System

Abstract

A system is provided.SOLUTION: A system comprising: means for generating a legal document using a generative artificial intelligence model; means for generating a legal document based on user input data, the legal document generated by the generative artificial intelligence model; and means for providing the generated legal document to a 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] The creation and review of legal documents requires highly specialized knowledge, which is time-consuming and costly. This problem is particularly pronounced when creating contracts or reviewing legal notices, where it is difficult to determine their appropriateness. The declining labor force is also creating a serious shortage of legal experts. The present invention aims to solve these problems and support the rapid and accurate creation and review of legal documents. [Means for solving the problem]

[0005] The present invention provides a means for creating legal documents using a generative AI model and generating them based on user input data. A system is then constructed that includes a means for providing the generated legal documents to the user. Furthermore, the generative AI model has a means for learning laws, precedents, rules, etc., and also includes a means for providing an input interface for reviewing the content of the generated legal documents and making corrections as necessary. In this way, legal review can be expedited and manpower shortages can be alleviated.

[0006] A "generative artificial intelligence model" is a model that uses artificial intelligence technology to learn patterns from large amounts of data and has the ability to generate new data.

[0007] "Legal Documents" refers collectively to documents with legal effect, such as contracts, legal notices, applications, certificates, and terms and conditions.

[0008] "Means for creating" refers to devices or software that provide the functionality to generate documents based on the information required for a specific purpose.

[0009] The "means for providing" refers to a device or software that has the function of displaying, transmitting, or allowing a user to download the generated document.

[0010] "Means for learning" refers to devices or software that have the ability to analyze large amounts of legal-related data and have an artificial intelligence model learn the characteristics of that data.

[0011] "Input interface" refers to a screen, form, or other interactive means through which a user enters information.

[0012] "User" refers to an individual or corporation that uses this system. [Brief explanation of the drawings]

[0013] [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

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] 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).

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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."

[0034] The present invention is a system that uses generative AI to support the creation and verification of legal documents, and is implemented as follows.

[0035] Server Roles

[0036] 1. Creating and updating legal databases

[0037] The server periodically collects data such as published laws, precedents, and regulations, and generates and updates the database.

[0038] The collected data is converted into a unified format and used as training data for generative artificial intelligence models.

[0039] 2. Training generative AI models

[0040] The server uses the collected legal data to train a generative artificial intelligence model.

[0041] During training, data is divided into categories and used as training data and test data.

[0042] Evaluate the model's performance and adjust parameters as needed.

[0043] 3. Processing User Requests

[0044] Receive requests from users for legal document creation or verification.

[0045] Based on the request content, the data provided by the user is analyzed and the necessary information is extracted.

[0046] The extracted information is fed into a generative AI model to generate legal documents.

[0047] The generated document is provided to the user.

[0048] Device Role

[0049] 1. Providing a user interface

[0050] The terminal provides an intuitive input form and interface for users to create and request verification of legal documents.

[0051] It has fields for entering required information (e.g., contractor information, contract terms, etc.).

[0052] 2. Data transmission and reception

[0053] The data entered by the user is encrypted and sent securely to the server.

[0054] The generated document is received from the server and displayed to the user.

[0055] User Roles

[0056] 1. Request for document creation

[0057] The user enters the necessary information (e.g., the name of the contract holder, contract terms) into the input form on the terminal and presses the send button.

[0058] Check the input and send it to the server.

[0059] 2. Check and correct the document

[0060] The legal document returned from the server is checked, and any necessary corrections are specified via the input interface.

[0061] If there are any corrections, the document is sent to the server again and the corrected document is received.

[0062] Specific examples

[0063] Example 1: Creating an employment contract

[0064] 1. User Action:

[0065] The user selects "Create a new employment contract" on the terminal and enters the employer's name, employee's name, employment conditions, etc.

[0066] Once you have completed the input, click the send button to send the data to the server.

[0067] 2. Server processing:

[0068] The server receives the user's input data, analyzes it, and inputs it into the generative AI model.

[0069] The generative AI model generates an employment contract and sends its contents back to the user from the server.

[0070] 3. User Action:

[0071] The user checks the returned employment contract, enters corrections as necessary, and resubmits it.

[0072] Example 2: Checking legal notices

[0073] 1. User Action:

[0074] The user selects "Review Legal Notices" on their device and copies and pastes the notice.

[0075] Send a confirmation request.

[0076] 2. Server processing:

[0077] The server receives the notification content and analyzes it using the generative AI model.

[0078] Legal issues and improvement proposals are extracted and returned to the user from the server.

[0079] 3. User Action:

[0080] The user corrects the notification document based on the returned results.

[0081] In this way, the system of the present invention utilizes generative artificial intelligence models to support the rapid and accurate creation and validation of legal documents. Users can efficiently create and validate legal documents through an intuitive interface.

[0082] The processing flow will be explained below.

[0083] Server Processing

[0084] Step 1: Generate and update the legal database

[0085] The server periodically collects publicly available data on laws, precedents, regulations, etc.

[0086] The collected data is converted into a unified format and prepared as training data for the generative artificial intelligence model.

[0087] Update the legal database, add new data and replace old data with the latest.

[0088] Step 2: Training the generative AI model

[0089] The server categorizes the legal documents in the database and prepares training and test data.

[0090] The server initializes the generative AI model and trains it using the training data.

[0091] The server uses test data to evaluate the model's performance and adjusts parameters as needed.

[0092] Step 3: Processing the user request

[0093] A server receives a request from a user for legal document creation or verification.

