service that accepts searches for the content, and A program described in any of the appendices 1 to 3, which performs the steps of: evaluating the points of contention and storing the evaluation results in a memory unit in association with the content.

[0291] (Note 5) A data structure for information related to legal content, The memory unit stores information on multiple legal-related content. The memory unit stores information on legal issues, associated with each of the multiple contents. A data structure for information on legal content used in a process that receives legal questions from users, analyzes the entered questions to identify the legal issues corresponding to the questions, and then searches for legal content related to those issues by referring to information on legal issues stored in a memory unit based on the identified issues.

[0292] (Note 6) In the memory unit, for each of the multiple contents, information on the legal issues associated with the content and a portion of the content corresponding to that legal issue are stored in association. In the memory section, reference relationships are defined between parts of multiple content related to the same issue. The data structure described in Appendix 5 is used for the process of searching for multiple pieces of content, each with a reference relationship defined for a specific issue, based on that reference relationship.

[0293] (Note 7) A program for operating a computer having one or more computer processors, The memory unit stores information on multiple legal-related content. The memory unit stores information on legal issues, associated with each of the multiple contents. The program runs on one or more computer processors. Steps to receive legal questions from users, The steps include: identifying the issues corresponding to the questions by analyzing the input questions; The steps include: searching for information on legal content related to a given legal issue by referring to information on the legal issue stored in the memory unit, based on the issue corresponding to the identified question; A program that performs the steps of presenting search results for content information to the user.

[0294] (Note 8) A method performed by a computer having one or more computer processors, The memory unit stores information on multiple legal-related content. The method involves one or more computer processors, For each of several legally related pieces of content, a prompt is generated that includes instructions to identify the legal issues corresponding to the content of said piece; The process involves providing the content used to generate prompts and the prompts generated for that content to a large-scale language model, receiving the output of the large-scale language model, and obtaining results that identify the legal issues for each piece of content. A method for performing the steps of storing identified legal issues in a memory unit in association with information about the content relating to those issues.

[0295] (Note 9) A method performed by a computer having one or more computer processors, The memory unit stores information on multiple legal-related content. The memory unit stores information on legal issues, associated with each of the multiple contents. The method involves one or more computer processors, Steps to receive legal questions from users, The steps include: identifying the issues corresponding to the questions by analyzing the input questions; The steps include: searching for information on legal content related to a given legal issue by referring to information on the legal issue stored in the memory unit, based on the issue corresponding to the identified question; The steps and methods for presenting search results for content information to the user.

[0296] (Note 10) An information processing device, The memory unit stores information on multiple legal-related content. The control unit of the information processing device, For each of several legally related pieces of content, a prompt is generated that includes instructions to identify the legal issues corresponding to the content of said piece; The process involves providing the content used to generate prompts and the prompts generated for that content to a large-scale language model, receiving the output of the large-scale language model, and obtaining results that identify the legal issues for each piece of content. An information processing device that performs the steps of storing identified legal issues in a memory unit in association with information on the content relating to those issues.

[0297] (Note 11) An information processing device, The memory unit stores information on multiple legal-related content. The memory unit stores information on legal issues, associated with each of the multiple contents. The control unit of the information processing device, Steps to receive legal questions from users, The steps include: identifying the issues corresponding to the questions by analyzing the input questions; The steps include: searching for information on legal content related to a given legal issue by referring to information on the legal issue stored in the memory unit, based on the issue corresponding to the identified question; An information processing device that performs the steps of presenting search results for content information to the user.

Claims

1. A program for operating a computer having one or more computer processors, The memory unit stores information on multiple legal-related content. The program is configured on one or more computer processors. For each of the multiple pieces of content related to the aforementioned law, a step is to generate a prompt that includes instructions to identify the legal issues corresponding to the content of said content, The steps include: providing the content for generating the prompt and the prompt generated for the content to a large-scale language model, and receiving the output results of the large-scale language model to obtain the results of identifying the legal issues for each of the contents; The steps of storing the identified legal issue in the storage unit in association with the information of the content relating to the identification, are performed. program.

