System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a natural language input; means for parsing the natural language input using natural language processing techniques; means for retrieving relevant laws and information from a legal database; means for generating an answer to a user based on the parsing results and the retrieved information; and means for providing the generated answer to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventionally, legal consultations and contract checking have required highly specialized knowledge, making it difficult for ordinary people to deal with these issues casually. Legal documents, in particular, contain many specialized terms, and in order to understand them, it is necessary to consult with a lawyer or other professional. However, lawyer fees are expensive and not readily available to everyone. As a result, even when faced with a legal problem, people are unable to receive appropriate advice, and the problem may become more serious. Therefore, the present invention aims to solve these problems and provide a system that allows anyone to easily obtain legal consultations and check contracts. [Means for solving the problem]

[0005] The present invention provides a system that receives natural language input, analyzes it using natural language processing technology, and searches for relevant laws and information from a legal database. It also includes a means for generating and providing a response to the user based on the analysis results and search information. It also enables checking of the contract by receiving image data of the contract, converting it into text data using image recognition technology, and analyzing the text data. It also includes a means for receiving additional questions from the user, performing natural language processing again, and generating and providing additional responses based on the reanalysis results. This allows users to appropriately solve legal problems even without specialized knowledge.

[0006] "Natural language input" refers to information expressed by a user in spoken or written language.

[0007] "Natural language processing technology" refers to technology that enables computers to understand, analyze, and process human language.

[0008] A "legal database" refers to a database that aggregates and makes searchable legal information such as various laws, statutes, and precedents, including the Constitution, Civil Code, Criminal Code, Commercial Code, Civil Procedure Code, and Criminal Procedure Code.

[0009] "Analysis results" refers to the results of analysis using natural language processing technology, and include relevant information and legal solutions to the user's question.

[0010] "Answer" refers to information generated to provide the analysis results to the user in an easy-to-understand manner.

[0011] "Image recognition technology" refers to the technology of analyzing image data and extracting necessary information.

[0012] "Text data" refers to character information converted from image data using image recognition technology.

[0013] "Contract checking" refers to the process of evaluating the contents of a contract from a legal perspective and pointing out problems and areas for improvement.

[0014] "Additional questions" refer to new questions or clarifications submitted by the user after the initial answer.

[0015] "Reanalysis" refers to the process of conducting a new analysis based on additional questions.

[0016] An "additional answer" refers to a new answer generated based on the results of reanalysis.

[0017] The "means for providing to the user" refers to a means for displaying the generated answers and additional answers to the user. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The system for implementing the present invention is a comprehensive platform for supporting users in obtaining legal advice and checking contracts. The elements that make up the system and their specific functions will be described below.

[0040] 1. User Input

[0041] Users can use devices such as smartphones or PCs to enter legal questions or upload photos of contracts. For example, a user can use a device to enter a question such as, "How will property be divided in the event of a divorce?" or take a photo of a newly signed contract and upload it.

[0042] 2. Natural Language Input and Text Analysis

[0043] The device transmits the user's questions and photos of the contract to the server, which then analyzes the received natural language input and, if necessary, extracts the contract's text using image recognition (OCR). For example, the image of the contract can be converted to text using OCR, and then analyzed for key legal elements using NLP technology.

[0044] 3. Natural Language Processing Technology

[0045] The server uses natural language processing (NLP) technology to analyze user questions and text data. The NLP engine extracts the intent of the question and important keywords, and automatically identifies the relevant legal field. For example, it extracts keywords such as "divorce" and "property division" and provides a corresponding legal interpretation.

[0046] 4. Legal Database Search

[0047] The server accesses a legal database to search for relevant laws and precedents. It retrieves legal provisions and case studies and provides appropriate answers to the user's questions. For example, it retrieves information about Article 768 of the Civil Code (property division) and provides specific examples.

[0048] 5. Generating and Providing Answers

[0049] Based on the search results and analysis results, the server generates an answer for the user. The generated answer is presented in a format that is easy for the user to understand. For example, an answer that includes a clear explanation such as, "According to Article 768 of the Civil Code, property division upon divorce is..." is created. The completed answer is sent from the server to the device and displayed to the user.

[0050] 6. Additional Questions and Reanalysis

[0051] Users are also provided with the ability to enter additional questions. If they have additional questions, they can submit them again using their terminal. The server then uses NLP technology to analyze the question, search for the necessary information in the legal database, and generate an additional answer. For example, a possible additional question would be, "What about a house in joint ownership?"

[0052] Specific examples

[0053] Example 1: Legal advice regarding divorce

[0054] When a user types "How will property be divided if I get divorced?" into their device, the device sends the question data to the server. The server uses an NLP engine to analyze the question and extracts the keywords "divorce" and "property division." It then searches for relevant provisions in a legal database and obtains information about Article 768 of the Civil Code. The server then generates an answer for the user based on the analysis results, such as "According to Article 768 of the Civil Code...," and sends it to the device, where it is displayed to the user.

[0055] Example 2: Contract Check

[0056] The user takes a photo of the contract on their device and uploads it. The device then sends the photo data to the server. The server uses OCR technology to convert the image into text data and analyzes the text data using NLP technology. The server searches a legal database for relevant legal provisions and commentary, and generates a check result based on the analysis results. For example, the server sends a response in the form of "The following clauses in this contract may be problematic..." and is displayed on the device.

[0057] The above is a specific embodiment of the present invention. This system enables users to quickly and accurately receive legal advice and check contracts, even if they do not have specialized knowledge.

[0058] The processing flow will be explained below.

[0059] Step 1:

[0060] The user enters a legal consultation question through the terminal or takes a photo of a contract and uploads it to the terminal.

[0061] Step 2:

[0062] The device sends the user's input data (text or image) to the server.

[0063] Step 3:

[0064] The server checks the received data and extracts the text data directly from the image if it is text data, or using OCR technology if it is image data.

[0065] Step 4:

[0066] The server uses natural language processing (NLP) technology to analyze the text data, extracting the intent of the question and important keywords from the contract.

[0067] Step 5:

[0068] The server accesses legal databases based on legal keywords and phrases to search for relevant statutes and precedents.

[0069] Step 6:

[0070] The server combines the search results with the analysis results to generate answers to the user's questions. In the case of contracts, the server also evaluates their contents from a legal perspective and points out problems and areas for improvement.

[0071] Step 7:

[0072] The server formats the generated response in a format that is easy for the user to understand and sends it to the terminal.

[0073] Step 8:

[0074] The terminal receives the answer sent from the server and displays it to the user, who can then check the answer and ask additional questions if necessary.

[0075] Step 9:

[0076] The user enters an additional question and sends it again from the terminal to the server.

[0077] Step 10:

[0078] The server receives the additional question and again analyzes it using natural language processing technology, searching the legal database again as needed to obtain new information.

[0079] Step 11:

[0080] The server generates a new answer based on the additional analysis results and sends it to the device.

[0081] Step 12:

[0082] The terminal displays the new answer to the user, and the process of additional questions and answers can be repeated until the user is satisfied.

[0083] The above is the specific flow of program processing when a user seeks legal advice or checks a contract.

[0084] Example 1

[0085] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0086] In modern society, legal consultations and contract checking are important tasks, but it is difficult for users without specialized knowledge to perform these tasks quickly and accurately. Furthermore, for general users to quickly obtain accurate legal information, they need the advice of lawyers with specialized knowledge and skills, which entails a significant burden in terms of cost and time. Therefore, there is a need for a system that allows even non-experts to easily obtain legal consultations and check contracts, while also providing accurate legal information quickly.

[0087] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0088] In this invention, the server includes means for receiving natural language input from a terminal, means for analyzing the natural language input using natural language processing technology, means for searching for relevant laws and information from a legal database, means for generating an answer for the user based on the analysis results and the search information using a generative AI model, and means for transmitting the generated answer to the terminal and providing it to the user. This allows even non-expert users to easily obtain legal advice or check contracts using a terminal such as a smartphone or PC, and enables them to quickly and accurately obtain legal information.

[0089] A "terminal" is a device for inputting and displaying information, and includes smartphones, personal computers, etc.

[0090] "Natural language input" refers to text data entered by a user in everyday language, including questions and consultation details.

[0091] "Natural language processing technology" is a technology that enables computers to understand and analyze human language, and includes tokenization, keyword extraction, grammar analysis, etc.

[0092] A "legal database" is a database containing information on laws, precedents, provisions, etc., and is used to obtain legal information.

[0093] A "generative AI model" is an artificial intelligence model that automatically generates natural-sounding sentences based on input data, creating answers based on specific conditions and context.

[0094] "Image recognition technology" refers to technology for extracting useful information from image data, and includes OCR (optical character recognition).

[0095] "OCR" is a technology that recognizes characters in an image and converts them into text data.

[0096] "User" refers to a person who uses the system to obtain legal advice or check contracts, and includes ordinary people without specialized knowledge.

[0097] A "contract" is a legally binding document that contains the contract terms and conditions.

[0098] A "follow-up question" is a question that a user enters after receiving an initial answer, and includes content that requests supplemental information.

[0099] "Reanalysis" refers to analyzing data again using natural language processing techniques after additional questions are entered.

[0100] The system for implementing the present invention is a comprehensive platform for supporting users in obtaining legal advice and checking contracts. The main elements that make up this system, their specific functions, and the hardware and software used will be described in detail below.

[0101] Hardware and Software Configuration

[0102] 1. User Input

[0103] Users can input legal questions using devices such as smartphones or PCs. For example, a user can input "How will property be divided in the event of divorce?" into the device. Users can also take photos of contracts and upload them to the system via the device.

[0104] 2. Data transfer to the server

[0105] The terminal transfers the input data (text data and image data of the contract) from the user to the server using the general HTTP protocol.

[0106] 3. Natural Language Input and Text Analysis

[0107] The server is equipped with software to analyze the received data. The text data is analyzed using a natural language processing engine (e.g., spaCy or BERT). The image data of the contract is converted to text using OCR technology (e.g., Tesseract), and then analyzed for key legal elements using natural language processing technology.

[0108] 4. Analysis using natural language processing technology

[0109] The server uses an NLP engine to analyze the user's questions and text data. This analysis involves tokenizing the input text and extracting keywords and intent. For example, it extracts the keywords "divorce" and "property division" to interpret the user's intent.

[0110] 5. Legal Database Search

[0111] The server accesses legal databases (e.g., LexisNexis or Westlaw) to search for relevant statutes and case law information, and generates queries to the database based on keywords extracted from the user to retrieve the required information.

[0112] 6. Answer Generation

[0113] The server generates answers for users using a generative AI model (e.g., GPT-3) based on the search results and analysis results. In this process, answers are created in a format that is easy for users to understand, based on legal data and relevant precedents.

[0114] 7. Displaying the Answer to the User

[0115] The generated answer is sent from the server to the device, and the received answer is displayed to the user. For example, the answer may be presented in the form of "According to Article 768 of the Civil Code, property division in the event of divorce is..."

[0116] 8. Additional Questions and Reanalysis

[0117] The user can enter additional questions. The device sends the new question to the server, which again uses NLP technology to analyze the question, search for the necessary information in the legal database, and generate an additional answer. For example, a possible additional question might be, "What about jointly owned houses?"

[0118] Specific examples

[0119] Example 1: Legal advice regarding divorce

[0120] When a user types "How will property be divided if I get divorced?" into their device, the device sends the question data to the server. The server uses an NLP engine to analyze the question and extracts the keywords "divorce" and "property division." It then searches for relevant provisions in a legal database and obtains information about Article 768 of the Civil Code. The server then generates an answer for the user based on the analysis results, such as "According to Article 768 of the Civil Code...," and sends it to the device, where it is displayed to the user.

[0121] Example 2: Contract Check

[0122] The user takes a photo of the contract on their device and uploads it. The device sends the photo data to the server. The server uses OCR technology to convert the image into text data. The server analyzes the text data using NLP technology. The server searches for relevant legal provisions and commentary in a legal database, and generates a check result based on the analysis. For example, the server sends a response to the device in the form of "The following clauses in this contract may be problematic..." The device then displays the check results it has received to the user.

[0123] Prompt Sentence Examples

[0124] "Please tell me about the division of assets in the event of divorce."

[0125] Please check the risk section of this contract.

[0126] The above is a concrete example of how to carry out the invention. By using this system, users can quickly and accurately receive legal advice and check contracts, even if they do not have specialized knowledge.

[0127] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0128] Step 1:

[0129] Users can use their devices to input legal questions, such as "How will property be divided in the event of a divorce?", or they can take a photo of a contract and upload it.

[0130] Input: User question text or contract image

[0131] Output: Sending data from the device to the server

[0132] Step 2:

[0133] The device transfers the entered question text and image data of the contract to the server using the standard HTTP protocol.

[0134] Input: Data (text or images) entered by the user into the device

[0135] Output: Data sent to the server

[0136] Step 3:

[0137] The server analyzes the received data. If it is text data, it is analyzed using natural language processing technology (NLP engine). If it is image data of a contract, OCR technology is used to extract text data from the image, and then NLP technology is used to analyze the text.

[0138] Input: Text or image data received by the server

[0139] Output: Analysis results including extracted keywords and intent

[0140] Step 4:

[0141] The server uses an NLP engine to perform a more detailed analysis of the text data from the user's questions and contracts. Specifically, it tokenizes the text and extracts keywords and context. For example, it identifies keywords such as "divorce" and "property division."

[0142] Input: Analysis results including extracted keywords and intent

[0143] Output: Detailed analysis results including user intent and keywords

[0144] Step 5:

[0145] The server accesses the legal database to search for relevant laws and information, generates a database query based on keywords, and retrieves the necessary legal provisions and case law information.

[0146] Input: Keywords and intent of detailed analysis results

[0147] Output: Laws and related information from legal databases

[0148] Step 6:

[0149] The server uses the generative AI model to generate answers for users based on search results and analysis results. Specifically, it generates answers in a format that is easy for users to understand, based on legal data and related precedents.

[0150] Input: Search results and analysis results

[0151] Output: The answer generated for the user

[0152] Step 7:

[0153] The server sends the generated answer to the device. The device displays the answer it receives to the user. For example, it might present the answer in a format such as, "According to Article 768 of the Civil Code, property division upon divorce is..."

[0154] Input: Generated answer

[0155] Output: Answer data sent to the device

[0156] Step 8:

[0157] The user enters an additional question. The terminal sends the new question data to the server.

[0158] Input: A new question from the user

[0159] Output: Send additional question data from the terminal to the server

[0160] Step 9:

[0161] The server then uses NLP techniques to parse the additional questions and retrieve the required information from legal databases.

[0162] Input: New question data

[0163] Output: Reparsed keywords, intent, and related legal information

[0164] Step 10:

[0165] The server generates an additional answer based on the reanalysis result and sends it to the terminal.

[0166] Input: Reanalysis result

[0167] Output: Sends the regenerated answer to the terminal

[0168] This will create a system that allows even non-expert users to easily obtain legal advice and check contracts.

[0169] (Application example 1)

[0170] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0171] Conventional legal support systems are limited to allowing users to ask legal questions or check contracts. However, in the case of autonomous vehicles in particular, it is necessary to provide immediate legal advice and take prompt and appropriate action when a traffic accident or legal trouble occurs, but such a system does not exist. In addition, the inability to quickly check the contents of contracts related to traffic accidents and systems makes it difficult for users to take appropriate action.

[0172] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0173] In this invention, the server includes means for receiving natural language input, means for analyzing the natural language input using natural language processing technology, means for searching for relevant laws and information from a legal database, means for generating a response to the user based on the analysis results and the search information, means for detecting the occurrence of a traffic accident and providing legal advice, means for confirming the contents of an insurance contract and procedures to be taken in the event of a traffic accident, and means for providing the generated response to the user. This makes it possible to provide immediate legal advice to an autonomous vehicle when a traffic accident occurs, and to assist in confirming the contents of the insurance contract and in the progress of procedures.

[0174] "Natural language input" is a method in which a user inputs questions or instructions using everyday polite language.

[0175] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0176] A "legal database" is a database that collects legal information such as laws and precedents.

[0177] "Means for detecting the occurrence of a traffic accident" refers to sensors and software that enable autonomous vehicles to automatically detect the occurrence of an accident.

[0178] "Means for providing legal advice" refers to a system that provides users with appropriate legal information in the event of an accident or legal trouble.

[0179] The "means for checking the contents of the insurance contract" is a system that analyzes the contents of the insurance contract and provides the user with appropriate information.

[0180] A "generative AI model" is an artificial intelligence model used to generate appropriate answers to input questions or instructions.

[0181] A "prompt sentence" is a question or instruction sentence that is input to a generative AI model.

[0182] A system for implementing the present invention is a legal assistance system installed in an autonomous vehicle. This system receives natural language input, analyzes it using natural language processing technology, and searches for relevant laws and information to provide legal advice in the event of a traffic accident. Specific embodiments are described below.

[0183] 1. User Input

[0184] Users can use the on-board display of the self-driving vehicle or devices such as smartphones to receive legal advice or check the details of their insurance contracts. For example, if they are involved in a traffic accident, they can input a question such as, "What are the legal procedures in the event of a traffic accident?"

[0185] 2. Natural Language Input and Text Analysis

[0186] The device forwards the user's question to the server, which then analyzes the received natural language input and, if necessary, uses image recognition (OCR) to extract the contract's text data. For example, an image of an insurance contract can be converted to text using OCR, and then NLP technology can be used to analyze the key legal elements.

[0187] 3. Natural language processing technology

[0188] The server uses natural language processing (NLP) technology to analyze the user's question and text data. The NLP engine extracts the intent of the question and important keywords, and automatically identifies the relevant legal field. For example, it extracts keywords such as "traffic accident" and "legal procedure" and provides a corresponding legal interpretation.

[0189] 4. Legal database search

[0190] The server accesses a legal database to search for relevant laws and precedents. It retrieves legal provisions and case studies and provides appropriate answers to the user's questions. For example, it retrieves laws related to responding to traffic accidents and provides specific examples.

[0191] 5. Generate and provide answers

[0192] Based on the search results and analysis results, the server generates an answer for the user. The generated answer is presented in a format that is easy for the user to understand. For example, an answer containing a clear explanation such as "If you are involved in a traffic accident, you should first contact the police and then report it to your insurance company" is created. The completed answer is sent from the server to the device and displayed to the user.

[0193] 6. Additional Questions and Reanalysis

[0194] Users are also provided with the ability to enter additional questions. If they have additional questions, they can submit them again using their terminal. The server then uses NLP technology to analyze the question, search for the necessary information in the legal database, and generate an additional answer. For example, a possible additional question would be, "How do we handle vehicles with joint ownership?"

[0195] 7. Specific Examples

[0196] In the event of a traffic accident, if the driver types the question "What are the legal procedures if I am involved in a traffic accident?" into the in-car display, the NLP engine will analyze the question and provide advice on the appropriate way to contact the police, how to report to the insurance company, etc. It is also possible to check the details of the insurance contract by taking a photo of it with a smartphone.

[0197] Using a generative AI model, the system generates appropriate answers and prompts for input questions and instructions. For example, by inputting the prompt "Please provide advice based on civil law and the Road Traffic Act regarding the legal procedures required in the event of a traffic accident," detailed legal advice is generated.

[0198] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0199] Step 1:

[0200] Users can use the on-board display of an autonomous vehicle or their smartphone to receive legal advice or check the details of their insurance policy. For example, if they are involved in a traffic accident, they can input a question such as, "What are the legal procedures in the event of a traffic accident?"

[0201] Input: Natural language input questions from the user

[0202] Output: Question text from user

[0203] Step 2:

[0204] The terminal forwards the user's question to the server.

[0205] Input: Question text submitted by the user

[0206] Output: The query data sent to the server

[0207] Step 3:

[0208] The server analyzes the received natural language input and, if necessary, uses image recognition technology (OCR) to extract the text data of the contract.

[0209] Input: Question text and optional contract image data to be sent

[0210] Output: Text data (question text and text data from the contract image)

[0211] Step 4:

[0212] The server uses natural language processing (NLP) technology to analyze user questions and text data, extracting the intent of the question and important keywords, and automatically identifying the relevant legal field.

[0213] Input: Text data

[0214] Output: Analysis results (keywords, intent)

[0215] Step 5:

[0216] The server accesses a legal database and searches for relevant statutes and case law information.

[0217] Input: Analysis results (keywords, intent)

[0218] Output: Search results (legal information, case law information)

[0219] Step 6:

[0220] Based on the search results and analysis results, the server generates an answer for the user, which is presented in a format that is easy for the user to understand.