[0094] The server analyzes the user's input data and extracts the necessary information.

[0095] The extracted information is fed into a generative AI model to generate legal documents.

[0096] The generated legal document is converted into an appropriate format for presentation to the user.

[0097] The generated legal document is transmitted to the user via encrypted communication.

[0098] Terminal processing (client side)

[0099] Step 1: Providing a User Interface

[0100] The terminal displays an input form to the user and provides an interface for accepting requests for legal document creation and confirmation.

[0101] It displays fields for information that the user must enter and provides guide and warning messages to assist with entry.

[0102] Step 2: Sending and Receiving Data

[0103] The device encrypts the user's input and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0104] The terminal receives the generated legal document from the server and displays it to the user.

[0105] Provide interactive feedback so users can make corrections as needed.

[0106] User Action

[0107] Step 1: Request for documentation

[0108] The user accurately enters the required information (contractor information, contract terms, etc.) into the input form on the device.

[0109] The user checks the contents and presses the send button to send the data to the server.

[0110] Step 2: Review and correct the document

[0111] The user receives the generated legal document returned from the server and checks the contents in detail.

[0112] If necessary, enter corrections and press the send button again to send it to the server.

[0113] The corrected document is received again from the server and a final check is performed.

[0114] Detailed processing of specific examples

[0115] Example 1: Creating an employment contract

[0116] Step 1: The user selects "Create a new employment contract" on the terminal and enters the employer name, employee name, employment conditions, etc.

[0117] Step 2: Check the information entered by the user and press the submit button to send it to the server.

[0118] Step 3: The server receives the user's input data and performs analysis.

[0119] Step 4: The server inputs the analysis results into the generation AI model and generates an employment contract.

[0120] Step 5: The server returns the generated employment contract to the user.

[0121] Step 6: The user checks the returned employment contract, enters any necessary corrections, and resubmits it.

[0122] Step 7: The server receives the correction request, regenerates the employment contract, and returns it to the user.

[0123] Example 2: Checking legal notices

[0124] Step 1: The user selects "Review Legal Notices" on their device and copies and pastes the notice.

[0125] Step 2: The user submits a confirmation request.

[0126] Step 3: The server receives the notification and analyzes it using the generative AI model.

[0127] Step 4: The server extracts legal issues and improvement proposals from the analysis results.

[0128] Step 5: The server returns the extracted results to the user.

[0129] Step 6: The user modifies the notification document based on the received results.

[0130] Example 1

[0131] 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."

[0132] Currently, creating and reviewing legal documents requires a great deal of time and effort. It is particularly difficult for users without specialized knowledge to create accurate legal documents. Furthermore, existing document creation systems often have complex user interfaces and raise concerns about data security. To solve these problems, a system that supports the efficient and accurate creation and review of legal documents using generative AI models is needed.

[0133] 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.

[0134] In this invention, the server includes means for collecting legal data, standardizing the format, and converting it into a database, means for training a generative AI model based on the database, means for encrypting and receiving data entered by a user, means for analyzing the user's input data, extracting necessary information, and inputting it into the generative AI model, and means for converting legal documents generated by the generative AI model into PDF format, encrypting the data, and providing the legal documents to the user. This allows users to quickly and accurately create and review legal documents without requiring specialized knowledge, and ensures data security.

[0135] "Legal data" refers to publicly available laws, precedents, regulations, statutes, guidelines, etc.

[0136] A "generative artificial intelligence model" refers to a generative artificial intelligence model that is trained on a large amount of legal data and has the ability to automatically generate legal documents.

[0137] "User" means any person or organization that uses this system to create or review legal documents.

[0138] "Encryption" refers to the technology of converting data being sent and received into a format that cannot be read by third parties, thereby ensuring secure communication.

[0139] "Database" means a structured digital storage system for storing and making readily accessible collected and organized legal data.

[0140] "PDF format" is an abbreviation for Portable Document Format and refers to a standard file format for electronically storing and displaying documents.

[0141] "Input interface" refers to the software window through which a user inputs information and interacts with a system.

[0142] "Analysis" refers to the process of segmenting and classifying received data, extracting specific information, and using it in a useful way.

[0143] "Training" refers to the process of using large amounts of data to train a generative artificial intelligence model and improve its performance.

[0144] The present invention is a system that uses generative AI to support the creation and verification of legal documents, and is implemented as follows.

[0145] Server Roles

[0146] 1. Creating and updating legal databases

[0147] The server periodically collects data on laws, precedents, rules, etc. that are publicly available via the Internet, and generates and updates a database, which is periodically maintained.

[0148] The collected data is converted into a standardized format (e.g., XML or JSON) and used as training data for generative artificial intelligence models.

[0149] 2. Training generative AI models

[0150] The server uses the collected legal data to train a generative AI model (e.g., GPT-3®), dividing the data into multiple categories and using them as training and test data.

[0151] After training, the model's performance is evaluated and parameters are adjusted as needed. This training is done using Python libraries such as TENSORFLOW (registered trademark) and PyTorch.

[0152] 3. Processing User Requests

[0153] The server receives requests from users for legal document creation or verification, and the data entered by the user is sent to the server in encrypted form.

[0154] The received data is analyzed on the server side, and the necessary information is extracted and input into the generative artificial intelligence model.

[0155] A generative artificial intelligence model generates legal documents, converts their contents into PDF format, encrypts them, and provides them to users.

[0156] Device Role

[0157] 1. Providing a user interface

[0158] The terminal provides an intuitive input form and interface for users to create and request verification of legal documents.