2. The memory unit is configured to hold information on multiple contents related to the aforementioned law, Information on laws and regulations, Information on case law, Information on legal books, We hold information on legal guidelines, at least one of the following: In the generation step, the prompt is generated which includes an instruction to extract a portion of the content that forms the basis for identifying the legal issues corresponding to the content of the content, The program according to claim 1, wherein, in the step of storing the aforementioned issue in the storage unit, the extracted portion of the content and the identified legal issue are associated and stored in the storage unit.

3. The program according to claim 2, wherein in the step of storing the aforementioned issue in the storage unit, a portion of the plurality of contents relating to the same issue is identified, and a reference relationship is defined between the portions of the identified plurality of contents and stored in the storage unit.

4. The aforementioned one or more computer processors further include: A step of evaluating the legal issues associated with the content, in accordance with the usage of search results in a service that accepts operations to search for the content; The program according to claim 1, which performs the step of storing the evaluation results obtained by evaluating the aforementioned issues in association with the content in the storage unit.

5. A data structure for information related to legal content, The memory unit stores information on multiple legal-related content. The memory unit holds information on legal issues in association with each of the multiple contents, A data structure for information on legal content used in a process of searching for legal content related to a legal issue by receiving input questions from a user, analyzing the input questions to identify the issues corresponding to the questions, and referring to the information on the legal issues stored in the memory based on the identified issues.

6. The memory unit is configured to store, in each of the multiple contents, information on the legal issue associated with the content and a portion of the content corresponding to the legal issue, in association with each of the multiple contents. In the aforementioned storage unit, reference relationships are defined between parts of the multiple contents relating to the same issue. The data structure according to claim 5, which is used in the process of searching for the plurality of contents for which the reference relationship is defined with respect to the identified issue, based on the reference relationship.

7. A program for operating a computer having one or more computer processors, The memory unit stores information on multiple legal-related content. The memory unit holds information on legal issues in association with each of the multiple contents, The program is configured on one or more computer processors. Steps to receive legal questions from users, The steps include identifying the issues corresponding to the input questions by analyzing the questions, The steps include: searching for information on the content related to the law associated with the issue by referring to the information on the legal issue stored in the memory unit, based on the issue corresponding to the identified question; A program that performs the steps of presenting the user with search results for information on the aforementioned content.

8. A method performed by a computer having one or more computer processors, The memory unit stores information on multiple legal-related content. The above method involves one or more computer processors, For each of the multiple pieces of content related to the aforementioned law, a step is to generate a prompt that includes instructions to identify the legal issues corresponding to the content of said content, The steps include: providing the content for generating the prompt and the prompt generated for the content to a large-scale language model, and receiving the output results of the large-scale language model to obtain the results of identifying the legal issues for each of the contents; A method for performing the steps of: storing the identified legal issue in the storage unit in association with the information of the content relating to the identification.

9. A method performed by a computer having one or more computer processors, The memory unit stores information on multiple legal-related content. The memory unit holds information on legal issues in association with each of the multiple contents, The above method involves one or more computer processors, Steps to receive legal questions from users, The steps include identifying the issues corresponding to the input questions by analyzing the questions, The steps include: searching for information on the content related to the law associated with the issue by referring to the information on the legal issue stored in the memory unit, based on the issue corresponding to the identified question; A method for performing the steps of presenting the user with search results for information on the aforementioned content.

10. An information processing device, The memory unit stores information on multiple legal-related content. The control unit of the information processing device, For each of the multiple pieces of content related to the aforementioned law, a step is to generate a prompt that includes instructions to identify the legal issues corresponding to the content of said content, The steps include: providing the content for generating the prompt and the prompt generated for the content to a large-scale language model, and receiving the output results of the large-scale language model to obtain the results of identifying the legal issues for each of the contents; An information processing device that performs the steps of storing the identified legal issue in the storage unit in association with the information of the content relating to the identification.

11. An information processing device, The memory unit stores information on multiple legal-related content. The memory unit holds information on legal issues in association with each of the multiple contents, The control unit of the information processing device, Steps to receive legal questions from users, The steps include identifying the issues corresponding to the input questions by analyzing the questions, The steps include: searching for information on the content related to the law associated with the issue by referring to the information on the legal issue stored in the memory unit, based on the issue corresponding to the identified question; An information processing device that performs the steps of presenting the user with search results for information on the aforementioned content.

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