[0221] Input: Search results, analysis results

[0222] Output: Generated answer text

[0223] Step 7:

[0224] The server sends the generated answer to the user's terminal and displays it to the user.

[0225] Input: Generated answer text

[0226] Output: Answer displayed on the user's terminal

[0227] Step 8:

[0228] The user is also provided with the ability to enter additional questions, and if there are any additional questions, the user can submit them again using the terminal.

[0229] Input: User-supplied question text

[0230] Output: Additional question data

[0231] Step 9:

[0232] The server then uses NLP techniques to analyze the additional questions, retrieve the necessary information from legal databases, and generate additional answers.

[0233] Input: Additional question data

[0234] Output: Reanalysis results and additional answer text

[0235] Step 10:

[0236] A generative AI model is used to generate prompts and provide them to the user, such as "Please provide advice based on civil law and the Road Traffic Act regarding the legal procedures required in the event of a traffic accident."

[0237] Input: Analysis results and instructions for generating prompt statements

[0238] Output: Generated prompt statement

[0239] The above are the specific processing steps of the system for realizing the application example.

[0240] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0241] The system for implementing the present invention is a platform that supports users in providing legal advice and checking contracts by combining an emotion engine. The configuration and specific operation of this system are described below.

[0242] 1. User Input

[0243] Users can use devices such as smartphones or PCs to enter legal questions or upload photos of contracts. For example, a user can use a device to enter a question such as, "How will property be divided in the event of a divorce?" or take a photo of a newly signed contract and upload it.

[0244] 2. Natural Language Input and Text Analysis

[0245] The device transfers the questions and photos of the contract submitted by the user to the server, where the server analyzes the received natural language input and, if necessary, extracts the text data of the contract using image recognition technology (OCR). The text data entered by the user is also analyzed.

[0246] 3. Natural Language Processing Technology

[0247] The server uses natural language processing (NLP) technology to analyze user questions and text data. The NLP engine extracts the intent of the question and important keywords, and automatically identifies the relevant legal field.

[0248] 4. Emotion Engine

[0249] The server is equipped with an emotion engine that recognizes emotions from the user's natural language input. The emotion engine works in conjunction with text analysis to evaluate emotions such as anger, anxiety, or joy that may be present in the user's input, and adjusts subsequent processing based on that information.

[0250] 5. Legal Database Search

[0251] The server accesses legal databases based on legal keywords and phrases, searches for relevant statutes and precedents, retrieves legal provisions and case studies, and provides relevant answers to the user's questions.

[0252] 6. Generating and Providing Answers

[0253] The server combines the search results, analysis results, and emotions recognized by the emotion engine to generate an answer for the user. The tone and content of the generated answer are adjusted based on the results of the emotion engine. For example, if the user is feeling anxious, the answer will be provided in more reassuring language. The answer generated by the server is sent to the device and displayed to the user.

[0254] 7. Additional Questions and Reanalysis

[0255] The user is also given the ability to enter additional questions. If there are additional questions, the user submits the question again using the terminal. The server again analyzes the question using NLP technology and an emotion engine, searches for the necessary information from the legal database, and generates an additional answer. For example, if the user asks an additional question such as, "What about jointly owned houses?", the same process is repeated.

[0256] Specific examples

[0257] Example 1: Legal advice regarding divorce

[0258] When a user types "How will property be divided in the event of divorce?" into their device, the device sends the question data to the server. The server uses an NLP engine to analyze the question and extracts keywords such as "divorce" and "property division." If the emotion engine senses anxiety from the user's input, it also reflects this in the analysis. It retrieves information about Article 768 (property division) of the Civil Code from a legal database, generates an answer in a reassuring format, such as "According to Article 768 of the Civil Code, property division in the event of divorce is...," and sends it to the device. The answer displayed to the user is written in a reassuring tone.

[0259] Example 2: Contract Check

[0260] The user takes a photo of the contract on their device and uploads it. The device then sends the photo data to the server. The server uses OCR technology to convert the image into text data and uses NLP technology to analyze the important contents of the contract. The emotion engine evaluates any concerns or anxieties the user may have before submitting the contract and reflects those emotions in the analysis results and feedback. The server searches for relevant legal provisions and commentary in a legal database and generates a check result based on the analysis results. For example, a response may be generated in a tone that reassures the user, such as, "This contract may have the following issues..." and is displayed on the device.

[0261] The above is a specific embodiment for carrying out the present invention. This system enables users without specialized knowledge to quickly and accurately receive legal advice and check contracts, and also to receive appropriate advice that takes into account the user's feelings.

[0262] The processing flow will be explained below.

[0263] Step 1:

[0264] A user uses a device to type in a legal question or upload a photo of a contract.

[0265] Step 2:

[0266] The device sends the user's input data (text or image) to the server.

[0267] Step 3:

[0268] The server checks the received data and extracts the text data directly from the image if it is text data, or using OCR technology if it is image data.

[0269] Step 4:

[0270] The server uses natural language processing (NLP) technology to analyze the text data, extracting the intent of the question and important keywords from the contract.

[0271] Step 5:

[0272] The server's emotion engine recognizes emotions from the user's text data, such as "anxiety," "anger," and "joy."

[0273] Step 6:

[0274] Based on the analysis results of the emotion engine, the server selects a method for generating an answer that reflects the user's emotional state.

[0275] Step 7:

[0276] The server accesses legal databases based on legal keywords and phrases to search for relevant statutes and precedents.

[0277] Step 8:

[0278] The server combines the search results, analysis results, and the results of the emotion engine to generate a response for the user, adjusting the tone and content of the response based on the results of the emotion engine.

[0279] Step 9:

[0280] The server formats the generated response in a format that is easy for the user to understand and sends it to the terminal.

[0281] Step 10:

[0282] The terminal receives the answer sent from the server and displays it to the user, who can then check the answer and ask additional questions if necessary.

[0283] Step 11:

[0284] The user enters an additional question and sends it again from the terminal to the server.

[0285] Step 12:

[0286] The server receives the additional questions and analyzes them again using natural language processing technology and an emotion engine, searching the legal database again as needed to obtain new information.

[0287] Step 13:

[0288] The server generates a new answer based on the additional analysis results and sends it to the device.

[0289] Step 14:

[0290] The terminal displays the new answer to the user, and the process of additional questions and answers can be repeated until the user is satisfied.

[0291] The above is the specific flow of program processing when a user seeks legal advice or checks a contract.

[0292] Example 2

[0293] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0294] Current legal consultation systems and contract review systems have the ability to analyze users' natural language input and provide relevant laws and information, but they do not take into account the user's emotions. As a result, they are unable to fully alleviate users' anxieties and doubts, making it difficult to provide appropriate advice. Furthermore, when reviewing contracts, feedback that understands emotions is required to ensure important information is not overlooked.

[0295] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0296] In this invention, the server includes means for receiving natural language input, means for analyzing the natural language input using natural language processing technology, means for searching for relevant laws and information from a legal database, means for generating an answer to the user based on the analysis result and the search information, means for providing the generated answer to the user, and means for identifying the user's emotions using an emotion engine and adjusting the generated answer in accordance with the user's emotions, thereby enabling more appropriate and reassuring legal consultations and contract checks that take the user's emotions into consideration.

[0297] "Natural language input" refers to a user sending information about legal advice or contract review to a server in natural language.

[0298] "Natural language processing technology" is a technology for analyzing natural language text data entered by a user and understanding its intent and meaning.

[0299] A "legal database" refers to a database that contains various information related to laws, precedents, and laws.

[0300] An "emotion engine" is a technology that identifies emotions from the user's input text and reflects that emotional information in the analysis results and generated answers.

[0301] "Contract image data" refers to an image file that a user photographs or uploads to check the contents of a contract.

[0302] "Image recognition technology" refers to technology for extracting text data from image data (e.g., optical character recognition (OCR)).

[0303] "Reanalysis" refers to the process of reapplying existing natural language processing technology to perform a new analysis based on additional questions from the user.

[0304] "Means for generating an answer" refers to a function that automatically creates an appropriate answer for the user based on the extracted related information and analysis results.

[0305] "Means for providing to the user" refers to a function for transmitting the generated answer to the user via the terminal and displaying it.

[0306] "Means for searching for relevant laws and information" refers to the function of searching for appropriate laws and precedents from legal databases using keywords extracted using natural language processing technology.

[0307] This invention relates to a platform that supports users in seeking legal advice and checking contracts. The system aims to analyze input data from users and provide appropriate answers using natural language processing technology and an emotion engine.

[0308] System configuration

[0309] The system consists of the following main components:

[0310] 1. User Interface

[0311] Device: Using a device such as a smartphone or computer, users input legal questions and images of contracts.

[0312] 2. Data transmission and reception

[0313] Terminal: Sends image data of questions and contracts entered by the user to the server via the Internet.

[0314] 3. Server Side

[0315] Server: Performs data processing, analysis, emotion evaluation, and answer generation. The server is equipped with the following technologies:

[0316] Natural language processing engine (NLP engine): A technology that analyzes text data such as user questions and contracts.

[0317] Emotion engine: A technology that identifies emotions from the text entered by the user and reflects that emotional information in the analysis results and generated answers.

[0318] Image recognition technology (OCR): A technology that extracts text data from image data of contracts.

[0319] Legal database: A database containing various information related to laws, precedents, and laws.

[0320] Processing flow

[0321] 1. User Input

[0322] Users use devices such as smartphones or computers to enter legal questions and upload photos of contracts.

[0323] For example, type "How will assets be divided in the event of divorce?" or upload a photo of the contract.

[0324] 2. Data transmission

[0325] The terminal transmits the input data from the user to the server.

[0326] 3. Data Analysis

[0327] The server analyzes the received natural language text data using a natural language processing engine and also extracts text data from contract images using OCR technology.

[0328] The NLP engine identifies the intent of the question and important keywords, such as "divorce" and "property division."

[0329] 4. Emotional assessment

[0330] The emotion engine installed on the server identifies emotions from the user's text, evaluating emotions such as "anxiety" or "anger," and reflecting this information in the analysis results.

[0331] 5. Legal Data Search

[0332] The server searches for relevant laws and precedents from a legal database based on the keywords identified by the NLP engine.

[0333] 6. Generating and Providing Answers

[0334] The server automatically generates an answer for the user based on the search results and analysis results.

[0335] The response generated is in a reassuring tone that reflects the emotional information provided by the emotion engine.

[0336] The generated answer is sent to the terminal and displayed to the user.

[0337] 7. Additional Questions and Reanalysis

[0338] If the user asks additional questions, that data is also sent from the terminal to the server.

[0339] The server then analyzes it again using NLP techniques and an emotion engine to generate additional answers.

[0340] Specific examples

[0341] Example 1: Legal advice regarding divorce

[0342] The user types into their device, "How will property be divided in the event of a divorce?" This question data is sent from the device to the server. The server uses an NLP engine to extract keywords such as "divorce" and "property division." If the emotion engine senses anxiety from the user's input, it also reflects this in the analysis. It retrieves information about Article 768 (property division) of the Civil Code from a legal database, generates a reassuring answer such as, "According to Article 768 of the Civil Code, property division in the event of a divorce is...," and sends it to the device. The answer displayed to the user is written in a reassuring tone.

[0343] Example 2: Contract Check

[0344] The user takes a photo of the contract on their device and uploads it. The device then sends the photo data to the server. The server uses OCR technology to convert the image into text data and uses NLP technology to analyze the important contents of the contract. An emotion engine evaluates any concerns the user may have before sending the contract and reflects those emotions in the analysis results and feedback. The server searches for relevant legal provisions and commentary in a legal database and generates a check result based on the analysis results. For example, a response is generated in a reassuring tone, such as, "The following points in this contract may be problematic..." and displayed on the device.

[0345] Prompt Sentence Examples

[0346] 1. Legal advice

[0347] How will property be divided when I get divorced?

[0348] What happens to a house that is in joint names?

[0349] 2. Check the contract

[0350] "I'm concerned about the contents of this contract. Please check it."

[0351] "What legal risks does this contract entail?"

[0352] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0353] Step 1: User Input

[0354] Users use a device (smartphone or PC) to enter legal questions or upload image data of contracts. The data entered is mainly text questions and image contracts. For example, a user might enter a question such as, "How will property be divided in the event of a divorce?" or take a photo of a newly signed contract and upload it.

[0355] Step 2: Sending data

[0356] The terminal sends the text data and image data entered by the user to the server. In this step, the input data (questions and contract images) is transferred to the server via the Internet. Specifically, the terminal divides the data into packets and sends them to a specific IP address on the server.

[0357] Step 3: Data Analysis (Natural Language Processing)

[0358] The server uses natural language processing technology (NLP) to analyze the received natural language text data. The input is the user's question text, and the output is extracted keywords and the intent of the question. Specifically, the server performs morphological analysis of the text and extracts important keywords (e.g., "divorce" and "property division"). In addition, image data of contracts is converted into text data using OCR technology. In this process, the image is scanned and converted into text data using a character recognition algorithm.

[0359] Step 4: Evaluate your emotions

[0360] The emotion engine installed on the server identifies emotions from the user's input text. The input is text after NLP processing, and the output is recognized emotional information (e.g., "anxiety" or "anger"). Specifically, the server analyzes emotional expressions in the text (e.g., "I'm worried" or "I'm anxious") to identify the user's emotion.

[0361] Step 5: Search for legal data

[0362] The server searches for relevant laws and precedents from a legal database based on the extracted keywords and phrases. The input is the extracted keywords (e.g., "divorce" and "property division"), and the output is the relevant laws and information (e.g., "Article 768 of the Civil Code"). Specifically, the server generates a database query, queries the legal database, and retrieves relevant provisions and precedents.

[0363] Step 6: Generate and serve answers

[0364] The server automatically generates an answer for the user based on the search results and analysis results. The input is legal information and emotional information, and the output is the answer provided to the user. The answer is generated in a tone that brings a sense of security, reflecting the emotional information generated by the emotion engine. For example, an answer containing reassuring language such as "According to Article 768 of the Civil Code, property division in the event of divorce is..." is generated. This answer is sent to the terminal and displayed to the user.

[0365] Step 7: Additional questions and reanalysis

[0366] If the user wants to enter an additional question, the question is sent again from the terminal to the server. The input is the new question data, and the output is a newly generated additional answer. The server again uses NLP technology and an emotion engine to analyze the question, searches the legal database for the necessary information again, and generates an additional answer. For example, in response to the additional question, "What about jointly owned houses?", the server generates an additional answer, "The specific legal treatment for jointly owned houses is..." This answer is also sent to the terminal and displayed to the user.

[0367] (Application example 2)

[0368] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0369] While conventional legal consultation systems can efficiently analyze customers' legal questions and the contents of contracts, they face the challenge of being unable to respond to customers' emotions. Furthermore, answers are often one-sided, which can leave customers feeling uneasy, or they may be unable to respond appropriately to follow-up questions. This creates a problem that makes it difficult to improve customer satisfaction.

[0370] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0371] In this invention, the server includes means for receiving natural language input, means for analyzing the natural language input using natural language processing technology, means for searching for relevant laws and information from a legal database, means for generating an answer to the user based on the analysis result and the search information, means for analyzing the user's emotions, and means for adjusting the tone of the answer based on the emotion analysis result, thereby making it possible to provide an answer that is sensitive to the customer's emotions and gives them a sense of security.

[0372] "Natural language input" is linguistic input provided by a user through speech or text.

[0373] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human language.

[0374] A "legal database" is a searchable database that collects laws, precedents, and legal information.

[0375] "Sentiment analysis" is a technology that extracts and evaluates emotions from text data.

[0376] "Tone adjustment" is the act of adjusting the expression of a sentence according to the user's emotions.

[0377] "Image data of a contract" is a digital image representation of a paper contract.

[0378] "Image recognition technology (OCR)" is a technology that analyzes characters in an image and extracts them as text data.

[0379] A "follow-up question" is a new question asked by the user following the initial question.

[0380] The system embodying the present invention is a platform for supporting legal counselors in brick-and-mortar stores. This system uses the following hardware and software:

[0381] Hardware

[0382] Smart glasses (e.g. smart devices)

[0383] Tablet devices (e.g., personal digital assistants)

[0384] Server (e.g. cloud infrastructure)

[0385] software

[0386] Natural language processing engines (e.g., natural language processing systems)

[0387] Image recognition technology (OCR) (e.g., optical character recognition tools)

[0388] Sentiment analysis engine (e.g., sentiment analysis tool)

[0389] Legal databases (e.g., legal information databases)

[0390] Program processing explanation

[0391] 1. Input acceptance:

[0392] Users can use smart glasses or a tablet to ask legal questions by voice or text, or upload a photo of a contract.

[0393] 2. Data transfer and analysis:

[0394] The device transfers voice input, text data, or image data to the server. The server converts the voice data into text (using a voice recognition API) and analyzes the text data. For image data, an optical character recognition tool is used to recognize characters and convert them into text data.

[0395] 3. Natural Language Processing:

[0396] A natural language processing system extracts key keywords from the input data and identifies relevant legal areas.

[0397] 4. Emotion analysis:

[0398] The emotion analysis tool recognizes emotions from the user's text data, assessing them as anxiety, anger, joy, etc., and uses the results to adjust the analysis results.

[0399] 5. Legal Database Search:

[0400] The server searches the legal information database based on the specified keywords and retrieves relevant laws and precedents.

[0401] 6. Generate and provide answers:

[0402] Based on the search results and sentiment analysis results, an appropriate answer is generated. The tone of the generated answer is adjusted according to the sentiment analysis results and provided to the user. For example, it may respond in a reassuring tone, such as "In accordance with Article 90 of the Civil Code, this provision may be invalid."

[0403] 7. Response to follow-up questions:

[0404] If the user has additional questions, natural language processing is performed again to generate additional answers. The process is repeated depending on the additional questions, providing answers in real time.

[0405] Specific examples

[0406] 1. Customer Questions:

[0407] A customer speaks to the smart glasses and says, "I'm confused by this clause in the contract."

[0408] 2. In-store counseling:

[0409] The smart glasses recognize the question through voice recognition and forward it to the server. The server analyzes the question and identifies important keywords such as "contract" and "article." The emotion analysis tool recognizes the customer's anxiety and notifies the counselor. The server then searches a legal information database to retrieve relevant laws and precedents. The server generates an answer based on the emotion analysis results and provides it to the counselor in a reassuring tone, such as "In accordance with Article 90 of the Civil Code, this article may be invalid."

[0410] Prompt Sentence Examples

[0411] I'm unclear about a particular clause in the contract and need clarification. Use sentiment analysis tools to generate legal advice with a tone that calms the customer's concerns.

[0412] This system will make legal consultations in brick-and-mortar stores more efficient and enable the provision of high-quality legal services that are sensitive to customer feelings.

[0413] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0414] Step 1:

[0415] The user inputs a question or an image of a contract through smart glasses or a tablet device, which generates voice data, text data, or image data.

[0416] Step 2:

[0417] The device transfers the voice data to the server. The voice data is input, and the server uses a voice recognition API to convert the voice data into text data. This text data is used for the next process.

[0418] Step 3:

[0419] The device transfers text data to the server. The input text data is analyzed and important keywords are extracted. This is done by a natural language processing system. Keywords are generated as output.

[0420] Step 4:

[0421] The device transfers image data to the server. Image recognition technology (OCR) is used to convert the image data into text data. This converted text data is then analyzed.

[0422] Step 5:

[0423] The server uses a natural language processing system to analyze the text data, and from the analyzed data, it identifies the relevant legal field, which is then used for the search in the next step.

[0424] Step 6:

[0425] The server uses an emotion analysis tool to extract the user's emotions from the text data. Emotions such as anxiety, anger, and joy are evaluated, and the results are output as data.

[0426] Step 7:

[0427] The server searches the legal information database based on the relevant legal field, retrieves relevant laws and precedents based on keywords, and outputs the information as data.

[0428] Step 8:

[0429] The server generates an appropriate answer based on the search results and sentiment analysis results. The answer is written and output in a tone that corresponds to the sentiment analysis results.

[0430] Step 9:

[0431] The server sends the generated answer to the terminal, the answer is received and displayed by the user, and the user enters additional questions if necessary.

[0432] Step 10:

[0433] The user enters a follow-up question, which the terminal forwards to the server.

[0434] Step 11:

[0435] The server then performs natural language processing again to analyze the additional question, extracting new keywords and outputting the analysis results.

[0436] Step 12:

[0437] The server generates an additional answer based on the analysis results, which is provided and output in a tone that corresponds to the sentiment analysis.