[0159] It has fields for entering required information (e.g., contractor information, contract terms, etc.).

[0160] The terminal uses HTML and JavaScript to provide interactive forms.

[0161] 2. Data transmission and reception

[0162] The terminal encrypts the data entered by the user and sends it securely to the server using the HTTPS protocol.

[0163] The generated document is received from the server, securely decrypted, and displayed to the user using a PDF viewer or similar.

[0164] User Roles

[0165] 1. Request for document creation

[0166] The user enters the necessary information (e.g., contract holder name, contract terms) into the input form on the terminal and presses the send button.

[0167] Check the input and send it to the server.

[0168] 2. Check and correct the document

[0169] The legal document returned from the server is checked, and any necessary corrections are specified via the input interface.

[0170] If there are any corrections, the document is sent to the server again and the corrected document is received.

[0171] Specific examples

[0172] Example 1: Creating an employment contract

[0173] 1. User Action:

[0174] The user selects "Create a new employment contract" on the terminal and enters the employer's name, employee's name, employment conditions, etc.

[0175] Once you have completed the input, click the send button to send the data to the server.

[0176] Example prompt: "To create a new employment contract, please enter the employer name, employee name, and employment terms."

[0177] 2. Server processing:

[0178] The server receives the user's input data, analyzes it, and inputs it into the generative AI model.

[0179] The generative AI model generates an employment contract and returns its contents to the user from the server.

[0180] 3. User Action:

[0181] The user checks the returned employment contract, enters corrections as necessary, and resubmits it.

[0182] Example 2: Checking legal notices

[0183] 1. User Action:

[0184] The user selects "Review Legal Notices" on their device and copies and pastes the notice.

[0185] Send a confirmation request.

[0186] Example prompt: "To view the legal notices, please copy and paste them."

[0187] 2. Server processing:

[0188] The server receives the notification content and analyzes it using the generative AI model.

[0189] Legal issues and improvement proposals are extracted and returned to the user from the server.

[0190] 3. User Action:

[0191] The user corrects the notification document based on the returned results.

[0192] In this way, the system of the present invention utilizes generative AI models to support the rapid and accurate creation and validation of legal documents. Users can efficiently generate and validate legal documents through an intuitive interface.

[0193] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0194] Step 1: Collect and format legal data

[0195] Input: Publicly available data such as laws, precedents, and regulations

[0196] How it works: The server periodically runs a scraping script to collect legal data from the internet, then uses a Python script to parse the raw data and convert it into a standardized format such as XML or JSON.

[0197] Data processing / data calculation: parsing and formatting raw data

[0198] Output: Legal data in a standardized format

[0199] Step 2: Update the database

[0200] Input: Legal data in a standardized format

[0201] Specific operation: The server uses an SQL query to add the converted data to the existing database, checking for duplicate data and ensuring consistency.

[0202] Data processing / data calculation: data insertion and duplication check

[0203] Output: A database containing up-to-date legal data

[0204] Step 3: Training the generative artificial intelligence model

[0205] Input: Legal data stored in a database

[0206] How it works: The server uses Python's Pandas library to load legal data by category and feed it into a generative AI model, which is trained using TensorFlow and PyTorch.

[0207] Data processing / data calculation: data pre-processing and model training

[0208] Output: A trained generative AI model

[0209] Step 4: Receiving user data

[0210] Input: A legal document request entered by a user at a terminal.

[0211] Specific operation: The user enters the required information on the device and presses the send button. The device encrypts the input information using AES and sends it to the server via HTTPS.

[0212] Data processing / data calculation: Encryption and transmission of user data

[0213] Output: Encrypted user data received by the server

[0214] Step 5: Analyze user data

[0215] Input: Encrypted user data received by the server

[0216] What happens: The server decrypts the data and parses it using a natural language processing (NLP) library to extract the necessary information (e.g., contract name, contract terms).

[0217] Data processing / data calculation: data interpretation and information extraction

[0218] Output: Parsed data to feed into a generative AI model

[0219] Step 6: Generate legal documents

[0220] Input: Parsed user data

[0221] Specific operation: The server inputs the analyzed data into the generative AI model and generates the required legal documents.

[0222] Data processing / data calculation: Document generation using generative AI models

[0223] Output: Generated legal document

[0224] Step 7: Encrypt and return the document

[0225] Input: Generated legal document

[0226] What happens: The server converts the generated legal document into PDF format, encrypts it using AES, and then returns the encrypted PDF to the user.

[0227] Data processing / data calculation: document format conversion and encryption

[0228] Output: Encrypted legal document in PDF format

[0229] Step 8: Review the document and request corrections

[0230] Input: Encrypted legal document in PDF format

[0231] Specific operation: The device decrypts the encrypted PDF received from the server and displays it to the user. The user checks the displayed document and, if any corrections are required, enters the corrections again and submits it.

[0232] Data processing / data calculation: Displaying documents and inputting corrections

[0233] Output: Correction request data

[0234] Through these steps, the system can efficiently and safely create and verify legal documents according to user requirements. At each processing step, specific actions and data processing / calculation are performed, resulting in fast and accurate document generation.

[0235] (Application example 1)

[0236] 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."

[0237] In recent years, the creation and review of security-related legal documents and regulations has increased, requiring rapid and accurate responses. However, creating and reviewing these documents requires specialized knowledge, which is a significant time and labor-intensive process. Furthermore, there is a lack of efficient means for correcting and providing feedback on created documents. Therefore, there is a need for a system that allows users to easily create security-related legal documents and regulations and quickly review and modify them.