[0438] Step 13:

[0439] The server sends any additional responses to the device and displays them to the user. All data collected up to this point is logged and saved for future analysis and improvement.

[0440] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0441] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0442] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0443] [Second embodiment]

[0444] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0445] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0446] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0447] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0448] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0449] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0450] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0451] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0452] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0453] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0454] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0455] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0456] The system for implementing the present invention is a comprehensive platform for supporting users in obtaining legal advice and checking contracts. The elements that make up the system and their specific functions will be described below.

[0457] 1. User Input

[0458] Users can use devices such as smartphones or PCs to enter legal questions or upload photos of contracts. For example, a user can use a device to enter a question such as, "How will property be divided in the event of a divorce?" or take a photo of a newly signed contract and upload it.

[0459] 2. Natural Language Input and Text Analysis

[0460] The device transmits the user's questions and photos of the contract to the server, which then analyzes the received natural language input and, if necessary, extracts the contract's text using image recognition (OCR). For example, the image of the contract can be converted to text using OCR, and then analyzed for key legal elements using NLP technology.

[0461] 3. Natural Language Processing Technology

[0462] The server uses natural language processing (NLP) technology to analyze user questions and text data. The NLP engine extracts the intent of the question and important keywords, and automatically identifies the relevant legal field. For example, it extracts keywords such as "divorce" and "property division" and provides a corresponding legal interpretation.

[0463] 4. Legal Database Search

[0464] The server accesses a legal database to search for relevant laws and precedents. It retrieves legal provisions and case studies and provides appropriate answers to the user's questions. For example, it retrieves information about Article 768 of the Civil Code (property division) and provides specific examples.

[0465] 5. Generating and Providing Answers

[0466] Based on the search results and analysis results, the server generates an answer for the user. The generated answer is presented in a format that is easy for the user to understand. For example, an answer that includes a clear explanation such as, "According to Article 768 of the Civil Code, property division upon divorce is..." is created. The completed answer is sent from the server to the device and displayed to the user.

[0467] 6. Additional Questions and Reanalysis

[0468] Users are also provided with the ability to enter additional questions. If they have additional questions, they can submit them again using their terminal. The server then uses NLP technology to analyze the question, search for the necessary information in the legal database, and generate an additional answer. For example, a possible additional question would be, "What about a house in joint ownership?"

[0469] Specific examples

[0470] Example 1: Legal advice regarding divorce

[0471] When a user types "How will property be divided if I get divorced?" into their device, the device sends the question data to the server. The server uses an NLP engine to analyze the question and extracts the keywords "divorce" and "property division." It then searches for relevant provisions in a legal database and obtains information about Article 768 of the Civil Code. The server then generates an answer for the user based on the analysis results, such as "According to Article 768 of the Civil Code...," and sends it to the device, where it is displayed to the user.

[0472] Example 2: Contract Check

[0473] The user takes a photo of the contract on their device and uploads it. The device then sends the photo data to the server. The server uses OCR technology to convert the image into text data and analyzes the text data using NLP technology. The server searches a legal database for relevant legal provisions and commentary, and generates a check result based on the analysis results. For example, the server sends a response in the form of "The following clauses in this contract may be problematic..." and is displayed on the device.

[0474] The above is a specific embodiment of the present invention. This system enables users to quickly and accurately receive legal advice and check contracts, even if they do not have specialized knowledge.

[0475] The processing flow will be explained below.

[0476] Step 1:

[0477] The user enters a legal consultation question through the terminal or takes a photo of a contract and uploads it to the terminal.

[0478] Step 2:

[0479] The device sends the user's input data (text or image) to the server.

[0480] Step 3:

[0481] The server checks the received data and extracts the text data directly from the image if it is text data, or using OCR technology if it is image data.

[0482] Step 4:

[0483] The server uses natural language processing (NLP) technology to analyze the text data, extracting the intent of the question and important keywords from the contract.

[0484] Step 5:

[0485] The server accesses legal databases based on legal keywords and phrases to search for relevant statutes and precedents.

[0486] Step 6:

[0487] The server combines the search results with the analysis results to generate answers to the user's questions. In the case of contracts, the server also evaluates their contents from a legal perspective and points out problems and areas for improvement.

[0488] Step 7:

[0489] The server formats the generated response in a format that is easy for the user to understand and sends it to the terminal.

[0490] Step 8:

[0491] The terminal receives the answer sent from the server and displays it to the user, who can then check the answer and ask additional questions if necessary.

[0492] Step 9:

[0493] The user enters an additional question and sends it again from the terminal to the server.

[0494] Step 10:

[0495] The server receives the additional question and again analyzes it using natural language processing technology, searching the legal database again as needed to obtain new information.

[0496] Step 11:

[0497] The server generates a new answer based on the additional analysis results and sends it to the device.

[0498] Step 12:

[0499] The terminal displays the new answer to the user, and the process of additional questions and answers can be repeated until the user is satisfied.

[0500] The above is the specific flow of program processing when a user seeks legal advice or checks a contract.

[0501] Example 1

[0502] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0503] In modern society, legal consultations and contract checking are important tasks, but it is difficult for users without specialized knowledge to perform these tasks quickly and accurately. Furthermore, for general users to quickly obtain accurate legal information, they need the advice of lawyers with specialized knowledge and skills, which entails a significant burden in terms of cost and time. Therefore, there is a need for a system that allows even non-experts to easily obtain legal consultations and check contracts, while also providing accurate legal information quickly.

[0504] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0505] In this invention, the server includes means for receiving natural language input from a terminal, means for analyzing the natural language input using natural language processing technology, means for searching for relevant laws and information from a legal database, means for generating an answer for the user based on the analysis results and the search information using a generative AI model, and means for transmitting the generated answer to the terminal and providing it to the user. This allows even non-expert users to easily obtain legal advice or check contracts using a terminal such as a smartphone or PC, and enables them to quickly and accurately obtain legal information.

[0506] A "terminal" is a device for inputting and displaying information, and includes smartphones, personal computers, etc.

[0507] "Natural language input" refers to text data entered by a user in everyday language, including questions and consultation details.

[0508] "Natural language processing technology" is a technology that enables computers to understand and analyze human language, and includes tokenization, keyword extraction, grammar analysis, etc.

[0509] A "legal database" is a database that contains information on laws, precedents, provisions, etc., and is used to obtain information related to the law.

[0510] A "generative AI model" is an artificial intelligence model that automatically generates natural-sounding sentences based on input data, creating answers based on specific conditions and context.

[0511] "Image recognition technology" refers to technology for extracting useful information from image data, and includes OCR (optical character recognition).

[0512] "OCR" is a technology that recognizes characters in an image and converts them into text data.

[0513] "User" refers to a person who uses the system to obtain legal advice or check contracts, and includes ordinary people without specialized knowledge.

[0514] A "contract" is a legally binding document that contains the contract terms and conditions.

[0515] A "follow-up question" is a question that a user enters after receiving an initial answer, and includes content that requests supplemental information.

[0516] "Reanalysis" refers to analyzing data again using natural language processing techniques after additional questions are entered.

[0517] The system for implementing the present invention is a comprehensive platform for supporting users in obtaining legal advice and checking contracts. The main elements that make up this system, their specific functions, and the hardware and software used will be described in detail below.

[0518] Hardware and Software Configuration

[0519] 1. User Input

[0520] Users can input legal questions using devices such as smartphones or PCs. For example, a user can input "How will property be divided in the event of divorce?" into the device. Users can also take photos of contracts and upload them to the system via the device.

[0521] 2. Data transfer to the server

[0522] The terminal transfers the input data (text data and image data of the contract) from the user to the server using the general HTTP protocol.

[0523] 3. Natural Language Input and Text Analysis

[0524] The server is equipped with software to analyze the received data. The text data is analyzed using a natural language processing engine (e.g., spaCy or BERT). The image data of the contract is converted to text using OCR technology (e.g., Tesseract), and then analyzed for key legal elements using natural language processing technology.

[0525] 4. Analysis using natural language processing technology

[0526] The server uses an NLP engine to analyze the user's questions and text data. This analysis involves tokenizing the input text and extracting keywords and intent. For example, it extracts the keywords "divorce" and "property division" to interpret the user's intent.

[0527] 5. Legal Database Search

[0528] The server accesses legal databases (e.g., LexisNexis or Westlaw) to search for relevant statutes and case law information, and generates queries to the database based on keywords extracted from the user to retrieve the required information.

[0529] 6. Answer Generation

[0530] The server generates answers for users using a generative AI model (e.g., GPT-3) based on the search results and analysis results. In this process, answers are created in a format that is easy for users to understand, based on legal data and relevant precedents.

[0531] 7. Displaying the Answer to the User

[0532] The generated answer is sent from the server to the device, and the received answer is displayed to the user. For example, the answer may be presented in the form of "According to Article 768 of the Civil Code, property division in the event of divorce is..."

[0533] 8. Additional Questions and Reanalysis

[0534] The user can enter additional questions. The device sends the new question to the server, which again uses NLP technology to analyze the question, search for the necessary information in the legal database, and generate an additional answer. For example, a possible additional question might be, "What about jointly owned houses?"

[0535] Specific examples

[0536] Example 1: Legal advice regarding divorce

[0537] When a user types "How will property be divided if I get divorced?" into their device, the device sends the question data to the server. The server uses an NLP engine to analyze the question and extracts the keywords "divorce" and "property division." It then searches for relevant provisions in a legal database and obtains information about Article 768 of the Civil Code. The server then generates an answer for the user based on the analysis results, such as "According to Article 768 of the Civil Code...," and sends it to the device, where it is displayed to the user.

[0538] Example 2: Contract Check

[0539] The user takes a photo of the contract on their device and uploads it. The device sends the photo data to the server. The server converts the image into text data using OCR technology. The server analyzes the text data using NLP technology. The server searches for relevant legal provisions and commentary in a legal database, and generates a check result based on the analysis. For example, the server sends a response to the device in the form of, "The following clauses in this contract may be problematic..." The device then displays the check results it has received to the user.

[0540] Prompt Sentence Examples

[0541] "Please tell me about the division of assets in the event of divorce."

[0542] Please check the risk section of this contract.

[0543] The above is a concrete example of how to carry out the invention. By using this system, users can quickly and accurately receive legal advice and check contracts, even if they do not have specialized knowledge.

[0544] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0545] Step 1:

[0546] Users can use their devices to input legal questions, such as "How will property be divided in the event of a divorce?", or they can take a photo of a contract and upload it.

[0547] Input: User question text or contract image

[0548] Output: Sending data from the device to the server

[0549] Step 2:

[0550] The device transfers the entered question text and image data of the contract to the server using the standard HTTP protocol.

[0551] Input: Data (text or images) entered by the user into the device

[0552] Output: Data sent to the server

[0553] Step 3:

[0554] The server analyzes the received data. If it is text data, it is analyzed using natural language processing technology (NLP engine). If it is image data of a contract, OCR technology is used to extract text data from the image, and then NLP technology is used to analyze the text.

[0555] Input: Text or image data received by the server

[0556] Output: Analysis results including extracted keywords and intent

[0557] Step 4:

[0558] The server uses an NLP engine to perform a more detailed analysis of the text data from the user's questions and contracts. Specifically, it tokenizes the text and extracts keywords and context. For example, it identifies keywords such as "divorce" and "property division."

[0559] Input: Analysis results including extracted keywords and intent

[0560] Output: Detailed analysis results including user intent and keywords

[0561] Step 5:

[0562] The server accesses the legal database to search for relevant laws and information, generates a database query based on keywords, and retrieves the necessary legal provisions and case law information.

[0563] Input: Keywords and intent of detailed analysis results

[0564] Output: Laws and related information from legal databases

[0565] Step 6:

[0566] The server uses the generative AI model to generate answers for users based on search results and analysis results. Specifically, it generates answers in a format that is easy for users to understand, based on legal data and related precedents.

[0567] Input: Search results and analysis results

[0568] Output: The answer generated for the user

[0569] Step 7:

[0570] The server sends the generated answer to the device. The device displays the answer it receives to the user. For example, it might present the answer in a format such as, "According to Article 768 of the Civil Code, property division upon divorce is..."

[0571] Input: Generated answer

[0572] Output: Answer data sent to the device

[0573] Step 8:

[0574] The user enters an additional question. The terminal sends the new question data to the server.

[0575] Input: A new question from the user

[0576] Output: Send additional question data from the terminal to the server

[0577] Step 9:

[0578] The server then uses NLP techniques to parse the additional questions and retrieve the required information from legal databases.

[0579] Input: New question data

[0580] Output: Reparsed keywords, intent, and related legal information

[0581] Step 10:

[0582] The server generates an additional answer based on the reanalysis result and sends it to the terminal.

[0583] Input: Reanalysis result

[0584] Output: Sends the regenerated answer to the terminal

[0585] This will create a system that allows even non-expert users to easily obtain legal advice and check contracts.

[0586] (Application example 1)

[0587] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0588] Conventional legal support systems are limited to allowing users to ask legal questions or check contracts. However, in the case of autonomous vehicles in particular, it is necessary to provide immediate legal advice and take prompt and appropriate action when a traffic accident or legal trouble occurs, but such a system does not exist. In addition, the inability to quickly check the contents of contracts related to traffic accidents and systems makes it difficult for users to take appropriate action.

[0589] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0590] In this invention, the server includes means for receiving natural language input, means for analyzing the natural language input using natural language processing technology, means for searching for relevant laws and information from a legal database, means for generating a response to the user based on the analysis results and the search information, means for detecting the occurrence of a traffic accident and providing legal advice, means for confirming the contents of an insurance contract and procedures to be taken in the event of a traffic accident, and means for providing the generated response to the user. This makes it possible to provide immediate legal advice to an autonomous vehicle when a traffic accident occurs, and to assist in confirming the contents of the insurance contract and in the progress of procedures.

[0591] "Natural language input" is a method in which a user inputs questions or instructions using everyday polite language.

[0592] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0593] A "legal database" is a database that collects legal information such as laws and precedents.

[0594] "Means for detecting the occurrence of a traffic accident" refers to sensors and software that enable autonomous vehicles to automatically detect the occurrence of an accident.

[0595] "Means for providing legal advice" refers to a system that provides users with appropriate legal information in the event of an accident or legal trouble.

[0596] The "means for checking the contents of the insurance contract" is a system that analyzes the contents of the insurance contract and provides the user with appropriate information.

[0597] A "generative AI model" is an artificial intelligence model used to generate appropriate answers to input questions or instructions.

[0598] A "prompt sentence" is a question or instruction sentence that is input to a generative AI model.

[0599] A system for implementing the present invention is a legal assistance system installed in an autonomous vehicle. This system receives natural language input, analyzes it using natural language processing technology, and searches for relevant laws and information to provide legal advice in the event of a traffic accident. Specific embodiments are described below.

[0600] 1. User Input

[0601] Users can use the on-board display of the self-driving vehicle or devices such as smartphones to receive legal advice or check the details of their insurance contracts. For example, if they are involved in a traffic accident, they can input a question such as, "What are the legal procedures in the event of a traffic accident?"

[0602] 2. Natural Language Input and Text Analysis

[0603] The device forwards the user's question to the server, which then analyzes the received natural language input and, if necessary, uses image recognition (OCR) to extract the contract's text data. For example, an image of an insurance contract can be converted to text using OCR, and then NLP technology can be used to analyze the key legal elements.

[0604] 3. Natural language processing technology

[0605] The server uses natural language processing (NLP) technology to analyze the user's question and text data. The NLP engine extracts the intent of the question and important keywords, and automatically identifies the relevant legal field. For example, it extracts keywords such as "traffic accident" and "legal procedure" and provides a corresponding legal interpretation.

[0606] 4. Legal database search

[0607] The server accesses a legal database to search for relevant laws and precedents. It retrieves legal provisions and case studies and provides appropriate answers to the user's questions. For example, it retrieves laws related to responding to traffic accidents and provides specific examples.

[0608] 5. Generate and provide answers

[0609] Based on the search results and analysis results, the server generates an answer for the user. The generated answer is presented in a format that is easy for the user to understand. For example, an answer containing a clear explanation such as "If you are involved in a traffic accident, you should first contact the police and then report it to your insurance company" is created. The completed answer is sent from the server to the device and displayed to the user.

[0610] 6. Additional Questions and Reanalysis

[0611] Users are also provided with the ability to enter additional questions. If they have additional questions, they can submit them again using their terminal. The server then uses NLP technology to analyze the question, search for the necessary information in the legal database, and generate an additional answer. For example, a possible additional question would be, "How do we handle vehicles with joint ownership?"

[0612] 7. Specific Examples

[0613] In the event of a traffic accident, if the driver types the question "What are the legal procedures if I am involved in a traffic accident?" into the in-car display, the NLP engine will analyze the question and provide advice on the appropriate way to contact the police, how to report to the insurance company, etc. It is also possible to check the details of the insurance contract by taking a photo of it with a smartphone.

[0614] Using a generative AI model, the system generates appropriate answers and prompts for input questions and instructions. For example, by inputting the prompt "Please provide advice based on civil law and the Road Traffic Act regarding the legal procedures required in the event of a traffic accident," detailed legal advice is generated.

[0615] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0616] Step 1:

[0617] Users can use the on-board display of an autonomous vehicle or their smartphone to receive legal advice or check the details of their insurance policy. For example, if they are involved in a traffic accident, they can input a question such as, "What are the legal procedures in the event of a traffic accident?"

[0618] Input: Natural language input questions from the user

[0619] Output: Question text from user

[0620] Step 2:

[0621] The terminal forwards the user's question to the server.

[0622] Input: Question text submitted by the user

[0623] Output: The query data sent to the server

[0624] Step 3:

[0625] The server analyzes the received natural language input and, if necessary, uses image recognition technology (OCR) to extract the text data of the contract.

[0626] Input: Question text and optional contract image data to be sent

[0627] Output: Text data (question text and text data from the contract image)

[0628] Step 4:

[0629] The server uses natural language processing (NLP) technology to analyze user questions and text data, extracting the intent of the question and important keywords, and automatically identifying the relevant legal field.

[0630] Input: Text data

[0631] Output: Analysis results (keywords, intent)

[0632] Step 5:

[0633] The server accesses a legal database and searches for relevant statutes and case law information.

[0634] Input: Analysis results (keywords, intent)

[0635] Output: Search results (legal information, case law information)

[0636] Step 6:

[0637] Based on the search results and analysis results, the server generates answers for the user, which are presented in a format that is easy for the user to understand.

[0638] Input: Search results, analysis results

[0639] Output: Generated answer text

[0640] Step 7:

[0641] The server sends the generated answer to the user's terminal and displays it to the user.

[0642] Input: Generated answer text

[0643] Output: Answer displayed on the user's terminal

[0644] Step 8:

[0645] The user is also provided with the ability to enter additional questions, and if there are any additional questions, the user can submit them again using the terminal.

[0646] Input: User-supplied question text

[0647] Output: Additional question data

[0648] Step 9:

[0649] The server then uses NLP techniques to analyze the additional questions, retrieve the necessary information from legal databases, and generate additional answers.

[0650] Input: Additional question data

[0651] Output: Reanalysis results and additional answer text

[0652] Step 10:

[0653] A generative AI model is used to generate prompts and provide them to the user. For example, a prompt such as "Please provide advice based on civil law and the Road Traffic Act regarding the legal procedures required in the event of a traffic accident" can be generated.

[0654] Input: Analysis results and instructions for generating prompt statements

[0655] Output: Generated prompt statement

[0656] The above are the specific processing steps of the system for realizing the application example.

[0657] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0658] The system for implementing the present invention is a platform that supports users in providing legal advice and checking contracts by combining an emotion engine. The configuration and specific operation of this system are described below.

[0659] 1. User Input

[0660] Users can use devices such as smartphones or PCs to enter legal questions or upload photos of contracts. For example, a user can use a device to enter a question such as, "How will property be divided in the event of a divorce?" or take a photo of a newly signed contract and upload it.

[0661] 2. Natural Language Input and Text Analysis

[0662] The device transfers the questions and photos of the contract submitted by the user to the server, where the server analyzes the received natural language input and, if necessary, extracts the text data of the contract using image recognition technology (OCR). The text data entered by the user is also analyzed.

[0663] 3. Natural Language Processing Technology

[0664] The server uses natural language processing (NLP) technology to analyze user questions and text data. The NLP engine extracts the intent of the question and important keywords, and automatically identifies the relevant legal field.

[0665] 4. Emotion Engine

[0666] The server is equipped with an emotion engine that recognizes emotions from the user's natural language input. The emotion engine works in conjunction with text analysis to evaluate emotions such as anger, anxiety, or joy that may be present in the user's input, and adjusts subsequent processing based on that information.

[0667] 5. Legal Database Search

[0668] The server accesses legal databases based on legal keywords and phrases, searches for relevant statutes and precedents, retrieves legal provisions and case studies, and provides relevant answers to the user's questions.

[0669] 6. Generating and Providing Answers

[0670] The server combines the search results, analysis results, and emotions recognized by the emotion engine to generate an answer for the user. The tone and content of the generated answer are adjusted based on the results of the emotion engine. For example, if the user is feeling anxious, the answer will be provided in more reassuring language. The answer generated by the server is sent to the device and displayed to the user.

[0671] 7. Additional Questions and Reanalysis

[0672] The user is also given the ability to enter additional questions. If there are additional questions, the user submits the question again using the terminal. The server again analyzes the question using NLP technology and an emotion engine, searches for the necessary information from the legal database, and generates an additional answer. For example, if the user asks an additional question such as, "What about jointly owned houses?", the same process is repeated.

[0673] Specific examples

[0674] Example 1: Legal advice regarding divorce

[0675] When a user types "How will property be divided in the event of divorce?" into their device, the device sends the question data to the server. The server uses an NLP engine to analyze the question and extracts keywords such as "divorce" and "property division." If the emotion engine senses anxiety from the user's input, it also reflects this in the analysis. It retrieves information about Article 768 (property division) of the Civil Code from a legal database, generates an answer in a reassuring format, such as "According to Article 768 of the Civil Code, property division in the event of divorce is...," and sends it to the device. The answer displayed to the user is written in a reassuring tone.

[0676] Example 2: Contract Check

[0677] The user takes a photo of the contract on their device and uploads it. The device then sends the photo data to the server. The server uses OCR technology to convert the image into text data and uses NLP technology to analyze the important contents of the contract. The emotion engine evaluates any concerns or anxieties the user may have before submitting the contract and reflects those emotions in the analysis results and feedback. The server searches for relevant legal provisions and commentary in a legal database and generates a check result based on the analysis results. For example, a response may be generated in a tone that reassures the user, such as, "This contract may have the following issues..." and is displayed on the device.

[0678] The above is a specific embodiment for carrying out the present invention. With this system, users can quickly and accurately receive legal advice and contract checks, even without specialized knowledge, and can also receive appropriate advice that takes into account the user's feelings.

[0679] The processing flow will be explained below.

[0680] Step 1:

[0681] A user uses a device to type in a legal question or upload a photo of a contract.

[0682] Step 2:

[0683] The device sends the user's input data (text or image) to the server.

[0684] Step 3:

[0685] The server checks the received data and extracts the text data directly from the image if it is text data, or using OCR technology if it is image data.

[0686] Step 4:

[0687] The server uses natural language processing (NLP) technology to analyze the text data, extracting the intent of the question and important keywords from the contract.

[0688] Step 5:

[0689] The server's emotion engine recognizes emotions from the user's text data, such as "anxiety," "anger," and "joy."

[0690] Step 6:

[0691] Based on the analysis results of the emotion engine, the server selects a method for generating an answer that reflects the user's emotional state.

[0692] Step 7:

[0693] The server accesses legal databases based on legal keywords and phrases to search for relevant statutes and precedents.

[0694] Step 8:

[0695] The server combines the search results, analysis results, and the results of the emotion engine to generate a response for the user, adjusting the tone and content of the response based on the results of the emotion engine.

[0696] Step 9:

[0697] The server formats the generated response in a format that is easy for the user to understand and sends it to the terminal.

[0698] Step 10:

[0699] The terminal receives the answer sent from the server and displays it to the user, who can then check the answer and ask additional questions if necessary.

[0700] Step 11:

[0701] The user enters an additional question and sends it again from the terminal to the server.

[0702] Step 12:

[0703] The server receives the additional questions and analyzes them again using natural language processing technology and an emotion engine, searching the legal database again as needed to obtain new information.

[0704] Step 13:

[0705] The server generates a new answer based on the additional analysis results and sends it to the device.

[0706] Step 14:

[0707] The terminal displays the new answer to the user, and the process of additional questions and answers can be repeated until the user is satisfied.

[0708] The above is the specific flow of program processing when a user seeks legal advice or checks a contract.

[0709] Example 2

[0710] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0711] Current legal consultation systems and contract review systems have the ability to analyze users' natural language input and provide relevant laws and information, but they do not take into account the user's emotions. As a result, they are unable to fully alleviate users' anxieties and doubts, making it difficult to provide appropriate advice. Furthermore, when reviewing contracts, feedback that understands emotions is required to ensure important information is not overlooked.

[0712] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0713] In this invention, the server includes means for receiving natural language input, means for analyzing the natural language input using natural language processing technology, means for searching for relevant laws and information from a legal database, means for generating an answer to the user based on the analysis result and the search information, means for providing the generated answer to the user, and means for identifying the user's emotions using an emotion engine and adjusting the generated answer in accordance with the user's emotions, thereby enabling more appropriate and reassuring legal consultations and contract checks that take the user's emotions into consideration.

[0714] "Natural language input" refers to a user sending information about legal advice or contract review to a server in natural language.

[0715] "Natural language processing technology" is a technology for analyzing natural language text data entered by a user and understanding its intent and meaning.

[0716] A "legal database" refers to a database that contains various information related to laws, precedents, and laws.

[0717] An "emotion engine" is a technology that identifies emotions from the user's input text and reflects that emotional information in the analysis results and generated answers.

[0718] "Contract image data" refers to an image file that a user photographs or uploads to check the contents of a contract.

[0719] "Image recognition technology" refers to technology for extracting text data from image data (e.g., optical character recognition (OCR)).

[0720] "Reanalysis" refers to the process of reapplying existing natural language processing technology to perform a new analysis based on additional questions from the user.

[0721] "Means for generating an answer" refers to a function that automatically creates an appropriate answer for the user based on the extracted related information and analysis results.

[0722] "Means for providing to the user" refers to a function for transmitting the generated answer to the user via the terminal and displaying it.

[0723] "Means for searching for relevant laws and information" refers to the function of searching for appropriate laws and precedents from legal databases using keywords extracted using natural language processing technology.

[0724] This invention relates to a platform that supports users in seeking legal advice and checking contracts. The system aims to analyze input data from users and provide appropriate answers using natural language processing technology and an emotion engine.

[0725] System configuration

[0726] The system consists of the following main components:

[0727] 1. User Interface

[0728] Device: Using a device such as a smartphone or computer, users input legal questions and images of contracts.

[0729] 2. Data transmission and reception

[0730] Terminal: Sends image data of questions and contracts entered by the user to the server via the Internet.

[0731] 3. Server Side

[0732] Server: Performs data processing, analysis, emotion evaluation, and answer generation. The server is equipped with the following technologies:

[0733] Natural language processing engine (NLP engine): A technology that analyzes text data such as user questions and contracts.

[0734] Emotion engine: A technology that identifies emotions from the text entered by the user and reflects that emotional information in the analysis results and generated answers.

[0735] Image recognition technology (OCR): A technology that extracts text data from image data of contracts.

[0736] Legal database: A database containing various information related to laws, precedents, and laws.

[0737] Processing flow

[0738] 1. User Input

[0739] Users use devices such as smartphones or computers to enter legal questions and upload photos of contracts.

[0740] For example, type "How will assets be divided in the event of divorce?" or upload a photo of the contract.

[0741] 2. Data transmission

[0742] The terminal transmits the input data from the user to the server.

[0743] 3. Data Analysis

[0744] The server analyzes the received natural language text data using a natural language processing engine and also extracts text data from contract images using OCR technology.

[0745] The NLP engine identifies the intent of the question and important keywords, such as "divorce" and "property division."

[0746] 4. Emotional Assessment

[0747] The emotion engine installed on the server identifies emotions from the user's text, evaluating emotions such as "anxiety" or "anger," and reflecting this information in the analysis results.

[0748] 5. Legal Data Search

[0749] The server searches for relevant laws and precedents from a legal database based on the keywords identified by the NLP engine.

[0750] 6. Generating and Providing Answers

[0751] The server automatically generates an answer for the user based on the search results and analysis results.

[0752] The response generated is in a reassuring tone that reflects the emotional information provided by the emotion engine.

[0753] The generated answer is sent to the terminal and displayed to the user.

[0754] 7. Additional Questions and Reanalysis

[0755] If the user asks additional questions, that data is also sent from the terminal to the server.

[0756] The server then analyzes it again using NLP techniques and an emotion engine to generate additional answers.

[0757] Specific examples

[0758] Example 1: Legal advice regarding divorce

[0759] The user types into their device, "How will property be divided in the event of a divorce?" This question data is sent from the device to the server. The server uses an NLP engine to extract keywords such as "divorce" and "property division." If the emotion engine senses anxiety from the user's input, it also reflects this in the analysis. It retrieves information about Article 768 (property division) of the Civil Code from a legal database, generates a reassuring answer such as, "According to Article 768 of the Civil Code, property division in the event of a divorce is...," and sends it to the device. The answer displayed to the user is written in a reassuring tone.

[0760] Example 2: Contract Check

[0761] The user takes a photo of the contract on their device and uploads it. The device then sends the photo data to the server. The server uses OCR technology to convert the image into text data and uses NLP technology to analyze the important contents of the contract. An emotion engine evaluates any concerns the user may have before sending the contract and reflects those emotions in the analysis results and feedback. The server searches for relevant legal provisions and commentary in a legal database and generates a check result based on the analysis results. For example, a response is generated in a reassuring tone, such as, "The following points in this contract may be problematic..." and displayed on the device.

[0762] Prompt Sentence Examples

[0763] 1. Legal advice

[0764] How will property be divided when I get divorced?

[0765] What happens to a house that is in joint names?

[0766] 2. Check the contract

[0767] "I'm concerned about the contents of this contract. Please check it."

[0768] "What legal risks does this contract entail?"

[0769] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0770] Step 1: User Input

[0771] Users use a device (smartphone or PC) to enter legal questions or upload image data of contracts. The data entered is mainly text questions and image contracts. For example, a user might enter a question such as, "How will property be divided in the event of a divorce?" or take a photo of a newly signed contract and upload it.

[0772] Step 2: Sending data

[0773] The terminal sends the text data and image data entered by the user to the server. In this step, the input data (questions and contract images) is transferred to the server via the Internet. Specifically, the terminal divides the data into packets and sends them to a specific IP address on the server.

[0774] Step 3: Data analysis (natural language processing)

[0775] The server uses natural language processing technology (NLP) to analyze the received natural language text data. The input is the user's question text, and the output is extracted keywords and the intent of the question. Specifically, the server performs morphological analysis of the text and extracts important keywords (e.g., "divorce" and "property division"). In addition, image data of contracts is converted into text data using OCR technology. In this process, the image is scanned and converted into text data using a character recognition algorithm.

[0776] Step 4: Evaluate your emotions

[0777] The emotion engine installed on the server identifies emotions from the user's input text. The input is text after NLP processing, and the output is recognized emotional information (e.g., "anxiety" or "anger"). Specifically, the server analyzes emotional expressions in the text (e.g., "I'm worried" or "I'm anxious") to identify the user's emotion.

[0778] Step 5: Search for legal data

[0779] The server searches for relevant laws and precedents from a legal database based on the extracted keywords and phrases. The input is the extracted keywords (e.g., "divorce" and "property division"), and the output is the relevant laws and information (e.g., "Article 768 of the Civil Code"). Specifically, the server generates a database query, queries the legal database, and retrieves relevant provisions and precedents.

[0780] Step 6: Generate and serve answers

[0781] The server automatically generates an answer for the user based on the search results and analysis results. The input is legal information and emotional information, and the output is the answer provided to the user. The answer is generated in a tone that brings a sense of security, reflecting the emotional information generated by the emotion engine. For example, an answer containing reassuring language such as "According to Article 768 of the Civil Code, property division in the event of divorce is..." is generated. This answer is sent to the terminal and displayed to the user.

[0782] Step 7: Additional questions and reanalysis

[0783] If the user wants to enter an additional question, the question is sent again from the terminal to the server. The input is the new question data, and the output is a newly generated additional answer. The server again uses NLP technology and an emotion engine to analyze the question, searches the legal database for the necessary information again, and generates an additional answer. For example, in response to the additional question, "What about jointly owned houses?", the server generates an additional answer, "The specific legal treatment for jointly owned houses is..." This answer is also sent to the terminal and displayed to the user.

[0784] (Application example 2)

[0785] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0786] While conventional legal consultation systems can efficiently analyze customers' legal questions and the contents of contracts, they face the challenge of being unable to respond to customers' emotions. Furthermore, answers are often one-sided, which can leave customers feeling uneasy, and the system may be unable to respond appropriately to follow-up questions. This creates a problem that makes it difficult to improve customer satisfaction.

[0787] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0788] In this invention, the server includes means for receiving natural language input, means for analyzing the natural language input using natural language processing technology, means for searching for relevant laws and information from a legal database, means for generating an answer to the user based on the analysis result and the search information, means for analyzing the user's emotions, and means for adjusting the tone of the answer based on the emotion analysis result, thereby making it possible to provide an answer that is sensitive to the customer's emotions and gives them a sense of security.

[0789] "Natural language input" is linguistic input provided by a user through speech or text.

[0790] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human language.

[0791] A "legal database" is a searchable database that collects laws, precedents, and legal information.

[0792] "Sentiment analysis" is a technology that extracts and evaluates emotions from text data.

[0793] "Tone adjustment" is the act of adjusting the expression of a sentence according to the user's emotions.

[0794] "Image data of a contract" is a digital image representation of a paper contract.

[0795] "Image recognition technology (OCR)" is a technology that analyzes characters in an image and extracts them as text data.

[0796] A "follow-up question" is a new question asked by the user following the initial question.

[0797] The system embodying the present invention is a platform for supporting legal counselors in brick-and-mortar stores. This system uses the following hardware and software:

[0798] Hardware

[0799] Smart glasses (e.g. smart devices)

[0800] Tablet devices (e.g., personal digital assistants)

[0801] Server (e.g. cloud infrastructure)

[0802] software

[0803] Natural language processing engines (e.g., natural language processing systems)

[0804] Image recognition technology (OCR) (e.g., optical character recognition tools)

[0805] Sentiment analysis engine (e.g., sentiment analysis tool)

[0806] Legal databases (e.g., legal information databases)

[0807] Program processing explanation

[0808] 1. Input acceptance:

[0809] Users can use smart glasses or a tablet to ask legal questions by voice or text, or upload a photo of a contract.

[0810] 2. Data transfer and analysis:

[0811] The device transfers voice input, text data, or image data to the server. The server converts the voice data into text (using a voice recognition API) and analyzes the text data. For image data, an optical character recognition tool is used to recognize characters and convert them into text data.

[0812] 3. Natural Language Processing:

[0813] A natural language processing system extracts key keywords from the input data and identifies relevant legal areas.

[0814] 4. Emotion analysis:

[0815] The emotion analysis tool recognizes emotions from the user's text data, assessing them as anxiety, anger, joy, etc., and uses the results to adjust the analysis results.

[0816] 5. Legal Database Search:

[0817] The server searches the legal information database based on the specified keywords and retrieves relevant laws and precedents.

[0818] 6. Generate and provide answers:

[0819] Based on the search results and sentiment analysis results, an appropriate answer is generated. The tone of the generated answer is adjusted according to the sentiment analysis results and provided to the user. For example, it may respond in a reassuring tone, such as "In accordance with Article 90 of the Civil Code, this provision may be invalid."

[0820] 7. Follow-up questions:

[0821] If the user has additional questions, natural language processing is performed again to generate additional answers. The process is repeated depending on the additional questions, providing answers in real time.

[0822] Specific examples

[0823] 1. Customer Questions:

[0824] A customer speaks to the smart glasses and says, "I'm confused by this clause in the contract."

[0825] 2. In-store counseling:

[0826] The smart glasses recognize the question through voice recognition and forward it to the server. The server analyzes the question and identifies important keywords such as "contract" and "article." The emotion analysis tool recognizes the customer's anxiety and notifies the counselor. The server searches a legal information database to retrieve relevant laws and precedents. The server generates an answer based on the emotion analysis results and provides it to the counselor in a reassuring tone, such as "In accordance with Article 90 of the Civil Code, this article may be invalid."

[0827] Prompt Sentence Examples

[0828] I'm unclear about a particular clause in the contract and need clarification. Use sentiment analysis tools to generate legal advice with a tone that calms the customer's concerns.

[0829] This system will make legal consultations in brick-and-mortar stores more efficient and enable the provision of high-quality legal services that are sensitive to customer feelings.

[0830] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0831] Step 1:

[0832] The user inputs a question or an image of a contract through smart glasses or a tablet device, which generates voice data, text data, or image data.

[0833] Step 2:

[0834] The device transfers the voice data to the server. The voice data is input, and the server uses a voice recognition API to convert the voice data into text data. This text data is used for the next process.

[0835] Step 3:

[0836] The device transfers text data to the server. The input text data is analyzed and important keywords are extracted. This is done by a natural language processing system. Keywords are generated as output.

[0837] Step 4:

[0838] The device transfers image data to the server. Image recognition technology (OCR) is used to convert the image data into text data. This converted text data is then analyzed.

[0839] Step 5:

[0840] The server uses a natural language processing system to analyze the text data, and from the analyzed data, it identifies the relevant legal field, which is then used for the search in the next step.

[0841] Step 6:

[0842] The server uses an emotion analysis tool to extract the user's emotions from the text data. Emotions such as anxiety, anger, and joy are evaluated, and the results are output as data.

[0843] Step 7:

[0844] The server searches the legal information database based on the relevant legal field, retrieves relevant laws and precedents based on keywords, and outputs the information as data.

[0845] Step 8:

[0846] The server generates an appropriate answer based on the search results and sentiment analysis results. The answer is written and output in a tone that corresponds to the sentiment analysis results.

[0847] Step 9:

[0848] The server sends the generated answer to the terminal, the answer is received and displayed by the user, and the user enters additional questions if necessary.

[0849] Step 10:

[0850] The user enters a follow-up question, which the terminal forwards to the server.

[0851] Step 11:

[0852] The server then performs natural language processing again to analyze the additional question, extracting new keywords and outputting the analysis results.

[0853] Step 12:

[0854] The server generates an additional answer based on the analysis results, which is provided and output in a tone that corresponds to the sentiment analysis.

[0855] Step 13:

[0856] The server sends any additional responses to the device and displays them to the user. All data collected up to this point is logged and saved for future analysis and improvement.

[0857] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0858] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0859] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0860] [Third embodiment]

[0861] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0862] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0863] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0864] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0865] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0866] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0867] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0868] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0869] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0870] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0871] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0872] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0873] The system for implementing the present invention is a comprehensive platform for supporting users in obtaining legal advice and checking contracts. The elements that make up the system and their specific functions will be described below.

[0874] 1. User Input

[0875] Users can use devices such as smartphones or PCs to enter legal questions or upload photos of contracts. For example, a user can use a device to enter a question such as, "How will property be divided in the event of a divorce?" or take a photo of a newly signed contract and upload it.

[0876] 2. Natural Language Input and Text Analysis

[0877] The device transmits the user's questions and photos of the contract to the server, which then analyzes the received natural language input and, if necessary, extracts the contract's text using image recognition (OCR). For example, the image of the contract can be converted to text using OCR, and then analyzed for key legal elements using NLP technology.

[0878] 3. Natural Language Processing Technology

[0879] The server uses natural language processing (NLP) technology to analyze user questions and text data. The NLP engine extracts the intent of the question and important keywords, and automatically identifies the relevant legal field. For example, it extracts keywords such as "divorce" and "property division" and provides a corresponding legal interpretation.

[0880] 4. Legal Database Search

[0881] The server accesses a legal database to search for relevant laws and precedents. It retrieves legal provisions and case studies and provides appropriate answers to the user's questions. For example, it retrieves information about Article 768 of the Civil Code (property division) and provides specific examples.

[0882] 5. Generating and Providing Answers

[0883] Based on the search results and analysis results, the server generates an answer for the user. The generated answer is presented in a format that is easy for the user to understand. For example, an answer that includes a clear explanation such as, "According to Article 768 of the Civil Code, property division upon divorce is..." is created. The completed answer is sent from the server to the device and displayed to the user.

[0884] 6. Additional Questions and Reanalysis

[0885] Users are also provided with the ability to enter additional questions. If they have additional questions, they can submit them again using their terminal. The server then uses NLP technology to analyze the question, search for the necessary information in the legal database, and generate an additional answer. For example, a possible additional question would be, "What about a house in joint ownership?"