[0238] 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.

[0239] In this invention, the server includes means for creating legal documents using a generative AI model, means for generating the legal documents created by the generative AI model based on user input data, means for providing the generated legal documents to the user via a smartphone application, means for creating and confirming security policies and regulations related to the legal documents, and means for correcting the confirmed documents by feeding them back to the AI ​​model. This enables users to efficiently create, confirm, and correct security-related legal documents and regulations without requiring specialized knowledge.

[0240] A "generative artificial intelligence model" is an algorithm that learns from large amounts of legal data and generates legal documents based on user input.

[0241] A "legal document" is an official document created based on laws, regulations, and precedents and has the force of law.

[0242] A "smartphone application" is a software program that runs on a smartphone and can be operated interactively by a user.

[0243] A "security policy" is a document that sets out standards and guidelines for ensuring safety when using an information system.

[0244] A "policy" is a document that sets out the rules and procedures relating to a particular activity or service.

[0245] "Feedback" is the process of sending user instructions for corrections and improvements to a legal document once it has been generated back to the generative AI model.

[0246] System Overview

[0247] This system uses generative AI models to help users create, review, and modify legal documents and security policies, and is implemented primarily through server, smartphone, and user operations.

[0248] Server Roles

[0249] Hardware and Software

[0250] Hardware: High-performance server (e.g., AWS (registered trademark) EC2)

[0251] Software: Legal database management, generative AI models (e.g., OpenAI® GPT-4®)

[0252] Data Processing

[0253] 1. Data collection and formatting:

[0254] The server periodically collects legal, legal precedent, regulatory and security-related data and converts it into a unified format.

[0255] This data is used as training data for the generative AI model.

[0256] 2. Document generation using AI models:

[0257] The server receives input data from the user and extracts the required information.

[0258] The extracted information is fed into a generative AI model to automatically generate legal documents or security policies.

[0259] 3. Feedback and Corrections:

[0260] Based on user feedback, the generated document is revised.

[0261] The revised document is then fed back into the AI ​​model to generate the final document.

[0262] Device Role

[0263] Hardware and Software

[0264] Hardware: Smartphone

[0265] Software: Mobile app development frameworks (e.g., React Native, Flutter (registered trademark))

[0266] User Interface

[0267] 1. User input form:

[0268] The terminal provides users with intuitive input forms to collect information required for legal documents and security policies.

[0269] 2. Data transmission and reception:

[0270] The terminal encrypts the user's input data and sends it securely to the server.

[0271] Receive the generated document from the server and display it to the user.

[0272] User Roles

[0273] 1. Request and review of documentation:

[0274] The user inputs the necessary information into the input form on the terminal and requests the creation of a document.

[0275] The generated document is checked, and corrections are entered as necessary, and fed back to the server.

[0276] Specific examples

[0277] Creating a New Security Policy

[0278] 1. User Action:

[0279] The user selects "Create a new security policy" on the smartphone app and enters the policy name, scope, key requirements, etc.

[0280] Once you have completed the input, click the send button to send the data to the server.

[0281] 2. Server processing:

[0282] The server receives the user's input data, analyzes it, and inputs it into the generative AI model.

[0283] The generative AI model generates a new security policy and sends it back to the user from the server.

[0284] 3. User Action:

[0285] The user checks the returned security policy, enters corrections as necessary, and resubmits it.

[0286] Example prompt sentence:

[0287] Generate a new security policy document based on the following user-specified information:

[0288] Policy Name: System Access Control Policy

[0289] Scope: Company-wide

[0290] Main requirements:

[0291] Strengthened user authentication measures

[0292] Recording and monitoring access logs

[0293] Regular Security Reviews

[0294] The generated document should be returned in the following format:

[0295] This format allows users to significantly reduce the amount of work required in the past and quickly and accurately create, check, and modify security-related legal documents and regulations.

[0296] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0297] Step 1:

[0298] User Input

[0299] The user opens the application on their smartphone and selects "Create a new security policy." The user enters the required information, such as the policy name, scope, and key requirements. This information is entered as text into the input form fields.

[0300] Input: User's text data (policy name, scope, main requirements, etc.)

[0301] Output: Input data stored on the device

[0302] Step 2:

[0303] Data transmission

[0304] The terminal encrypts the data entered by the user and sends it to the server using a secure communication protocol, with the SSL / TLS protocol used between the server and the terminal.

[0305] Input: Input data stored on the device

[0306] Output: Encrypted data sent to the server

[0307] Step 3:

[0308] Data reception and analysis

[0309] The server decrypts the received data and analyzes the user's input data to extract the necessary information. Python libraries (e.g., pandas, numpy) are used for data analysis.

[0310] Input: Encrypted data sent to the server

[0311] Output: Parsed information (policy name, scope, key requirements, etc.)

[0312] Step 4:

[0313] Input to generative AI models

[0314] The server inputs the analyzed information into a generative AI model (e.g., OpenAI GPT-4) as a prompt sentence, which specifically contains the user's input data.

[0315] Input: Parsed information (policy name, scope, key requirements, etc.)

[0316] Output: Prompt sentence to the generative AI model

[0317] Step 5:

[0318] Document Generation

[0319] The generative AI model generates a new security policy document based on the prompt sentence. In this generation process, the AI ​​model creates the document based on the legal and security-related data it has previously learned.

[0320] Input: Prompt sentence for generative AI model

[0321] Output: Generated security policy document

[0322] Step 6:

[0323] Document Transmission

[0324] The server receives the generated security policy document, re-encrypts it, and sends it to the terminal. Again, the SSL / TLS protocol is used between the server and the terminal.

[0325] Input: The security policy document to be generated

[0326] Output: Encrypted document data sent to the device

[0327] Step 7:

[0328] User confirmation and correction

[0329] The terminal decrypts the received document data and displays it to the user. The user checks the document and, if necessary, enters corrections. The corrections are again entered as text in a field on the terminal.

[0330] Input: Encrypted document data

[0331] Output: The security policy document that is displayed to the user

[0332] Step 8:

[0333] Send corrected data

[0334] The device re-encrypts the data, including any corrections entered by the user, and sends it to the server, which receives the data and repeats the process.

[0335] Input: Corrected input data

[0336] Output: Encrypted data sent to the server

[0337] The system's processing steps integrate the entire process and allow users to operate it intuitively. Rapid feedback and correction of generated documents greatly streamlines the generation and review of legal documents and security policies.

[0338] 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.

[0339] This invention combines a system that uses generative AI to assist in the creation and review of legal documents with an emotion engine that recognizes the user's emotions. This system is implemented as follows.

[0340] Server Roles

[0341] 1. Creating and updating legal databases

[0342] The server periodically collects publicly available data on laws, precedents, rules, etc., and generates and updates the database.

[0343] The collected data is converted into a unified format and prepared as training data for the generative artificial intelligence model.

[0344] 2. Training generative AI models

[0345] The server categorizes the legal documents in the database and prepares training and test data.

[0346] The server initializes the generative AI model and trains it using the training data.

[0347] The server uses test data to evaluate the model's performance and adjusts parameters as needed.

[0348] 3. Processing User Requests

[0349] A server receives a request from a user for legal document creation or verification.

[0350] The server analyzes the user's input data and extracts the necessary information.

[0351] The extracted information is fed into a generative AI model to generate legal documents.

[0352] The generated legal document is converted into an appropriate format for presentation to the user.

[0353] Terminal role (client side)

[0354] 1. Providing a user interface

[0355] The terminal displays an input form to the user and provides an interface for accepting requests for legal document creation and confirmation.

[0356] It has fields for entering necessary information (e.g., contractor information, contract terms, etc.).

[0357] 2. Operation of the Emotion Engine

[0358] An emotion engine built into the device analyzes the user's facial expressions and voice in real time to determine their emotional state.

[0359] The emotion engine adjusts the interface and the content of the presented documents appropriately depending on the user's emotional state.

[0360] 3. Data transmission and reception

[0361] The data entered by the user is encrypted and sent to the server using a secure communication protocol (e.g., HTTPS).

[0362] The generated legal document is received from the server and displayed to the user.

[0363] Provide interactive feedback so users can make corrections as needed.

[0364] User Roles

[0365] 1. Request for document creation

[0366] The user accurately enters the required information (contractor information, contract terms, etc.) into the input form on the device.

[0367] The user checks the contents and presses the send button to send the data to the server.

[0368] 2. Check and correct the document

[0369] The user receives the generated legal document returned from the server and checks the contents in detail.

[0370] If necessary, enter corrections and press the send button again to send it to the server.

[0371] The corrected document is received again from the server and a final check is performed.

[0372] Specific examples

[0373] Example 1: Creating an employment contract

[0374] 1. User Action

[0375] The user selects "Create a new employment contract" on the terminal and enters the employer's name, employee's name, employment conditions, etc.

[0376] The emotion engine analyzes the user's facial expressions and voice, and if it detects emotions such as tension or anxiety, it makes the interface gentler and displays messages encouraging relaxation.

[0377] Once you have completed the input, click the send button to send the data to the server.

[0378] 2. Server Processing

[0379] The server receives the user's input data and performs analysis.

[0380] The analysis results are input into a generative AI model to generate an employment contract.

[0381] The generated employment contract is returned to the user.

[0382] 3. User Actions

[0383] The user checks the returned employment contract, enters corrections as necessary, and resubmits it.

[0384] Depending on the situation, the emotion engine will analyze the user's emotions again and provide a support message.

[0385] Example 2: Checking legal notices

[0386] 1. User Action

[0387] The user selects "Review Legal Notices" on their device and copies and pastes the notice.

[0388] If the emotion engine detects stress or anxiety, it will provide reassurance by displaying supportive messages and brief explanations.

[0389] Send a confirmation request.

[0390] 2. Server Processing

[0391] The server receives the notification content and analyzes it using the generative AI model.

[0392] Legal issues and improvement proposals are extracted and returned to the user.

[0393] 3. User Actions

[0394] The user modifies the notification document based on the received results.

[0395] An emotion engine continues to monitor the user's emotional state and provides supportive messages as needed.

[0396] In this way, the system of the present invention utilizes generative artificial intelligence models to not only assist in the rapid and accurate creation and review of legal documents, but also respond to the user's emotional state, thereby improving the user experience and reducing the stress of legal work.

[0397] The processing flow will be explained below.

[0398] Server Processing

[0399] Step 1: Generate and update the legal database

[0400] The server periodically collects publicly available data on laws, precedents, regulations, etc.

[0401] The collected data is converted into a unified format and prepared as training data for the generative artificial intelligence model.

[0402] Update the legal database, add new data and replace old data with the latest.

[0403] Step 2: Training the generative AI model

[0404] The server categorizes the legal documents in the database and prepares training and test data.

[0405] The server initializes the generative AI model and trains it using the training data.