[0886] Specific examples

[0887] Example 1: Legal advice regarding divorce

[0888] When a user types "How will property be divided if I get divorced?" into their device, the device sends the question data to the server. The server uses an NLP engine to analyze the question and extracts the keywords "divorce" and "property division." It then searches for relevant provisions in a legal database and obtains information about Article 768 of the Civil Code. The server then generates an answer for the user based on the analysis results, such as "According to Article 768 of the Civil Code...," and sends it to the device, where it is displayed to the user.

[0889] Example 2: Contract Check

[0890] The user takes a photo of the contract on their device and uploads it. The device then sends the photo data to the server. The server uses OCR technology to convert the image into text data and analyzes the text data using NLP technology. The server searches a legal database for relevant legal provisions and commentary, and generates a check result based on the analysis results. For example, the server sends a response in the form of "The following clauses in this contract may be problematic..." and is displayed on the device.

[0891] The above is a specific embodiment of the present invention. This system enables users to quickly and accurately receive legal advice and check contracts, even if they do not have specialized knowledge.

[0892] The processing flow will be explained below.

[0893] Step 1:

[0894] The user enters a legal consultation question through the terminal or takes a photo of a contract and uploads it to the terminal.

[0895] Step 2:

[0896] The device sends the user's input data (text or image) to the server.

[0897] Step 3:

[0898] The server checks the received data and extracts the text data directly from the image if it is text data, or using OCR technology if it is image data.

[0899] Step 4:

[0900] The server uses natural language processing (NLP) technology to analyze the text data, extracting the intent of the question and important keywords from the contract.

[0901] Step 5:

[0902] The server accesses legal databases based on legal keywords and phrases to search for relevant statutes and precedents.

[0903] Step 6:

[0904] The server combines the search results with the analysis results to generate answers to the user's questions. In the case of contracts, the server also evaluates their contents from a legal perspective and points out problems and areas for improvement.

[0905] Step 7:

[0906] The server formats the generated response in a format that is easy for the user to understand and sends it to the terminal.

[0907] Step 8:

[0908] The terminal receives the answer sent from the server and displays it to the user, who can then check the answer and ask additional questions if necessary.

[0909] Step 9:

[0910] The user enters an additional question and sends it again from the terminal to the server.

[0911] Step 10:

[0912] The server receives the additional question and again analyzes it using natural language processing technology, searching the legal database again as needed to obtain new information.

[0913] Step 11:

[0914] The server generates a new answer based on the additional analysis results and sends it to the device.

[0915] Step 12:

[0916] The terminal displays the new answer to the user, and the process of additional questions and answers can be repeated until the user is satisfied.

[0917] The above is the specific flow of program processing when a user seeks legal advice or checks a contract.

[0918] Example 1

[0919] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0920] In modern society, legal consultations and contract checking are important tasks, but it is difficult for users without specialized knowledge to perform these tasks quickly and accurately. Furthermore, for general users to quickly obtain accurate legal information, they need the advice of lawyers with specialized knowledge and skills, which entails a significant burden in terms of cost and time. Therefore, there is a need for a system that allows even non-experts to easily obtain legal consultations and check contracts, while also providing accurate legal information quickly.

[0921] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0922] In this invention, the server includes means for receiving natural language input from a terminal, means for analyzing the natural language input using natural language processing technology, means for searching for relevant laws and information from a legal database, means for generating an answer for the user based on the analysis results and the search information using a generative AI model, and means for transmitting the generated answer to the terminal and providing it to the user. This allows even non-expert users to easily obtain legal advice or check contracts using a terminal such as a smartphone or PC, and enables them to quickly and accurately obtain legal information.

[0923] A "terminal" is a device for inputting and displaying information, and includes smartphones, personal computers, etc.

[0924] "Natural language input" refers to text data entered by a user in everyday language, including questions and consultation details.

[0925] "Natural language processing technology" is a technology that enables computers to understand and analyze human language, and includes tokenization, keyword extraction, grammar analysis, etc.

[0926] A "legal database" is a database that contains information on laws, precedents, provisions, etc., and is used to obtain information related to the law.

[0927] A "generative AI model" is an artificial intelligence model that automatically generates natural-sounding sentences based on input data, creating answers based on specific conditions and context.

[0928] "Image recognition technology" refers to technology for extracting useful information from image data, and includes OCR (optical character recognition).

[0929] "OCR" is a technology that recognizes characters in an image and converts them into text data.

[0930] "User" refers to a person who uses the system to obtain legal advice or check contracts, and includes ordinary people without specialized knowledge.

[0931] A "contract" is a legally binding document that contains the contract terms and conditions.

[0932] A "follow-up question" is a question that a user enters after receiving an initial answer, and includes content that requests supplemental information.

[0933] "Reanalysis" refers to analyzing data again using natural language processing techniques after additional questions are entered.

[0934] The system for implementing the present invention is a comprehensive platform for supporting users in obtaining legal advice and checking contracts. The main elements that make up this system, their specific functions, and the hardware and software used will be described in detail below.

[0935] Hardware and Software Configuration

[0936] 1. User Input

[0937] Users can input legal questions using devices such as smartphones or PCs. For example, a user can input "How will property be divided in the event of divorce?" into the device. Users can also take photos of contracts and upload them to the system via the device.

[0938] 2. Data transfer to the server

[0939] The terminal transfers the input data (text data and image data of the contract) from the user to the server using the general HTTP protocol.

[0940] 3. Natural Language Input and Text Analysis

[0941] The server is equipped with software to analyze the received data. The text data is analyzed using a natural language processing engine (e.g., spaCy or BERT). The image data of the contract is converted to text using OCR technology (e.g., Tesseract), and then analyzed for key legal elements using natural language processing technology.

[0942] 4. Analysis using natural language processing technology

[0943] The server uses an NLP engine to analyze the user's questions and text data. This analysis involves tokenizing the input text and extracting keywords and intent. For example, it extracts the keywords "divorce" and "property division" to interpret the user's intent.

[0944] 5. Legal Database Search

[0945] The server accesses legal databases (e.g., LexisNexis or Westlaw) to search for relevant statutes and case law information, and generates queries to the database based on keywords extracted from the user to retrieve the required information.

[0946] 6. Answer Generation

[0947] The server generates answers for users using a generative AI model (e.g., GPT-3) based on the search results and analysis results. In this process, answers are created in a format that is easy for users to understand, based on legal data and relevant precedents.

[0948] 7. Displaying the Answer to the User

[0949] The generated answer is sent from the server to the device, and the received answer is displayed to the user. For example, the answer may be presented in the form of "According to Article 768 of the Civil Code, property division in the event of divorce is..."

[0950] 8. Additional Questions and Reanalysis

[0951] The user can enter additional questions. The device sends the new question to the server, which again uses NLP technology to analyze the question, search for the necessary information in the legal database, and generate an additional answer. For example, a possible additional question might be, "What about jointly owned houses?"

[0952] Specific examples

[0953] Example 1: Legal advice regarding divorce

[0954] When a user types "How will property be divided if I get divorced?" into their device, the device sends the question data to the server. The server uses an NLP engine to analyze the question and extracts the keywords "divorce" and "property division." It then searches for relevant provisions in a legal database and obtains information about Article 768 of the Civil Code. The server then generates an answer for the user based on the analysis results, such as "According to Article 768 of the Civil Code...," and sends it to the device, where it is displayed to the user.

[0955] Example 2: Contract Check

[0956] The user takes a photo of the contract on their device and uploads it. The device sends the photo data to the server. The server converts the image into text data using OCR technology. The server analyzes the text data using NLP technology. The server searches for relevant legal provisions and commentary in a legal database, and generates a check result based on the analysis. For example, the server sends a response to the device in the form of, "The following clauses in this contract may be problematic..." The device then displays the check results it has received to the user.

[0957] Prompt Sentence Examples

[0958] "Please tell me about the division of assets in the event of divorce."

[0959] Please check the risk section of this contract.

[0960] The above is a concrete example of how to carry out the invention. By using this system, users can quickly and accurately receive legal advice and check contracts, even if they do not have specialized knowledge.

[0961] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0962] Step 1:

[0963] Users can use their devices to input legal questions, such as "How will property be divided in the event of a divorce?", or they can take a photo of a contract and upload it.

[0964] Input: User question text or contract image

[0965] Output: Sending data from the device to the server

[0966] Step 2:

[0967] The device transfers the entered question text and image data of the contract to the server using the standard HTTP protocol.

[0968] Input: Data (text or images) entered by the user into the device

[0969] Output: Data sent to the server

[0970] Step 3:

[0971] The server analyzes the received data. If it is text data, it is analyzed using natural language processing technology (NLP engine). If it is image data of a contract, OCR technology is used to extract text data from the image, and then NLP technology is used to analyze the text.

[0972] Input: Text or image data received by the server

[0973] Output: Analysis results including extracted keywords and intent

[0974] Step 4:

[0975] The server uses an NLP engine to perform a more detailed analysis of the text data from the user's questions and contracts. Specifically, it tokenizes the text and extracts keywords and context. For example, it identifies keywords such as "divorce" and "property division."

[0976] Input: Analysis results including extracted keywords and intent

[0977] Output: Detailed analysis results including user intent and keywords

[0978] Step 5:

[0979] The server accesses the legal database to search for relevant laws and information, generates a database query based on keywords, and retrieves the necessary legal provisions and case law information.

[0980] Input: Keywords and intent of detailed analysis results

[0981] Output: Laws and related information from legal databases

[0982] Step 6:

[0983] The server uses the generative AI model to generate answers for users based on search results and analysis results. Specifically, it generates answers in a format that is easy for users to understand, based on legal data and related precedents.

[0984] Input: Search results and analysis results

[0985] Output: The answer generated for the user

[0986] Step 7:

[0987] The server sends the generated answer to the device. The device displays the answer it receives to the user. For example, it might present the answer in a format such as, "According to Article 768 of the Civil Code, property division upon divorce is..."

[0988] Input: Generated answer

[0989] Output: Answer data sent to the device

[0990] Step 8:

[0991] The user enters an additional question. The terminal sends the new question data to the server.

[0992] Input: A new question from the user

[0993] Output: Send additional question data from the terminal to the server

[0994] Step 9:

[0995] The server then uses NLP techniques to parse the additional questions and retrieve the required information from legal databases.

[0996] Input: New question data

[0997] Output: Reparsed keywords, intent, and related legal information

[0998] Step 10:

[0999] The server generates an additional answer based on the reanalysis result and sends it to the terminal.

[1000] Input: Reanalysis result

[1001] Output: Sends the regenerated answer to the terminal

[1002] This will create a system that allows even non-expert users to easily obtain legal advice and check contracts.

[1003] (Application example 1)

[1004] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1005] Conventional legal support systems are limited to allowing users to ask legal questions or check contracts. However, in the case of autonomous vehicles in particular, it is necessary to provide immediate legal advice and take prompt and appropriate action when a traffic accident or legal trouble occurs, but such a system does not exist. In addition, the inability to quickly check the contents of contracts related to traffic accidents and systems makes it difficult for users to take appropriate action.

[1006] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1007] In this invention, the server includes means for receiving natural language input, means for analyzing the natural language input using natural language processing technology, means for searching for relevant laws and information from a legal database, means for generating a response to the user based on the analysis results and the search information, means for detecting the occurrence of a traffic accident and providing legal advice, means for confirming the contents of an insurance contract and procedures to be taken in the event of a traffic accident, and means for providing the generated response to the user. This makes it possible to provide immediate legal advice to an autonomous vehicle when a traffic accident occurs, and to assist in confirming the contents of the insurance contract and in the progress of procedures.

[1008] "Natural language input" is a method in which a user inputs questions or instructions using everyday polite language.

[1009] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[1010] A "legal database" is a database that collects legal information such as laws and precedents.

[1011] "Means for detecting the occurrence of a traffic accident" refers to sensors and software that enable autonomous vehicles to automatically detect the occurrence of an accident.

[1012] "Means for providing legal advice" refers to a system that provides users with appropriate legal information in the event of an accident or legal trouble.

[1013] The "means for checking the contents of the insurance contract" is a system that analyzes the contents of the insurance contract and provides the user with appropriate information.

[1014] A "generative AI model" is an artificial intelligence model used to generate appropriate answers to input questions or instructions.

[1015] A "prompt sentence" is a question or instruction sentence that is input to a generative AI model.

[1016] A system for implementing the present invention is a legal assistance system installed in an autonomous vehicle. This system receives natural language input, analyzes it using natural language processing technology, and searches for relevant laws and information to provide legal advice in the event of a traffic accident. Specific embodiments are described below.

[1017] 1. User Input

[1018] Users can use the on-board display of the self-driving vehicle or devices such as smartphones to receive legal advice or check the details of their insurance contracts. For example, if they are involved in a traffic accident, they can input a question such as, "What are the legal procedures in the event of a traffic accident?"

[1019] 2. Natural Language Input and Text Analysis

[1020] The device forwards the user's question to the server, which then analyzes the received natural language input and, if necessary, uses image recognition (OCR) to extract the contract's text data. For example, an image of an insurance contract can be converted to text using OCR, and then NLP technology can be used to analyze the key legal elements.

[1021] 3. Natural language processing technology

[1022] The server uses natural language processing (NLP) technology to analyze the user's question and text data. The NLP engine extracts the intent of the question and important keywords, and automatically identifies the relevant legal field. For example, it extracts keywords such as "traffic accident" and "legal procedure" and provides a corresponding legal interpretation.

[1023] 4. Legal database search

[1024] The server accesses a legal database to search for relevant laws and precedents. It retrieves legal provisions and case studies and provides appropriate answers to the user's questions. For example, it retrieves laws related to responding to traffic accidents and provides specific examples.

[1025] 5. Generate and provide answers

[1026] Based on the search results and analysis results, the server generates an answer for the user. The generated answer is presented in a format that is easy for the user to understand. For example, an answer containing a clear explanation such as "If you are involved in a traffic accident, you should first contact the police and then report it to your insurance company" is created. The completed answer is sent from the server to the device and displayed to the user.

[1027] 6. Additional Questions and Reanalysis

[1028] Users are also provided with the ability to enter additional questions. If they have additional questions, they can submit them again using their terminal. The server then uses NLP technology to analyze the question, search for the necessary information in the legal database, and generate an additional answer. For example, a possible additional question would be, "How do we handle vehicles with joint ownership?"

[1029] 7. Specific Examples

[1030] In the event of a traffic accident, if the driver types the question "What are the legal procedures if I am involved in a traffic accident?" into the in-car display, the NLP engine will analyze the question and provide advice on the appropriate way to contact the police, how to report to the insurance company, etc. It is also possible to check the details of the insurance contract by taking a photo of it with a smartphone.

[1031] Using a generative AI model, the system generates appropriate answers and prompts for input questions and instructions. For example, by inputting the prompt "Please provide advice based on civil law and the Road Traffic Act regarding the legal procedures required in the event of a traffic accident," detailed legal advice is generated.

[1032] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1033] Step 1:

[1034] Users can use the on-board display of an autonomous vehicle or their smartphone to receive legal advice or check the details of their insurance policy. For example, if they are involved in a traffic accident, they can input a question such as, "What are the legal procedures in the event of a traffic accident?"

[1035] Input: Natural language input questions from the user

[1036] Output: Question text from user

[1037] Step 2:

[1038] The terminal forwards the user's question to the server.

[1039] Input: Question text submitted by the user

[1040] Output: The query data sent to the server

[1041] Step 3:

[1042] The server analyzes the received natural language input and, if necessary, uses image recognition technology (OCR) to extract the text data of the contract.

[1043] Input: Question text and optional contract image data to be sent

[1044] Output: Text data (question text and text data from the contract image)

[1045] Step 4:

[1046] The server uses natural language processing (NLP) technology to analyze user questions and text data, extracting the intent of the question and important keywords, and automatically identifying the relevant legal field.

[1047] Input: Text data

[1048] Output: Analysis results (keywords, intent)

[1049] Step 5:

[1050] The server accesses a legal database and searches for relevant statutes and case law information.

[1051] Input: Analysis results (keywords, intent)

[1052] Output: Search results (legal information, case law information)

[1053] Step 6:

[1054] Based on the search results and analysis results, the server generates answers for the user, which are presented in a format that is easy for the user to understand.

[1055] Input: Search results, analysis results

[1056] Output: Generated answer text

[1057] Step 7:

[1058] The server sends the generated answer to the user's terminal and displays it to the user.

[1059] Input: Generated answer text

[1060] Output: Answer displayed on the user's terminal

[1061] Step 8:

[1062] The user is also provided with the ability to enter additional questions, and if there are any additional questions, the user can submit them again using the terminal.

[1063] Input: User-supplied question text

[1064] Output: Additional question data

[1065] Step 9:

[1066] The server then uses NLP techniques to analyze the additional questions, retrieve the necessary information from legal databases, and generate additional answers.

[1067] Input: Additional question data

[1068] Output: Reanalysis results and additional answer text

[1069] Step 10:

[1070] A generative AI model is used to generate prompts and provide them to the user. For example, a prompt such as "Please provide advice based on civil law and the Road Traffic Act regarding the legal procedures required in the event of a traffic accident" can be generated.

[1071] Input: Analysis results and instructions for generating prompt statements

[1072] Output: Generated prompt statement

[1073] The above are the specific processing steps of the system for realizing the application example.

[1074] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1075] The system for implementing the present invention is a platform that supports users in providing legal advice and checking contracts by combining an emotion engine. The configuration and specific operation of this system are described below.

[1076] 1. User Input

[1077] Users can use devices such as smartphones or PCs to enter legal questions or upload photos of contracts. For example, a user can use a device to enter a question such as, "How will property be divided in the event of a divorce?" or take a photo of a newly signed contract and upload it.

[1078] 2. Natural Language Input and Text Analysis

[1079] The device transfers the questions and photos of the contract submitted by the user to the server, where the server analyzes the received natural language input and, if necessary, extracts the text data of the contract using image recognition technology (OCR). The text data entered by the user is also analyzed.

[1080] 3. Natural Language Processing Technology

[1081] The server uses natural language processing (NLP) technology to analyze user questions and text data. The NLP engine extracts the intent of the question and important keywords, and automatically identifies the relevant legal field.

[1082] 4. Emotion Engine

[1083] The server is equipped with an emotion engine that recognizes emotions from the user's natural language input. The emotion engine works in conjunction with text analysis to evaluate emotions such as anger, anxiety, or joy that may be present in the user's input, and adjusts subsequent processing based on that information.

[1084] 5. Legal Database Search

[1085] The server accesses legal databases based on legal keywords and phrases, searches for relevant statutes and precedents, retrieves legal provisions and case studies, and provides relevant answers to the user's questions.

[1086] 6. Generating and Providing Answers

[1087] The server combines the search results, analysis results, and emotions recognized by the emotion engine to generate an answer for the user. The tone and content of the generated answer are adjusted based on the results of the emotion engine. For example, if the user is feeling anxious, the answer will be provided in more reassuring language. The answer generated by the server is sent to the device and displayed to the user.

[1088] 7. Additional Questions and Reanalysis

[1089] The user is also given the ability to enter additional questions. If there are additional questions, the user submits the question again using the terminal. The server again analyzes the question using NLP technology and an emotion engine, searches for the necessary information from the legal database, and generates an additional answer. For example, if the user asks an additional question such as, "What about jointly owned houses?", the same process is repeated.

[1090] Specific examples

[1091] Example 1: Legal advice regarding divorce

[1092] When a user types "How will property be divided in the event of divorce?" into their device, the device sends the question data to the server. The server uses an NLP engine to analyze the question and extracts keywords such as "divorce" and "property division." If the emotion engine senses anxiety from the user's input, it also reflects this in the analysis. It retrieves information about Article 768 (property division) of the Civil Code from a legal database, generates an answer in a reassuring format, such as "According to Article 768 of the Civil Code, property division in the event of divorce is...," and sends it to the device. The answer displayed to the user is written in a reassuring tone.

[1093] Example 2: Contract Check

[1094] The user takes a photo of the contract on their device and uploads it. The device then sends the photo data to the server. The server uses OCR technology to convert the image into text data and uses NLP technology to analyze the important contents of the contract. The emotion engine evaluates any concerns or anxieties the user may have before submitting the contract and reflects those emotions in the analysis results and feedback. The server searches for relevant legal provisions and commentary in a legal database and generates a check result based on the analysis results. For example, a response may be generated in a tone that reassures the user, such as, "This contract may have the following issues..." and is displayed on the device.