[0406] The server uses test data to evaluate the model's performance and adjusts parameters as needed.

[0407] Step 3: Processing the user request

[0408] A server receives a request from a user for legal document creation or verification.

[0409] The server analyzes the user's input data and extracts the necessary information.

[0410] The extracted information is fed into a generative AI model to generate legal documents.

[0411] The generated legal document is converted into an appropriate format for presentation to the user.

[0412] The generated legal document is transmitted to the user via encrypted communication.

[0413] Terminal processing (client side)

[0414] Step 1: Providing a User Interface

[0415] The terminal displays an input form to the user and provides an interface for accepting requests for legal document creation and confirmation.

[0416] It displays fields for information that the user must enter and provides guide and warning messages to assist with entry.

[0417] Step 2: Emotion Engine in Action

[0418] An emotion engine built into the device analyzes the user's facial expressions and voice in real time to determine their emotional state.

[0419] The emotion engine adjusts the interface and the content of the presented documents appropriately depending on the user's emotional state.

[0420] Step 3: Sending and Receiving Data

[0421] The device encrypts the user's input and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0422] The terminal receives the generated legal document from the server and displays it to the user.

[0423] Provide interactive feedback so users can make corrections as needed.

[0424] User Action

[0425] Step 1: Request for documentation

[0426] The user accurately enters the required information (contractor information, contract terms, etc.) into the input form on the device.

[0427] The user checks the contents and presses the send button to send the data to the server.

[0428] Step 2: Review and correct the document

[0429] The user receives the generated legal document returned from the server and checks the contents in detail.

[0430] If necessary, enter corrections and press the send button again to send it to the server.

[0431] The corrected document is received again from the server and a final check is performed.

[0432] Detailed processing of specific examples

[0433] Example 1: Creating an employment contract

[0434] Step 1: The user selects "Create a new employment contract" on the terminal and enters the employer name, employee name, employment conditions, etc.

[0435] Step 2: The emotion engine analyzes the user's facial expressions and voice, and if it detects emotions such as tension or anxiety, it softens the interface and displays a relaxing message.

[0436] Step 3: The user checks the input and presses the submit button to send it to the server.

[0437] Step 4: The server receives the user's input data and performs analysis.

[0438] Step 5: The server inputs the analysis results into the generation AI model and generates an employment contract.

[0439] Step 6: The server returns the generated employment contract to the user.

[0440] Step 7: The user checks the returned employment contract, enters corrections as necessary, and resubmits it.

[0441] Step 8: The server receives the correction request, regenerates the employment contract, and returns it to the user.

[0442] Step 9: User gives final review and approval.

[0443] Example 2: Checking legal notices

[0444] Step 1: The user selects "Review Legal Notices" on their device and copies and pastes the notice.

[0445] Step 2: If the emotion engine detects stress or anxiety, it displays a supportive message or brief explanation to provide reassurance.

[0446] Step 3: The user submits a confirmation request.

[0447] Step 4: The server receives the notification and analyzes it using the generative AI model.

[0448] Step 5: The server extracts legal issues and improvement proposals from the analysis results.

[0449] Step 6: The server returns the extracted results to the user.

[0450] Step 7: The user modifies the notification document based on the returned results.

[0451] Step 8: The emotion engine continues to monitor the user's emotional state and provides support messages as needed.

[0452] Step 9: The user performs a final check and completes the correction.

[0453] Example 2

[0454] 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."

[0455] While conventional legal document creation systems have achieved a certain level of accuracy and speed in the creation of documents, they lack the functionality to consider the user's emotional state, which hinders the user experience.Furthermore, there are still concerns about privacy and data confidentiality due to the lack of secure means to send and receive user-entered data and generated documents.

[0456] 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.

[0457] In this invention, the server includes a means for creating legal documents using a generative artificial intelligence model, a means for using an emotion engine that analyzes the user's emotional state and appropriately adjusts the interface and document content, and a means for providing the generated legal documents to the user. This makes it possible to provide an interface that takes the user's emotional state into consideration while maintaining the accuracy and speed of the legal documents. Furthermore, by adding a means for securely sending and receiving data entered by the user and the generated documents, concerns about privacy and confidentiality can be alleviated.

[0458] "Generative artificial intelligence model" refers to a machine learning algorithm for automatically generating legal documents.

[0459] "Legal Document" means a document containing legal content, such as a contract, legal notice, or legal precedent.

[0460] "User-input data" refers to data entered by a user to provide information necessary for generating or requesting validation of a legal document.

[0461] An "emotion engine" refers to technology that analyzes a user's facial expressions and voice to determine the user's emotional state in real time.

[0462] "Interface" refers to the input and output means by which a user interacts with a system.

[0463] "Data transmission and reception" refers to the means of communication for securely exchanging data between the user and the server.

[0464] "Encryption" refers to the technology of converting data into a format that cannot be deciphered by third parties in order to transmit the data securely.

[0465] "Training data" refers to the data set used to train a generative AI model.

[0466] "Test data" refers to a dataset used to evaluate the performance of a generative AI model after training.

[0467] "Analysis" refers to the process of analyzing input data and generated documents to extract necessary information.

[0468] The present invention is a system that uses a generative AI model to assist in the creation and review of legal documents, and also combines it with an emotion engine that analyzes user emotions. Specific embodiments of this system are described below.

[0469] Server Roles

[0470] Hardware and Software Use

[0471] The server should be equipped with a high-performance processor and large memory capacity. Specifically, a server equipped with a GPU (e.g., a machine equipped with an NVIDIA GPU) is preferable. For software, a machine learning framework such as TensorFlow or PyTorch is used to build, train, and run the generative AI model.

[0472] Creating and updating legal databases

[0473] The server regularly collects publicly available data on laws, precedents, rules, etc., and generates and updates the database. The collected data is converted into a unified format and used as training data for the generative AI model. This ensures that the latest legal information is always available.

[0474] Training generative AI models

[0475] The server categorizes legal documents in the database, prepares training data and test data, initializes a generative AI model (e.g., GPT-3, BERT), and trains the model using the training data. Performance is evaluated and parameters are adjusted as needed to enable highly accurate legal document generation.

[0476] Processing user requests

[0477] The server receives a request from a user to create or review a legal document, analyzes the input data, extracts the necessary information, inputs the extracted information into a generative AI model to generate the legal document, and then converts the generated legal document into an appropriate format (e.g., PDF or Word document) and provides it to the user.

[0478] Terminal role (client side)

[0479] Hardware and Software Use

[0480] The device can be a PC, tablet, smartphone, etc. The device needs a camera and microphone to support the operation of the emotion engine. The software used is an emotion analysis engine (e.g., OpenFace or Microsoft® Azure® Face API).

[0481] Providing a user interface

[0482] The terminal displays an input form to the user, providing an interface for accepting requests for legal document creation and confirmation, along with fields for entering required information (e.g., contract information, contract terms, etc.).

[0483] Emotion Engine Operation

[0484] An emotion engine built into the device analyzes the user's facial expressions and voice in real time to determine their emotional state. The emotion engine then adjusts the interface and the content of the documents provided appropriately based on the user's emotional state. For example, if the user is nervous, it displays a message encouraging them to relax.

[0485] Sending and receiving data

[0486] Encrypts the data entered by the user and sends it to the server using a secure communication protocol (e.g., HTTPS), receives the generated legal document from the server and displays it to the user, and provides interactive feedback to the user to allow them to make corrections, if necessary.

[0487] User Roles

[0488] Document creation request

[0489] The user accurately enters the necessary information into the input form on the terminal and presses the send button to send the data to the server.

[0490] Document review and correction

[0491] The user receives the generated legal document sent from the server, checks its contents in detail, enters corrections as necessary, and presses the send button again to send it to the server. The corrected document is then received again from the server and a final check is made.

[0492] Specific examples

[0493] Example 1: Creating an employment contract

[0494] 1. User Action

[0495] The user selects "Create a new employment contract" on the terminal and enters employer information, employee information, and employment conditions.

[0496] The emotion engine detects tension from the user's facial expressions and voice and displays the message, "Please relax and continue typing."

[0497] 2. Server Processing

[0498] The server receives and analyzes the user's input data and inputs it into the generative AI model.

[0499] Generate an employment contract, convert it to PDF and return it to the device.

[0500] 3. User Actions

[0501] The user reviews the returned contract, enters any necessary corrections, and resubmits it. The emotion engine continues to monitor emotions and displays support messages.

[0502] Example 2: Checking legal notices

[0503] 1. User Action

[0504] The user selects "Review Legal Notice" on the device and enters the notice.

[0505] The emotion engine detects stress and displays, "Don't worry. An expert will check."

[0506] 2. Server Processing

[0507] The server receives the notification content, analyzes it, and inputs it into the generative AI model.

[0508] Legal issues and improvement proposals are extracted and returned to the user.

[0509] 3. User Actions

[0510] The user modifies the notification based on the returned results. The emotion engine continues to monitor emotions and displays support messages as needed.

[0511] Prompt Sentence Examples

[0512] 1. Please create a new employment contract. The employer information is 'Company A', the employee information is 'Mr. B', and the employment conditions are 'Full-time, monthly salary 300,000 yen'.

[0513] 2. What is the problem with this legal notice? The notice reads: 'This is a notice from Company X to Company Y.'

[0514] From the above, this system effectively combines generative AI models and an emotion engine to generate and verify legal documents quickly and accurately, while providing support tailored to the user's emotional state, thereby reducing the stress of legal work and improving the user experience.

[0515] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0516] Step 1:

[0517] Data collection and database generation and updating

[0518] The server collects public legal data (e.g., legal data, precedents, and rules). Inputs include data from internet legal databases and court websites. It stores this data in local storage.

[0519] The server converts the collected data into a unified format, for example, converting data in different formats into XML or JSON.

[0520] The server updates the existing database with new or updated data, checks for duplicates and inconsistencies, and cleans the data.

[0521] The output is a cleaned legal document database in a unified format.

[0522] Step 2:

[0523] Training generative AI models

[0524] The server splits the legal document data in the database into training data and test data. The input is the unified database obtained in the previous step.

[0525] The server initializes a generative AI model (e.g., GPT-3, BERT) and trains it with training data. Specifically, it uses TensorFlow or PyTorch to train the model while feeding it data.

[0526] The server evaluates the trained model on the test data and adjusts the parameters as needed.

[0527] The output is a trained generative AI model capable of generating legal documents with high accuracy.

[0528] Step 3:

[0529] Processing user requests

[0530] The server receives requests from users to create or verify legal documents, and the input includes user input data sent from the terminal via HTTPS.

[0531] The server analyzes the received data and extracts necessary information (e.g., contract information, legal terms). Data analysis uses natural language processing technology. Specifically, it uses a text analysis algorithm.

[0532] The server inputs the extracted information into a generative AI model to generate the necessary legal documents.