[1095] The above is a specific embodiment for carrying out the present invention. With this system, users can quickly and accurately receive legal advice and contract checks, even without specialized knowledge, and can also receive appropriate advice that takes into account the user's feelings.

[1096] The processing flow will be explained below.

[1097] Step 1:

[1098] A user uses a device to type in a legal question or upload a photo of a contract.

[1099] Step 2:

[1100] The device sends the user's input data (text or image) to the server.

[1101] Step 3:

[1102] The server checks the received data and extracts the text data directly from the image if it is text data, or using OCR technology if it is image data.

[1103] Step 4:

[1104] The server uses natural language processing (NLP) technology to analyze the text data, extracting the intent of the question and important keywords from the contract.

[1105] Step 5:

[1106] The server's emotion engine recognizes emotions from the user's text data, such as "anxiety," "anger," and "joy."

[1107] Step 6:

[1108] Based on the analysis results of the emotion engine, the server selects a method for generating an answer that reflects the user's emotional state.

[1109] Step 7:

[1110] The server accesses legal databases based on legal keywords and phrases to search for relevant statutes and precedents.

[1111] Step 8:

[1112] The server combines the search results, analysis results, and the results of the emotion engine to generate a response for the user, adjusting the tone and content of the response based on the results of the emotion engine.

[1113] Step 9:

[1114] The server formats the generated response in a format that is easy for the user to understand and sends it to the terminal.

[1115] Step 10:

[1116] The terminal receives the answer sent from the server and displays it to the user, who can then check the answer and ask additional questions if necessary.

[1117] Step 11:

[1118] The user enters an additional question and sends it again from the terminal to the server.

[1119] Step 12:

[1120] The server receives the additional questions and analyzes them again using natural language processing technology and an emotion engine, searching the legal database again as needed to obtain new information.

[1121] Step 13:

[1122] The server generates a new answer based on the additional analysis results and sends it to the device.

[1123] Step 14:

[1124] The terminal displays the new answer to the user, and the process of additional questions and answers can be repeated until the user is satisfied.

[1125] The above is the specific flow of program processing when a user seeks legal advice or checks a contract.

[1126] Example 2

[1127] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1128] Current legal consultation systems and contract review systems have the ability to analyze users' natural language input and provide relevant laws and information, but they do not take into account the user's emotions. As a result, they are unable to fully alleviate users' anxieties and doubts, making it difficult to provide appropriate advice. Furthermore, when reviewing contracts, feedback that understands emotions is required to ensure important information is not overlooked.

[1129] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1130] In this invention, the server includes means for receiving natural language input, means for analyzing the natural language input using natural language processing technology, means for searching for relevant laws and information from a legal database, means for generating an answer to the user based on the analysis result and the search information, means for providing the generated answer to the user, and means for identifying the user's emotions using an emotion engine and adjusting the generated answer in accordance with the user's emotions, thereby enabling more appropriate and reassuring legal consultations and contract checks that take the user's emotions into consideration.

[1131] "Natural language input" refers to a user sending information about legal advice or contract review to a server in natural language.

[1132] "Natural language processing technology" is a technology for analyzing natural language text data entered by a user and understanding its intent and meaning.

[1133] A "legal database" refers to a database that contains various information related to laws, precedents, and laws.

[1134] An "emotion engine" is a technology that identifies emotions from the user's input text and reflects that emotional information in the analysis results and generated answers.

[1135] "Contract image data" refers to an image file that a user photographs or uploads to check the contents of a contract.

[1136] "Image recognition technology" refers to technology for extracting text data from image data (e.g., optical character recognition (OCR)).

[1137] "Reanalysis" refers to the process of reapplying existing natural language processing technology to perform a new analysis based on additional questions from the user.

[1138] "Means for generating an answer" refers to a function that automatically creates an appropriate answer for the user based on the extracted related information and analysis results.

[1139] "Means for providing to the user" refers to a function for transmitting the generated answer to the user via the terminal and displaying it.

[1140] "Means for searching for relevant laws and information" refers to the function of searching for appropriate laws and precedents from legal databases using keywords extracted using natural language processing technology.

[1141] This invention relates to a platform that supports users in seeking legal advice and checking contracts. The system aims to analyze input data from users and provide appropriate answers using natural language processing technology and an emotion engine.

[1142] System configuration

[1143] The system consists of the following main components:

[1144] 1. User Interface

[1145] Device: Using a device such as a smartphone or computer, users input legal questions and images of contracts.

[1146] 2. Data transmission and reception

[1147] Terminal: Sends image data of questions and contracts entered by the user to the server via the Internet.

[1148] 3. Server Side

[1149] Server: Performs data processing, analysis, emotion evaluation, and answer generation. The server is equipped with the following technologies:

[1150] Natural language processing engine (NLP engine): A technology that analyzes text data such as user questions and contracts.

[1151] Emotion engine: A technology that identifies emotions from the text entered by the user and reflects that emotional information in the analysis results and generated answers.

[1152] Image recognition technology (OCR): A technology that extracts text data from image data of contracts.

[1153] Legal database: A database containing various information related to laws, precedents, and laws.

[1154] Processing flow

[1155] 1. User Input

[1156] Users use devices such as smartphones or computers to enter legal questions and upload photos of contracts.

[1157] For example, type "How will assets be divided in the event of divorce?" or upload a photo of the contract.

[1158] 2. Data transmission

[1159] The terminal transmits the input data from the user to the server.

[1160] 3. Data Analysis

[1161] The server analyzes the received natural language text data using a natural language processing engine and also extracts text data from contract images using OCR technology.

[1162] The NLP engine identifies the intent of the question and important keywords, such as "divorce" and "property division."

[1163] 4. Emotional Assessment

[1164] The emotion engine installed on the server identifies emotions from the user's text, evaluating emotions such as "anxiety" or "anger," and reflecting this information in the analysis results.

[1165] 5. Legal Data Search

[1166] The server searches for relevant laws and precedents from a legal database based on the keywords identified by the NLP engine.

[1167] 6. Generating and Providing Answers

[1168] The server automatically generates an answer for the user based on the search results and analysis results.

[1169] The response generated is in a reassuring tone that reflects the emotional information provided by the emotion engine.

[1170] The generated answer is sent to the terminal and displayed to the user.

[1171] 7. Additional Questions and Reanalysis

[1172] If the user asks additional questions, that data is also sent from the terminal to the server.

[1173] The server then analyzes it again using NLP techniques and an emotion engine to generate additional answers.

[1174] Specific examples

[1175] Example 1: Legal advice regarding divorce

[1176] The user types into their device, "How will property be divided in the event of a divorce?" This question data is sent from the device to the server. The server uses an NLP engine to extract keywords such as "divorce" and "property division." If the emotion engine senses anxiety from the user's input, it also reflects this in the analysis. It retrieves information about Article 768 (property division) of the Civil Code from a legal database, generates a reassuring answer such as, "According to Article 768 of the Civil Code, property division in the event of a divorce is...," and sends it to the device. The answer displayed to the user is written in a reassuring tone.

[1177] Example 2: Contract Check

[1178] The user takes a photo of the contract on their device and uploads it. The device then sends the photo data to the server. The server uses OCR technology to convert the image into text data and uses NLP technology to analyze the important contents of the contract. An emotion engine evaluates any concerns the user may have before sending the contract and reflects those emotions in the analysis results and feedback. The server searches for relevant legal provisions and commentary in a legal database and generates a check result based on the analysis results. For example, a response is generated in a reassuring tone, such as, "The following points in this contract may be problematic..." and displayed on the device.

[1179] Prompt Sentence Examples

[1180] 1. Legal advice

[1181] How will property be divided when I get divorced?

[1182] What happens to a house that is in joint names?

[1183] 2. Check the contract

[1184] "I'm concerned about the contents of this contract. Please check it."

[1185] "What legal risks does this contract entail?"

[1186] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1187] Step 1: User Input

[1188] Users use a device (smartphone or PC) to enter legal questions or upload image data of contracts. The data entered is mainly text questions and image contracts. For example, a user might enter a question such as, "How will property be divided in the event of a divorce?" or take a photo of a newly signed contract and upload it.

[1189] Step 2: Sending data

[1190] The terminal sends the text data and image data entered by the user to the server. In this step, the input data (questions and contract images) is transferred to the server via the Internet. Specifically, the terminal divides the data into packets and sends them to a specific IP address on the server.

[1191] Step 3: Data analysis (natural language processing)

[1192] The server uses natural language processing technology (NLP) to analyze the received natural language text data. The input is the user's question text, and the output is extracted keywords and the intent of the question. Specifically, the server performs morphological analysis of the text and extracts important keywords (e.g., "divorce" and "property division"). In addition, image data of contracts is converted into text data using OCR technology. In this process, the image is scanned and converted into text data using a character recognition algorithm.

[1193] Step 4: Evaluate your emotions

[1194] The emotion engine installed on the server identifies emotions from the user's input text. The input is text after NLP processing, and the output is recognized emotional information (e.g., "anxiety" or "anger"). Specifically, the server analyzes emotional expressions in the text (e.g., "I'm worried" or "I'm anxious") to identify the user's emotion.

[1195] Step 5: Search for legal data

[1196] The server searches for relevant laws and precedents from a legal database based on the extracted keywords and phrases. The input is the extracted keywords (e.g., "divorce" and "property division"), and the output is the relevant laws and information (e.g., "Article 768 of the Civil Code"). Specifically, the server generates a database query, queries the legal database, and retrieves relevant provisions and precedents.

[1197] Step 6: Generate and serve answers

[1198] The server automatically generates an answer for the user based on the search results and analysis results. The input is legal information and emotional information, and the output is the answer provided to the user. The answer is generated in a tone that brings a sense of security, reflecting the emotional information generated by the emotion engine. For example, an answer containing reassuring language such as "According to Article 768 of the Civil Code, property division in the event of divorce is..." is generated. This answer is sent to the terminal and displayed to the user.

[1199] Step 7: Additional questions and reanalysis

[1200] If the user wants to enter an additional question, the question is sent again from the terminal to the server. The input is the new question data, and the output is a newly generated additional answer. The server again uses NLP technology and an emotion engine to analyze the question, searches the legal database for the necessary information again, and generates an additional answer. For example, in response to the additional question, "What about jointly owned houses?", the server generates an additional answer, "The specific legal treatment for jointly owned houses is..." This answer is also sent to the terminal and displayed to the user.

[1201] (Application example 2)

[1202] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1203] While conventional legal consultation systems can efficiently analyze customers' legal questions and the contents of contracts, they face the challenge of being unable to respond to customers' emotions. Furthermore, answers are often one-sided, which can leave customers feeling uneasy, and the system may be unable to respond appropriately to follow-up questions. This creates a problem that makes it difficult to improve customer satisfaction.

[1204] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1205] In this invention, the server includes means for receiving natural language input, means for analyzing the natural language input using natural language processing technology, means for searching for relevant laws and information from a legal database, means for generating an answer to the user based on the analysis result and the search information, means for analyzing the user's emotions, and means for adjusting the tone of the answer based on the emotion analysis result, thereby making it possible to provide an answer that is sensitive to the customer's emotions and gives them a sense of security.

[1206] "Natural language input" is linguistic input provided by a user through speech or text.

[1207] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human language.

[1208] A "legal database" is a searchable database that collects laws, precedents, and legal information.

[1209] "Sentiment analysis" is a technology that extracts and evaluates emotions from text data.

[1210] "Tone adjustment" is the act of adjusting the expression of a sentence according to the user's emotions.

[1211] "Image data of a contract" is a digital image representation of a paper contract.

[1212] "Image recognition technology (OCR)" is a technology that analyzes characters in an image and extracts them as text data.

[1213] A "follow-up question" is a new question asked by the user following the initial question.

[1214] The system embodying the present invention is a platform for supporting legal counselors in brick-and-mortar stores. This system uses the following hardware and software:

[1215] Hardware

[1216] Smart glasses (e.g. smart devices)

[1217] Tablet devices (e.g., personal digital assistants)

[1218] Server (e.g. cloud infrastructure)

[1219] software

[1220] Natural language processing engines (e.g., natural language processing systems)

[1221] Image recognition technology (OCR) (e.g., optical character recognition tools)

[1222] Sentiment analysis engine (e.g., sentiment analysis tool)

[1223] Legal databases (e.g., legal information databases)

[1224] Program processing explanation

[1225] 1. Input acceptance:

[1226] Users can use smart glasses or a tablet to ask legal questions by voice or text, or upload a photo of a contract.

[1227] 2. Data transfer and analysis:

[1228] The device transfers voice input, text data, or image data to the server. The server converts the voice data into text (using a voice recognition API) and analyzes the text data. For image data, an optical character recognition tool is used to recognize characters and convert them into text data.

[1229] 3. Natural Language Processing:

[1230] A natural language processing system extracts key keywords from the input data and identifies relevant legal areas.

[1231] 4. Emotion analysis:

[1232] The emotion analysis tool recognizes emotions from the user's text data, assessing them as anxiety, anger, joy, etc., and uses the results to adjust the analysis results.

[1233] 5. Legal Database Search:

[1234] The server searches the legal information database based on the specified keywords and retrieves relevant laws and precedents.

[1235] 6. Generate and provide answers:

[1236] Based on the search results and sentiment analysis results, an appropriate answer is generated. The tone of the generated answer is adjusted according to the sentiment analysis results and provided to the user. For example, it may respond in a reassuring tone, such as "In accordance with Article 90 of the Civil Code, this provision may be invalid."

[1237] 7. Follow-up questions:

[1238] If the user has additional questions, natural language processing is performed again to generate additional answers. The process is repeated depending on the additional questions, providing answers in real time.

[1239] Specific examples

[1240] 1. Customer Questions:

[1241] A customer speaks to the smart glasses and says, "I'm confused by this clause in the contract."

[1242] 2. In-store counseling:

[1243] The smart glasses recognize the question through voice recognition and forward it to the server. The server analyzes the question and identifies important keywords such as "contract" and "article." The emotion analysis tool recognizes the customer's anxiety and notifies the counselor. The server searches a legal information database to retrieve relevant laws and precedents. The server generates an answer based on the emotion analysis results and provides it to the counselor in a reassuring tone, such as "In accordance with Article 90 of the Civil Code, this article may be invalid."

[1244] Prompt Sentence Examples

[1245] I'm unclear about a particular clause in the contract and need clarification. Use sentiment analysis tools to generate legal advice with a tone that calms the customer's concerns.

[1246] This system will make legal consultations in brick-and-mortar stores more efficient and enable the provision of high-quality legal services that are sensitive to customer feelings.

[1247] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1248] Step 1:

[1249] The user inputs a question or an image of a contract through smart glasses or a tablet device, which generates voice data, text data, or image data.

[1250] Step 2:

[1251] The device transfers the voice data to the server. The voice data is input, and the server uses a voice recognition API to convert the voice data into text data. This text data is used for the next process.

[1252] Step 3:

[1253] The device transfers text data to the server. The input text data is analyzed and important keywords are extracted. This is done by a natural language processing system. Keywords are generated as output.

[1254] Step 4:

[1255] The device transfers image data to the server. Image recognition technology (OCR) is used to convert the image data into text data. This converted text data is then analyzed.

[1256] Step 5:

[1257] The server uses a natural language processing system to analyze the text data, and from the analyzed data, it identifies the relevant legal field, which is then used for the search in the next step.

[1258] Step 6:

[1259] The server uses an emotion analysis tool to extract the user's emotions from the text data. Emotions such as anxiety, anger, and joy are evaluated, and the results are output as data.

[1260] Step 7:

[1261] The server searches the legal information database based on the relevant legal field, retrieves relevant laws and precedents based on keywords, and outputs the information as data.

[1262] Step 8:

[1263] The server generates an appropriate answer based on the search results and sentiment analysis results. The answer is written and output in a tone that corresponds to the sentiment analysis results.

[1264] Step 9:

[1265] The server sends the generated answer to the terminal, the answer is received and displayed by the user, and the user enters additional questions if necessary.

[1266] Step 10:

[1267] The user enters a follow-up question, which the terminal forwards to the server.

[1268] Step 11:

[1269] The server then performs natural language processing again to analyze the additional question, extracting new keywords and outputting the analysis results.

[1270] Step 12:

[1271] The server generates an additional answer based on the analysis results, which is provided and output in a tone that corresponds to the sentiment analysis.

[1272] Step 13:

[1273] The server sends any additional responses to the device and displays them to the user. All data collected up to this point is logged and saved for future analysis and improvement.

[1274] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1275] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1276] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1277] [Fourth embodiment]

[1278] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1279] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1280] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1281] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1282] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1283] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1284] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1285] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1286] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1287] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1288] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1289] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1290] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1291] The system for implementing the present invention is a comprehensive platform for supporting users in obtaining legal advice and checking contracts. The elements that make up the system and their specific functions will be described below.

[1292] 1. User Input

[1293] Users can use devices such as smartphones or PCs to enter legal questions or upload photos of contracts. For example, a user can use a device to enter a question such as, "How will property be divided in the event of a divorce?" or take a photo of a newly signed contract and upload it.

[1294] 2. Natural Language Input and Text Analysis

[1295] The device transmits the user's questions and photos of the contract to the server, which then analyzes the received natural language input and, if necessary, extracts the contract's text using image recognition (OCR). For example, the image of the contract can be converted to text using OCR, and then analyzed for key legal elements using NLP technology.

[1296] 3. Natural Language Processing Technology

[1297] The server uses natural language processing (NLP) technology to analyze user questions and text data. The NLP engine extracts the intent of the question and important keywords, and automatically identifies the relevant legal field. For example, it extracts keywords such as "divorce" and "property division" and provides a corresponding legal interpretation.

[1298] 4. Legal Database Search

[1299] The server accesses a legal database to search for relevant laws and precedents. It retrieves legal provisions and case studies and provides appropriate answers to the user's questions. For example, it retrieves information about Article 768 of the Civil Code (property division) and provides specific examples.

[1300] 5. Generating and Providing Answers

[1301] Based on the search results and analysis results, the server generates an answer for the user. The generated answer is presented in a format that is easy for the user to understand. For example, an answer that includes a clear explanation such as, "According to Article 768 of the Civil Code, property division upon divorce is..." is created. The completed answer is sent from the server to the device and displayed to the user.

[1302] 6. Additional Questions and Reanalysis

[1303] Users are also provided with the ability to enter additional questions. If they have additional questions, they can submit them again using their terminal. The server then uses NLP technology to analyze the question, search for the necessary information in the legal database, and generate an additional answer. For example, a possible additional question would be, "What about a house in joint ownership?"

[1304] Specific examples

[1305] Example 1: Legal advice regarding divorce

[1306] When a user types "How will property be divided if I get divorced?" into their device, the device sends the question data to the server. The server uses an NLP engine to analyze the question and extracts the keywords "divorce" and "property division." It then searches for relevant provisions in a legal database and obtains information about Article 768 of the Civil Code. The server then generates an answer for the user based on the analysis results, such as "According to Article 768 of the Civil Code...," and sends it to the device, where it is displayed to the user.

[1307] Example 2: Contract Check

[1308] The user takes a photo of the contract on their device and uploads it. The device then sends the photo data to the server. The server uses OCR technology to convert the image into text data and analyzes the text data using NLP technology. The server searches a legal database for relevant legal provisions and commentary, and generates a check result based on the analysis results. For example, the server sends a response in the form of "The following clauses in this contract may be problematic..." and is displayed on the device.

[1309] The above is a specific embodiment of the present invention. This system enables users to quickly and accurately receive legal advice and check contracts, even if they do not have specialized knowledge.

[1310] The processing flow will be explained below.

[1311] Step 1:

[1312] The user enters a legal consultation question through the terminal or takes a photo of a contract and uploads it to the terminal.

[1313] Step 2:

[1314] The device sends the user's input data (text or image) to the server.

[1315] Step 3:

[1316] The server checks the received data and extracts the text data directly from the image if it is text data, or using OCR technology if it is image data.

[1317] Step 4:

[1318] The server uses natural language processing (NLP) technology to analyze the text data, extracting the intent of the question and important keywords from the contract.

[1319] Step 5:

[1320] The server accesses legal databases based on legal keywords and phrases to search for relevant statutes and precedents.

[1321] Step 6:

[1322] The server combines the search results with the analysis results to generate answers to the user's questions. In the case of contracts, the server also evaluates their contents from a legal perspective and points out problems and areas for improvement.

[1323] Step 7:

[1324] The server formats the generated response in a format that is easy for the user to understand and sends it to the terminal.

[1325] Step 8:

[1326] The terminal receives the answer sent from the server and displays it to the user, who can then check the answer and ask additional questions if necessary.

[1327] Step 9:

[1328] The user enters an additional question and sends it again from the terminal to the server.

[1329] Step 10:

[1330] The server receives the additional question and again analyzes it using natural language processing technology, searching the legal database again as needed to obtain new information.

[1331] Step 11:

[1332] The server generates a new answer based on the additional analysis results and sends it to the device.

[1333] Step 12:

[1334] The terminal displays the new answer to the user, and the process of additional questions and answers can be repeated until the user is satisfied.

[1335] The above is the specific flow of program processing when a user seeks legal advice or checks a contract.

[1336] Example 1

[1337] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1338] In modern society, legal consultations and contract checking are important tasks, but it is difficult for users without specialized knowledge to perform these tasks quickly and accurately. Furthermore, for general users to quickly obtain accurate legal information, they need the advice of lawyers with specialized knowledge and skills, which entails a significant burden in terms of cost and time. Therefore, there is a need for a system that allows even non-experts to easily obtain legal consultations and check contracts, while also providing accurate legal information quickly.

[1339] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1340] In this invention, the server includes means for receiving natural language input from a terminal, means for analyzing the natural language input using natural language processing technology, means for searching for relevant laws and information from a legal database, means for generating an answer for the user based on the analysis results and the search information using a generative AI model, and means for transmitting the generated answer to the terminal and providing it to the user. This allows even non-expert users to easily obtain legal advice or check contracts using a terminal such as a smartphone or PC, and enables them to quickly and accurately obtain legal information.

[1341] A "terminal" is a device for inputting and displaying information, and includes smartphones, personal computers, etc.

[1342] "Natural language input" refers to text data entered by a user in everyday language, including questions and consultation details.

[1343] "Natural language processing technology" is a technology that enables computers to understand and analyze human language, and includes tokenization, keyword extraction, grammar analysis, etc.

[1344] A "legal database" is a database that contains information on laws, precedents, provisions, etc., and is used to obtain information related to the law.

[1345] A "generative AI model" is an artificial intelligence model that automatically generates natural-sounding sentences based on input data, creating answers based on specific conditions and context.

[1346] "Image recognition technology" refers to technology for extracting useful information from image data, and includes OCR (optical character recognition).

[1347] "OCR" is a technology that recognizes characters in an image and converts them into text data.

[1348] "User" refers to a person who uses the system to obtain legal advice or check contracts, and includes ordinary people without specialized knowledge.

[1349] A "contract" is a legally binding document that contains the contract terms and conditions.

[1350] A "follow-up question" is a question that a user enters after receiving an initial answer, and includes content that requests supplemental information.

[1351] "Reanalysis" refers to analyzing data again using natural language processing techniques after additional questions are entered.

[1352] The system for implementing the present invention is a comprehensive platform for supporting users in obtaining legal advice and checking contracts. The main elements that make up this system, their specific functions, and the hardware and software used will be described in detail below.

[1353] Hardware and Software Configuration

[1354] 1. User Input

[1355] Users can input legal questions using devices such as smartphones or PCs. For example, a user can input "How will property be divided in the event of divorce?" into the device. Users can also take photos of contracts and upload them to the system via the device.

[1356] 2. Data transfer to the server

[1357] The terminal transfers the input data (text data and image data of the contract) from the user to the server using the general HTTP protocol.

[1358] 3. Natural Language Input and Text Analysis

[1359] The server is equipped with software to analyze the received data. The text data is analyzed using a natural language processing engine (e.g., spaCy or BERT). The image data of the contract is converted to text using OCR technology (e.g., Tesseract), and then analyzed for key legal elements using natural language processing technology.

[1360] 4. Analysis using natural language processing technology

[1361] The server uses an NLP engine to analyze the user's questions and text data. This analysis involves tokenizing the input text and extracting keywords and intent. For example, it extracts the keywords "divorce" and "property division" to interpret the user's intent.

[1362] 5. Legal Database Search

[1363] The server accesses legal databases (e.g., LexisNexis or Westlaw) to search for relevant statutes and case law information, and generates queries to the database based on keywords extracted from the user to retrieve the required information.

[1364] 6. Answer Generation

[1365] The server generates answers for users using a generative AI model (e.g., GPT-3) based on the search results and analysis results. In this process, answers are created in a format that is easy for users to understand, based on legal data and relevant precedents.

[1366] 7. Displaying the Answer to the User

[1367] The generated answer is sent from the server to the device, and the received answer is displayed to the user. For example, the answer may be presented in the form of "According to Article 768 of the Civil Code, property division in the event of divorce is..."

[1368] 8. Additional Questions and Reanalysis

[1369] The user can enter additional questions. The device sends the new question to the server, which again uses NLP technology to analyze the question, search for the necessary information in the legal database, and generate an additional answer. For example, a possible additional question might be, "What about jointly owned houses?"

[1370] Specific examples

[1371] Example 1: Legal advice regarding divorce

[1372] When a user types "How will property be divided if I get divorced?" into their device, the device sends the question data to the server. The server uses an NLP engine to analyze the question and extracts the keywords "divorce" and "property division." It then searches for relevant provisions in a legal database and obtains information about Article 768 of the Civil Code. The server then generates an answer for the user based on the analysis results, such as "According to Article 768 of the Civil Code...," and sends it to the device, where it is displayed to the user.

[1373] Example 2: Contract Check

[1374] The user takes a photo of the contract on their device and uploads it. The device sends the photo data to the server. The server converts the image into text data using OCR technology. The server analyzes the text data using NLP technology. The server searches for relevant legal provisions and commentary in a legal database, and generates a check result based on the analysis. For example, the server sends a response to the device in the form of, "The following clauses in this contract may be problematic..." The device then displays the check results it has received to the user.

[1375] Prompt Sentence Examples

[1376] "Please tell me about the division of assets in the event of divorce."

[1377] Please check the risk section of this contract.

[1378] The above is a concrete example of how to carry out the invention. By using this system, users can quickly and accurately receive legal advice and check contracts, even if they do not have specialized knowledge.

[1379] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1380] Step 1:

[1381] Users can use their devices to input legal questions, such as "How will property be divided in the event of a divorce?", or they can take a photo of a contract and upload it.

[1382] Input: User question text or contract image

[1383] Output: Sending data from the device to the server

[1384] Step 2:

[1385] The device transfers the entered question text and image data of the contract to the server using the standard HTTP protocol.

[1386] Input: Data (text or images) entered by the user into the device

[1387] Output: Data sent to the server

[1388] Step 3:

[1389] The server analyzes the received data. If it is text data, it is analyzed using natural language processing technology (NLP engine). If it is image data of a contract, OCR technology is used to extract text data from the image, and then NLP technology is used to analyze the text.

[1390] Input: Text or image data received by the server

[1391] Output: Analysis results including extracted keywords and intent

[1392] Step 4:

[1393] The server uses an NLP engine to perform a more detailed analysis of the text data from the user's questions and contracts. Specifically, it tokenizes the text and extracts keywords and context. For example, it identifies keywords such as "divorce" and "property division."

[1394] Input: Analysis results including extracted keywords and intent

[1395] Output: Detailed analysis results including user intent and keywords

[1396] Step 5:

[1397] The server accesses the legal database to search for relevant laws and information, generates a database query based on keywords, and retrieves the necessary legal provisions and case law information.

[1398] Input: Keywords and intent of detailed analysis results

[1399] Output: Laws and related information from legal databases

[1400] Step 6:

[1401] The server uses the generative AI model to generate answers for users based on search results and analysis results. Specifically, it generates answers in a format that is easy for users to understand, based on legal data and related precedents.

[1402] Input: Search results and analysis results

[1403] Output: The answer generated for the user

[1404] Step 7:

[1405] The server sends the generated answer to the device. The device displays the answer it receives to the user. For example, it might present the answer in a format such as, "According to Article 768 of the Civil Code, property division upon divorce is..."

[1406] Input: Generated answer

[1407] Output: Answer data sent to the device

[1408] Step 8:

[1409] The user enters an additional question. The terminal sends the new question data to the server.

[1410] Input: A new question from the user

[1411] Output: Send additional question data from the terminal to the server

[1412] Step 9:

[1413] The server then uses NLP techniques to parse the additional questions and retrieve the required information from legal databases.

[1414] Input: New question data

[1415] Output: Reparsed keywords, intent, and related legal information

[1416] Step 10:

[1417] The server generates an additional answer based on the reanalysis result and sends it to the terminal.

[1418] Input: Reanalysis result

[1419] Output: Sends the regenerated answer to the terminal

[1420] This will create a system that allows even non-expert users to easily obtain legal advice and check contracts.

[1421] (Application example 1)

[1422] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1423] Conventional legal support systems are limited to allowing users to ask legal questions or check contracts. However, in the case of autonomous vehicles in particular, it is necessary to provide immediate legal advice and take prompt and appropriate action when a traffic accident or legal trouble occurs, but such a system does not exist. In addition, the inability to quickly check the contents of contracts related to traffic accidents and systems makes it difficult for users to take appropriate action.

[1424] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1425] In this invention, the server includes means for receiving natural language input, means for analyzing the natural language input using natural language processing technology, means for searching for relevant laws and information from a legal database, means for generating a response to the user based on the analysis results and the search information, means for detecting the occurrence of a traffic accident and providing legal advice, means for confirming the contents of an insurance contract and procedures to be taken in the event of a traffic accident, and means for providing the generated response to the user. This makes it possible to provide immediate legal advice to an autonomous vehicle when a traffic accident occurs, and to assist in confirming the contents of the insurance contract and in the progress of procedures.

[1426] "Natural language input" is a method in which a user inputs questions or instructions using everyday polite language.

[1427] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[1428] A "legal database" is a database that collects legal information such as laws and precedents.

[1429] "Means for detecting the occurrence of a traffic accident" refers to sensors and software that enable autonomous vehicles to automatically detect the occurrence of an accident.

[1430] "Means for providing legal advice" refers to a system that provides users with appropriate legal information in the event of an accident or legal trouble.

[1431] The "means for checking the contents of the insurance contract" is a system that analyzes the contents of the insurance contract and provides the user with appropriate information.

[1432] A "generative AI model" is an artificial intelligence model used to generate appropriate answers to input questions or instructions.

[1433] A "prompt sentence" is a question or instruction sentence that is input to a generative AI model.

[1434] A system for implementing the present invention is a legal assistance system installed in an autonomous vehicle. This system receives natural language input, analyzes it using natural language processing technology, and searches for relevant laws and information to provide legal advice in the event of a traffic accident. Specific embodiments are described below.

[1435] 1. User Input

[1436] Users can use the on-board display of the self-driving vehicle or devices such as smartphones to receive legal advice or check the details of their insurance contracts. For example, if they are involved in a traffic accident, they can input a question such as, "What are the legal procedures in the event of a traffic accident?"

[1437] 2. Natural Language Input and Text Analysis

[1438] The device forwards the user's question to the server, which then analyzes the received natural language input and, if necessary, uses image recognition (OCR) to extract the contract's text data. For example, an image of an insurance contract can be converted to text using OCR, and then NLP technology can be used to analyze the key legal elements.

[1439] 3. Natural language processing technology

[1440] The server uses natural language processing (NLP) technology to analyze the user's question and text data. The NLP engine extracts the intent of the question and important keywords, and automatically identifies the relevant legal field. For example, it extracts keywords such as "traffic accident" and "legal procedure" and provides a corresponding legal interpretation.

[1441] 4. Legal database search

[1442] The server accesses a legal database to search for relevant laws and precedents. It retrieves legal provisions and case studies and provides appropriate answers to the user's questions. For example, it retrieves laws related to responding to traffic accidents and provides specific examples.

[1443] 5. Generate and provide answers

[1444] Based on the search results and analysis results, the server generates an answer for the user. The generated answer is presented in a format that is easy for the user to understand. For example, an answer containing a clear explanation such as "If you are involved in a traffic accident, you should first contact the police and then report it to your insurance company" is created. The completed answer is sent from the server to the device and displayed to the user.

[1445] 6. Additional Questions and Reanalysis

[1446] Users are also provided with the ability to enter additional questions. If they have additional questions, they can submit them again using their terminal. The server then uses NLP technology to analyze the question, search for the necessary information in the legal database, and generate an additional answer. For example, a possible additional question would be, "How do we handle vehicles with joint ownership?"

[1447] 7. Specific Examples

[1448] In the event of a traffic accident, if the driver types the question "What are the legal procedures if I am involved in a traffic accident?" into the in-car display, the NLP engine will analyze the question and provide advice on the appropriate way to contact the police, how to report to the insurance company, etc. It is also possible to check the details of the insurance contract by taking a photo of it with a smartphone.

[1449] Using a generative AI model, the system generates appropriate answers and prompts for input questions and instructions. For example, by inputting the prompt "Please provide advice based on civil law and the Road Traffic Act regarding the legal procedures required in the event of a traffic accident," detailed legal advice is generated.

[1450] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1451] Step 1:

[1452] Users can use the on-board display of an autonomous vehicle or their smartphone to receive legal advice or check the details of their insurance policy. For example, if they are involved in a traffic accident, they can input a question such as, "What are the legal procedures in the event of a traffic accident?"

[1453] Input: Natural language input questions from the user

[1454] Output: Question text from user

[1455] Step 2:

[1456] The terminal forwards the user's question to the server.

[1457] Input: Question text submitted by the user

[1458] Output: The query data sent to the server

[1459] Step 3:

[1460] The server analyzes the received natural language input and, if necessary, uses image recognition technology (OCR) to extract the text data of the contract.

[1461] Input: Question text and optional contract image data to be sent

[1462] Output: Text data (question text and text data from the contract image)

[1463] Step 4:

[1464] The server uses natural language processing (NLP) technology to analyze user questions and text data, extracting the intent of the question and important keywords, and automatically identifying the relevant legal field.

[1465] Input: Text data

[1466] Output: Analysis results (keywords, intent)

[1467] Step 5:

[1468] The server accesses a legal database and searches for relevant statutes and case law information.

[1469] Input: Analysis results (keywords, intent)

[1470] Output: Search results (legal information, case law information)

[1471] Step 6:

[1472] Based on the search results and analysis results, the server generates answers for the user, which are presented in a format that is easy for the user to understand.

[1473] Input: Search results, analysis results

[1474] Output: Generated answer text

[1475] Step 7:

[1476] The server sends the generated answer to the user's terminal and displays it to the user.

[1477] Input: Generated answer text

[1478] Output: Answer displayed on the user's terminal

[1479] Step 8:

[1480] The user is also provided with the ability to enter additional questions, and if there are any additional questions, the user can submit them again using the terminal.

[1481] Input: User-supplied question text

[1482] Output: Additional question data

[1483] Step 9:

[1484] The server then uses NLP techniques to analyze the additional questions, retrieve the necessary information from legal databases, and generate additional answers.

[1485] Input: Additional question data

[1486] Output: Reanalysis results and additional answer text

[1487] Step 10:

[1488] A generative AI model is used to generate prompts and provide them to the user. For example, a prompt such as "Please provide advice based on civil law and the Road Traffic Act regarding the legal procedures required in the event of a traffic accident" can be generated.

[1489] Input: Analysis results and instructions for generating prompt statements

[1490] Output: Generated prompt statement

[1491] The above are the specific processing steps of the system for realizing the application example.

[1492] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1493] The system for implementing the present invention is a platform that supports users in providing legal advice and checking contracts by combining an emotion engine. The configuration and specific operation of this system are described below.

[1494] 1. User Input

[1495] Users can use devices such as smartphones or PCs to enter legal questions or upload photos of contracts. For example, a user can use a device to enter a question such as, "How will property be divided in the event of a divorce?" or take a photo of a newly signed contract and upload it.

[1496] 2. Natural Language Input and Text Analysis

[1497] The device transfers the questions and photos of the contract submitted by the user to the server, where the server analyzes the received natural language input and, if necessary, extracts the text data of the contract using image recognition technology (OCR). The text data entered by the user is also analyzed.

[1498] 3. Natural Language Processing Technology

[1499] The server uses natural language processing (NLP) technology to analyze user questions and text data. The NLP engine extracts the intent of the question and important keywords, and automatically identifies the relevant legal field.

[1500] 4. Emotion Engine

[1501] The server is equipped with an emotion engine that recognizes emotions from the user's natural language input. The emotion engine works in conjunction with text analysis to evaluate emotions such as anger, anxiety, or joy that may be present in the user's input, and adjusts subsequent processing based on that information.

[1502] 5. Legal Database Search

[1503] The server accesses legal databases based on legal keywords and phrases, searches for relevant statutes and precedents, retrieves legal provisions and case studies, and provides relevant answers to the user's questions.

[1504] 6. Generating and Providing Answers

[1505] The server combines the search results, analysis results, and emotions recognized by the emotion engine to generate an answer for the user. The tone and content of the generated answer are adjusted based on the results of the emotion engine. For example, if the user is feeling anxious, the answer will be provided in more reassuring language. The answer generated by the server is sent to the device and displayed to the user.

[1506] 7. Additional Questions and Reanalysis

[1507] The user is also given the ability to enter additional questions. If there are additional questions, the user submits the question again using the terminal. The server again analyzes the question using NLP technology and an emotion engine, searches for the necessary information from the legal database, and generates an additional answer. For example, if the user asks an additional question such as, "What about jointly owned houses?", the same process is repeated.

[1508] Specific examples

[1509] Example 1: Legal advice regarding divorce

[1510] When a user types "How will property be divided in the event of divorce?" into their device, the device sends the question data to the server. The server uses an NLP engine to analyze the question and extracts keywords such as "divorce" and "property division." If the emotion engine senses anxiety from the user's input, it also reflects this in the analysis. It retrieves information about Article 768 (property division) of the Civil Code from a legal database, generates an answer in a reassuring format, such as "According to Article 768 of the Civil Code, property division in the event of divorce is...," and sends it to the device. The answer displayed to the user is written in a reassuring tone.

[1511] Example 2: Contract Check

[1512] The user takes a photo of the contract on their device and uploads it. The device then sends the photo data to the server. The server uses OCR technology to convert the image into text data and uses NLP technology to analyze the important contents of the contract. The emotion engine evaluates any concerns or anxieties the user may have before submitting the contract and reflects those emotions in the analysis results and feedback. The server searches for relevant legal provisions and commentary in a legal database and generates a check result based on the analysis results. For example, a response may be generated in a tone that reassures the user, such as, "This contract may have the following issues..." and is displayed on the device.

[1513] The above is a specific embodiment for carrying out the present invention. With this system, users can quickly and accurately receive legal advice and contract checks, even without specialized knowledge, and can also receive appropriate advice that takes into account the user's feelings.

[1514] The processing flow will be explained below.

[1515] Step 1:

[1516] A user uses a device to type in a legal question or upload a photo of a contract.

[1517] Step 2:

[1518] The device sends the user's input data (text or image) to the server.

[1519] Step 3:

[1520] The server checks the received data and extracts the text data directly from the image if it is text data, or using OCR technology if it is image data.

[1521] Step 4:

[1522] The server uses natural language processing (NLP) technology to analyze the text data, extracting the intent of the question and important keywords from the contract.

[1523] Step 5:

[1524] The server's emotion engine recognizes emotions from the user's text data, such as "anxiety," "anger," and "joy."

[1525] Step 6:

[1526] Based on the analysis results of the emotion engine, the server selects a method for generating an answer that reflects the user's emotional state.

[1527] Step 7:

[1528] The server accesses legal databases based on legal keywords and phrases to search for relevant statutes and precedents.

[1529] Step 8:

[1530] The server combines the search results, analysis results, and the results of the emotion engine to generate a response for the user, adjusting the tone and content of the response based on the results of the emotion engine.

[1531] Step 9:

[1532] The server formats the generated response in a format that is easy for the user to understand and sends it to the terminal.

[1533] Step 10:

[1534] The terminal receives the answer sent from the server and displays it to the user, who can then check the answer and ask additional questions if necessary.

[1535] Step 11:

[1536] The user enters an additional question and sends it again from the terminal to the server.

[1537] Step 12:

[1538] The server receives the additional questions and analyzes them again using natural language processing technology and an emotion engine, searching the legal database again as needed to obtain new information.

[1539] Step 13:

[1540] The server generates a new answer based on the additional analysis results and sends it to the device.

[1541] Step 14:

[1542] The terminal displays the new answer to the user, and the process of additional questions and answers can be repeated until the user is satisfied.

[1543] The above is the specific flow of program processing when a user seeks legal advice or checks a contract.

[1544] Example 2

[1545] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1546] Current legal consultation systems and contract review systems have the ability to analyze users' natural language input and provide relevant laws and information, but they do not take into account the user's emotions. As a result, they are unable to fully alleviate users' anxieties and doubts, making it difficult to provide appropriate advice. Furthermore, when reviewing contracts, feedback that understands emotions is required to ensure important information is not overlooked.

[1547] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1548] In this invention, the server includes means for receiving natural language input, means for analyzing the natural language input using natural language processing technology, means for searching for relevant laws and information from a legal database, means for generating an answer to the user based on the analysis result and the search information, means for providing the generated answer to the user, and means for identifying the user's emotions using an emotion engine and adjusting the generated answer in accordance with the user's emotions, thereby enabling more appropriate and reassuring legal consultations and contract checks that take the user's emotions into consideration.

[1549] "Natural language input" refers to a user sending information about legal advice or contract review to a server in natural language.

[1550] "Natural language processing technology" is a technology for analyzing natural language text data entered by a user and understanding its intent and meaning.

[1551] A "legal database" refers to a database that contains various information related to laws, precedents, and laws.

[1552] An "emotion engine" is a technology that identifies emotions from the user's input text and reflects that emotional information in the analysis results and generated answers.

[1553] "Contract image data" refers to an image file that a user photographs or uploads to check the contents of a contract.

[1554] "Image recognition technology" refers to technology for extracting text data from image data (e.g., optical character recognition (OCR)).

[1555] "Reanalysis" refers to the process of reapplying existing natural language processing technology to perform a new analysis based on additional questions from the user.

[1556] "Means for generating an answer" refers to a function that automatically creates an appropriate answer for the user based on the extracted related information and analysis results.

[1557] "Means for providing to the user" refers to a function for transmitting the generated answer to the user via the terminal and displaying it.

[1558] "Means for searching for relevant laws and information" refers to the function of searching for appropriate laws and precedents from legal databases using keywords extracted using natural language processing technology.

[1559] This invention relates to a platform that supports users in seeking legal advice and checking contracts. The system aims to analyze input data from users and provide appropriate answers using natural language processing technology and an emotion engine.

[1560] System configuration

[1561] The system consists of the following main components:

[1562] 1. User Interface

[1563] Device: Using a device such as a smartphone or computer, users input legal questions and images of contracts.

[1564] 2. Data transmission and reception

[1565] Terminal: Sends image data of questions and contracts entered by the user to the server via the Internet.

[1566] 3. Server Side

[1567] Server: Performs data processing, analysis, emotion evaluation, and answer generation. The server is equipped with the following technologies:

[1568] Natural language processing engine (NLP engine): A technology that analyzes text data such as user questions and contracts.

[1569] Emotion engine: A technology that identifies emotions from the text entered by the user and reflects that emotional information in the analysis results and generated answers.

[1570] Image recognition technology (OCR): A technology that extracts text data from image data of contracts.

[1571] Legal database: A database containing various information related to laws, precedents, and laws.

[1572] Processing flow

[1573] 1. User Input

[1574] Users use devices such as smartphones or computers to enter legal questions and upload photos of contracts.

[1575] For example, type "How will assets be divided in the event of divorce?" or upload a photo of the contract.

[1576] 2. Data transmission

[1577] The terminal transmits the input data from the user to the server.

[1578] 3. Data Analysis

[1579] The server analyzes the received natural language text data using a natural language processing engine and also extracts text data from contract images using OCR technology.

[1580] The NLP engine identifies the intent of the question and important keywords, such as "divorce" and "property division."

[1581] 4. Emotional Assessment

[1582] The emotion engine installed on the server identifies emotions from the user's text, evaluating emotions such as "anxiety" or "anger," and reflecting this information in the analysis results.

[1583] 5. Legal Data Search

[1584] The server searches for relevant laws and precedents from a legal database based on the keywords identified by the NLP engine.

[1585] 6. Generating and Providing Answers

[1586] The server automatically generates an answer for the user based on the search results and analysis results.

[1587] The response generated is in a reassuring tone that reflects the emotional information provided by the emotion engine.

[1588] The generated answer is sent to the terminal and displayed to the user.

[1589] 7. Additional Questions and Reanalysis

[1590] If the user asks additional questions, that data is also sent from the terminal to the server.

[1591] The server then analyzes it again using NLP techniques and an emotion engine to generate additional answers.

[1592] Specific examples

[1593] Example 1: Legal advice regarding divorce

[1594] The user types into their device, "How will property be divided in the event of a divorce?" This question data is sent from the device to the server. The server uses an NLP engine to extract keywords such as "divorce" and "property division." If the emotion engine senses anxiety from the user's input, it also reflects this in the analysis. It retrieves information about Article 768 (property division) of the Civil Code from a legal database, generates a reassuring answer such as, "According to Article 768 of the Civil Code, property division in the event of a divorce is...," and sends it to the device. The answer displayed to the user is written in a reassuring tone.

[1595] Example 2: Contract Check

[1596] The user takes a photo of the contract on their device and uploads it. The device then sends the photo data to the server. The server uses OCR technology to convert the image into text data and uses NLP technology to analyze the important contents of the contract. An emotion engine evaluates any concerns the user may have before sending the contract and reflects those emotions in the analysis results and feedback. The server searches for relevant legal provisions and commentary in a legal database and generates a check result based on the analysis results. For example, a response is generated in a reassuring tone, such as, "The following points in this contract may be problematic..." and displayed on the device.

[1597] Prompt Sentence Examples

[1598] 1. Legal advice

[1599] How will property be divided when I get divorced?

[1600] What happens to a house that is in joint names?

[1601] 2. Check the contract

[1602] "I'm concerned about the contents of this contract. Please check it."

[1603] "What legal risks does this contract entail?"

[1604] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1605] Step 1: User Input

[1606] Users use a device (smartphone or PC) to enter legal questions or upload image data of contracts. The data entered is mainly text questions and image contracts. For example, a user might enter a question such as, "How will property be divided in the event of a divorce?" or take a photo of a newly signed contract and upload it.

[1607] Step 2: Sending data

[1608] The terminal sends the text data and image data entered by the user to the server. In this step, the input data (questions and contract images) is transferred to the server via the Internet. Specifically, the terminal divides the data into packets and sends them to a specific IP address on the server.

[1609] Step 3: Data analysis (natural language processing)

[1610] The server uses natural language processing technology (NLP) to analyze the received natural language text data. The input is the user's question text, and the output is extracted keywords and the intent of the question. Specifically, the server performs morphological analysis of the text and extracts important keywords (e.g., "divorce" and "property division"). In addition, image data of contracts is converted into text data using OCR technology. In this process, the image is scanned and converted into text data using a character recognition algorithm.

[1611] Step 4: Evaluate your emotions

[1612] The emotion engine installed on the server identifies emotions from the user's input text. The input is text after NLP processing, and the output is recognized emotional information (e.g., "anxiety" or "anger"). Specifically, the server analyzes emotional expressions in the text (e.g., "I'm worried" or "I'm anxious") to identify the user's emotion.

[1613] Step 5: Search for legal data

[1614] The server searches for relevant laws and precedents from a legal database based on the extracted keywords and phrases. The input is the extracted keywords (e.g., "divorce" and "property division"), and the output is the relevant laws and information (e.g., "Article 768 of the Civil Code"). Specifically, the server generates a database query, queries the legal database, and retrieves relevant provisions and precedents.

[1615] Step 6: Generate and serve answers

[1616] The server automatically generates an answer for the user based on the search results and analysis results. The input is legal information and emotional information, and the output is the answer provided to the user. The answer is generated in a tone that brings a sense of security, reflecting the emotional information generated by the emotion engine. For example, an answer containing reassuring language such as "According to Article 768 of the Civil Code, property division in the event of divorce is..." is generated. This answer is sent to the terminal and displayed to the user.

[1617] Step 7: Additional questions and reanalysis

[1618] If the user wants to enter an additional question, the question is sent again from the terminal to the server. The input is the new question data, and the output is a newly generated additional answer. The server again uses NLP technology and an emotion engine to analyze the question, searches the legal database for the necessary information again, and generates an additional answer. For example, in response to the additional question, "What about jointly owned houses?", the server generates an additional answer, "The specific legal treatment for jointly owned houses is..." This answer is also sent to the terminal and displayed to the user.

[1619] (Application example 2)

[1620] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1621] While conventional legal consultation systems can efficiently analyze customers' legal questions and the contents of contracts, they face the challenge of being unable to respond to customers' emotions. Furthermore, answers are often one-sided, which can leave customers feeling uneasy, and the system may be unable to respond appropriately to follow-up questions. This creates a problem that makes it difficult to improve customer satisfaction.

[1622] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1623] In this invention, the server includes means for receiving natural language input, means for analyzing the natural language input using natural language processing technology, means for searching for relevant laws and information from a legal database, means for generating an answer to the user based on the analysis result and the search information, means for analyzing the user's emotions, and means for adjusting the tone of the answer based on the emotion analysis result, thereby making it possible to provide an answer that is sensitive to the customer's emotions and gives them a sense of security.

[1624] "Natural language input" is linguistic input provided by a user through speech or text.

[1625] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human language.

[1626] A "legal database" is a searchable database that collects laws, precedents, and legal information.

[1627] "Sentiment analysis" is a technology that extracts and evaluates emotions from text data.

[1628] "Tone adjustment" is the act of adjusting the expression of a sentence according to the user's emotions.

[1629] "Image data of a contract" is a digital image representation of a paper contract.

[1630] "Image recognition technology (OCR)" is a technology that analyzes characters in an image and extracts them as text data.

[1631] A "follow-up question" is a new question asked by the user following the initial question.

[1632] The system embodying the present invention is a platform for supporting legal counselors in brick-and-mortar stores. This system uses the following hardware and software:

[1633] Hardware

[1634] Smart glasses (e.g. smart devices)

[1635] Tablet devices (e.g., personal digital assistants)

[1636] Server (e.g. cloud infrastructure)

[1637] software

[1638] Natural language processing engines (e.g., natural language processing systems)

[1639] Image recognition technology (OCR) (e.g., optical character recognition tools)

[1640] Sentiment analysis engine (e.g., sentiment analysis tool)

[1641] Legal databases (e.g., legal information databases)

[1642] Program processing explanation

[1643] 1. Input acceptance:

[1644] Users can use smart glasses or a tablet to ask legal questions by voice or text, or upload a photo of a contract.

[1645] 2. Data transfer and analysis:

[1646] The device transfers voice input, text data, or image data to the server. The server converts the voice data into text (using a voice recognition API) and analyzes the text data. For image data, an optical character recognition tool is used to recognize characters and convert them into text data.

[1647] 3. Natural Language Processing:

[1648] A natural language processing system extracts key keywords from the input data and identifies relevant legal areas.

[1649] 4. Emotion analysis:

[1650] The emotion analysis tool recognizes emotions from the user's text data, assessing them as anxiety, anger, joy, etc., and uses the results to adjust the analysis results.

[1651] 5. Legal Database Search:

[1652] The server searches the legal information database based on the specified keywords and retrieves relevant laws and precedents.

[1653] 6. Generate and provide answers:

[1654] Based on the search results and sentiment analysis results, an appropriate answer is generated. The tone of the generated answer is adjusted according to the sentiment analysis results and provided to the user. For example, it may respond in a reassuring tone, such as "In accordance with Article 90 of the Civil Code, this provision may be invalid."

[1655] 7. Follow-up questions:

[1656] If the user has additional questions, natural language processing is performed again to generate additional answers. The process is repeated depending on the additional questions, providing answers in real time.

[1657] Specific examples

[1658] 1. Customer Questions:

[1659] A customer speaks to the smart glasses and says, "I'm confused by this clause in the contract."

[1660] 2. In-store counseling:

[1661] The smart glasses recognize the question through voice recognition and forward it to the server. The server analyzes the question and identifies important keywords such as "contract" and "article." The emotion analysis tool recognizes the customer's anxiety and notifies the counselor. The server searches a legal information database to retrieve relevant laws and precedents. The server generates an answer based on the emotion analysis results and provides it to the counselor in a reassuring tone, such as "In accordance with Article 90 of the Civil Code, this article may be invalid."

[1662] Prompt Sentence Examples

[1663] I'm unclear about a particular clause in the contract and need clarification. Use sentiment analysis tools to generate legal advice with a tone that calms the customer's concerns.

[1664] This system will make legal consultations in brick-and-mortar stores more efficient and enable the provision of high-quality legal services that are sensitive to customer feelings.

[1665] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1666] Step 1:

[1667] The user inputs a question or an image of a contract through smart glasses or a tablet device, which generates voice data, text data, or image data.

[1668] Step 2:

[1669] The device transfers the voice data to the server. The voice data is input, and the server uses a voice recognition API to convert the voice data into text data. This text data is used for the next process.

[1670] Step 3:

[1671] The device transfers text data to the server. The input text data is analyzed and important keywords are extracted. This is done by a natural language processing system. Keywords are generated as output.

[1672] Step 4:

[1673] The device transfers image data to the server. Image recognition technology (OCR) is used to convert the image data into text data. This converted text data is then analyzed.

[1674] Step 5:

[1675] The server uses a natural language processing system to analyze the text data, and from the analyzed data, it identifies the relevant legal field, which is then used for the search in the next step.

[1676] Step 6:

[1677] The server uses an emotion analysis tool to extract the user's emotions from the text data. Emotions such as anxiety, anger, and joy are evaluated, and the results are output as data.

[1678] Step 7:

[1679] The server searches the legal information database based on the relevant legal field, retrieves relevant laws and precedents based on keywords, and outputs the information as data.

[1680] Step 8:

[1681] The server generates an appropriate answer based on the search results and sentiment analysis results. The answer is written and output in a tone that corresponds to the sentiment analysis results.

[1682] Step 9:

[1683] The server sends the generated answer to the terminal, the answer is received and displayed by the user, and the user enters additional questions if necessary.

[1684] Step 10:

[1685] The user enters a follow-up question, which the terminal forwards to the server.

[1686] Step 11:

[1687] The server then performs natural language processing again to analyze the additional question, extracting new keywords and outputting the analysis results.

[1688] Step 12:

[1689] The server generates an additional answer based on the analysis results, which is provided and output in a tone that corresponds to the sentiment analysis.

[1690] Step 13:

[1691] The server sends any additional responses to the device and displays them to the user. All data collected up to this point is logged and saved for future analysis and improvement.

[1692] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1693] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1694] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1695] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1696] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1697] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1698] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1699] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1700] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1701] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1702] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1703] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1704] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1705] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1706] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1707] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1708] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1709] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1710] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1711] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1712] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1713] The following is further disclosed regarding the above embodiment.

[1714] (Claim 1)

[1715] means for receiving natural language input;

[1716] means for analyzing the natural language input using natural language processing techniques;

[1717] A means of searching for relevant legislation and information from legal databases;

[1718] means for generating a response to the user based on the analysis results and the search information;

[1719] means for providing the generated answer to a user;

[1720] A system including:

[1721] (Claim 2)

[1722] A means for receiving image data of the contract;

[1723] means for converting the image data of the contract into text data using image recognition technology;

[1724] means for analyzing the text data using the natural language processing technology;

[1725] The system of claim 1 further comprising:

[1726] (Claim 3)

[1727] means for accepting follow-up questions from the user;

[1728] means for performing natural language processing again based on the additional question and reanalyzing the result;

[1729] means for generating an additional answer based on the reanalysis result;

[1730] means for providing said additional answers to a user;

[1731] The system of claim 1 further comprising:

[1732] "Example 1"

[1733] (Claim 1)

[1734] means for receiving natural language input from a terminal;

[1735] means for analyzing the natural language input using natural language processing techniques;

[1736] A means of searching for relevant legislation and information from legal databases;

[1737] A means for generating an answer for a user based on the analysis results and search information using a generative AI model;

[1738] means for transmitting the generated answer to a terminal and providing it to a user;

[1739] A system including:

[1740] (Claim 2)

[1741] A means for receiving image data of the contract;

[1742] means for converting the image data of the contract into text data using image recognition technology;

[1743] means for analyzing the text data using the natural language processing technology;

[1744] The system of claim 1 further comprising:

[1745] (Claim 3)

[1746] means for accepting follow-up questions from the user;

[1747] means for performing natural language processing again based on the additional question and reanalyzing the result;

[1748] means for generating an additional answer based on the reanalysis result;

[1749] means for transmitting the additional response to a terminal and providing the additional response to a user;

[1750] The system of claim 1 further comprising:

[1751] "Application Example 1"

[1752] (Claim 1)

[1753] means for receiving natural language input;

[1754] means for analyzing the natural language input using natural language processing techniques;

[1755] A means of searching for relevant legislation and information from legal databases;

[1756] means for generating a response to the user based on the analysis results and the search information;

[1757] A means of detecting the occurrence of road accidents and providing legal advice;

[1758] A means to check the contents of the insurance contract and the procedures to follow in the event of a traffic accident,

[1759] means for providing the generated answer to a user;

[1760] A system including:

[1761] (Claim 2)

[1762] A means for receiving image data of the contract;

[1763] means for converting the image data of the contract into text data using image recognition technology;

[1764] means for analyzing the text data using the natural language processing technology;

[1765] The system of claim 1 further comprising:

[1766] (Claim 3)

[1767] means for accepting follow-up questions from the user;

[1768] means for performing natural language processing again based on the additional question and reanalyzing the result;

[1769] means for generating an additional answer based on the reanalysis result;

[1770] means for providing said additional answers to a user;

[1771] a means for generating a prompt sentence using the generative AI model and providing the prompt sentence to the user;

[1772] The system of claim 1 further comprising:

[1773] "Example 2: Combining Emotion Engines"

[1774] (Claim 1)

[1775] means for receiving natural language input;

[1776] means for analyzing the natural language input using natural language processing techniques;

[1777] a means of searching for relevant legislation and information from legal databases;

[1778] means for generating a response to the user based on the analysis results and the search information;

[1779] means for providing the generated answer to a user;

[1780] means for identifying a user's emotion using an emotion engine and adjusting generated answers in response to said user's emotion;

[1781] A system including:

[1782] (Claim 2)

[1783] A means for receiving image data of the contract;

[1784] means for converting the image data of the contract into text data using image recognition technology;

[1785] means for analyzing the text data using the natural language processing technology;

[1786] means for assessing a user's pre-submission concerns or anxieties using said emotion engine;

[1787] 10. The system of claim 1.

[1788] (Claim 3)

[1789] means for accepting follow-up questions from the user;

[1790] means for performing natural language processing again based on the additional question and reanalyzing the result;

[1791] means for generating an additional answer based on the reanalysis result;

[1792] means for providing said additional answers to a user;

[1793] a means for considering the user's emotions in the follow-up questions using an emotion engine;

[1794] 10. The system of claim 1.

[1795] "Application example 2 when combining emotion engines"

[1796] (Claim 1)

[1797] means for receiving natural language input;

[1798] means for analyzing the natural language input using natural language processing techniques;

[1799] A means of searching for relevant legislation and information from legal databases;

[1800] means for generating a response to the user based on the analysis results and the search information;

[1801] means for providing the generated answer to a user;

[1802] means for analyzing user emotions;

[1803] means for adjusting the tone of a response based on the emotion analysis result;

[1804] A system including:

[1805] (Claim 2)

[1806] A means for receiving image data of the contract;

[1807] means for converting the image data of the contract into text data using image recognition technology;

[1808] means for analyzing the text data using the natural language processing technology;

[1809] means for adjusting the tone of the analysis result based on the emotion analysis result;

[1810] The system of claim 1 further comprising:

[1811] (Claim 3)

[1812] means for accepting follow-up questions from the user;

[1813] means for performing natural language processing again based on the additional question and reanalyzing the result;

[1814] means for generating an additional answer based on the reanalysis result;

[1815] means for providing said additional answers to a user;

[1816] means for adjusting the tone of the additional response based on the sentiment analysis result;

[1817] The system of claim 1 further comprising: [Explanation of symbols]

[1818] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving natural language input; means for analyzing the natural language input using natural language processing techniques; A means of searching for relevant legislation and information from legal databases; means for generating a response to the user based on the analysis results and the search information; means for providing the generated answer to a user; A system including:

2. A means for receiving image data of the contract; means for converting the image data of the contract into text data using image recognition technology; means for analyzing the text data using the natural language processing technology; The system of claim 1 further comprising:

3. means for accepting follow-up questions from the user; means for performing natural language processing again based on the additional question and reanalyzing the result; means for generating an additional answer based on the reanalysis result; means for providing said additional answers to a user; The system of claim 1 further comprising:

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