[0533] The server converts the generated legal document into an appropriate format (e.g., PDF, Word document) and provides it to the user.

[0534] As an output, a legal document generated based on the user's request is sent to the terminal.

[0535] Step 4:

[0536] Emotion Engine Operation

[0537] The emotion engine built into the device analyzes the user's facial expressions and voice in real time. The inputs include facial expression data and voice data obtained through the camera and microphone.

[0538] The device determines the user's emotional state based on the analysis results of the emotion engine, for example, using facial recognition technology or voice tone analysis.

[0539] The device will adjust the interface and messages provided appropriately depending on the user's emotional state.

[0540] The output is a tailored interface or message that reflects the user's emotional state.

[0541] Step 5:

[0542] User data transmission and feedback

[0543] The user enters the necessary information into the input fields on the terminal and presses the send button. Input includes contract information, legal terms, etc.

[0544] The terminal encrypts the user's input data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0545] The server transmits the generated legal document to the terminal, where it is received by the user.

[0546] The user checks the generated document and, if necessary, inputs corrections through the terminal interface, which then resends it.

[0547] The device provides interactive feedback, allowing users to make corrections, and the emotion engine continuously monitors the user's emotional state and displays appropriate support messages.

[0548] The output is a final legal document that the user has reviewed and corrected.

[0549] (Application example 2)

[0550] 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."

[0551] Conventional legal document drafting systems do not take into account the user's emotional state, which means they are unable to provide efficient support, especially to users who feel nervous or anxious. Furthermore, the lack of emotion analysis prevents an improved user experience and makes it difficult to reduce stress and anxiety.

[0552] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for creating a legal document using a generative AI model; means for generating the legal document created by the generative AI model based on user input data; means for providing the generated legal document to the user; means for combining an emotion engine that analyzes the user's emotional state; and means for adjusting the interface and the content of the provided document based on the emotion data generated by the emotion engine. This makes it possible to support the creation and review of legal documents individually tailored according to the user's emotional state, thereby improving the user experience and reducing stress and anxiety.

[0553] A "generative artificial intelligence model" is a model that uses machine learning algorithms to create legal documents.

[0554] "Legal documents" refers to legal documents such as contracts, regulations, notices, etc.

[0555] "User input data" refers to information and conditions provided by a user for document creation.

[0556] The "emotion engine" is a system that analyzes the user's facial expressions and voice to determine their emotional state.

[0557] "Emotional Data" refers to information about a user's emotional state generated by an emotion engine.

[0558] "Interface" refers to the screen and operating means that allow a user to interact with a system.

[0559] This invention is a system that combines a generative AI model and an emotion engine. The system is mainly composed of a server and a terminal, and provides emotional support to users when creating and reviewing legal documents. Specific embodiments for implementing this invention are described below.

[0560] Server Roles

[0561] The server performs the following processing.

[0562] 1. Collection and organization of legal data:

[0563] The server periodically collects data on laws, precedents, regulations, etc., creates and updates a database, converts this data into a unified format, and prepares it as training data for generative AI models.

[0564] 2. Training the generative AI model:

[0565] The server categorizes legal document data and uses it to train a generative AI model. After training, the model's performance is evaluated and parameters are adjusted as needed.

[0566] 3. Processing User Requests:

[0567] The server receives a request from a user to create or verify a legal document, analyzes the user's input data, inputs the analyzed data into a generative AI model to generate the required legal document, and then converts the generated legal document into an appropriate format for delivery to the user.

[0568] Terminal role (client side)

[0569] The terminal performs the following processing:

[0570] 1. Providing the user interface:

[0571] The terminal displays an input form to the user, providing an interface for accepting requests for legal document creation and confirmation, and includes fields for entering required information (contractor information, contract terms, etc.).

[0572] 2. How the Emotion Engine works:

[0573] An emotion engine built into the device analyzes the user's facial expressions and voice in real time to determine their emotional state, and then adjusts the interface and the content of the documents presented appropriately based on the user's emotional state.

[0574] 3. Sending and receiving data:

[0575] The device encrypts the data entered by the user and sends it to the server using a secure communication protocol (e.g., HTTPS), and receives the generated legal document from the server and displays it to the user, providing interactive feedback so that the user can make corrections if necessary.

[0576] User Roles

[0577] 1. Document Request:

[0578] The user accurately enters the required information into the input form on the device, and when the input is complete, presses the send button to send the data to the server.

[0579] 2. Document review and correction:

[0580] The user receives the generated legal document returned from the server, checks its contents in detail, enters corrections as necessary, and presses the send button again to send it to the server. The user then receives the corrected document again and makes a final check.

[0581] Specific examples

[0582] Example 1: Creating an employment contract

[0583] The user selects "Create a new employment contract" and enters the employer's name, employee's name, employment conditions, etc. The emotion engine analyzes the user's facial expressions and voice, and if it detects emotions such as tension or anxiety, it makes the interface gentler and displays a message encouraging relaxation. Once the input is complete, the user presses the send button to send the data to the server.

[0584] The server receives the user's input data, analyzes it, inputs the analysis results into the generative AI model to generate an employment contract, and then returns the generated employment contract to the user.

[0585] The user checks the returned employment contract, enters any necessary corrections, and resubmits it. Depending on the situation, the emotion engine will again analyze the user's emotions and provide a support message.

[0586] Example 2: Checking legal notices

[0587] The user selects "Confirm Legal Notice" and enters the notification content. If the emotion engine senses stress or anxiety, it provides a sense of security by displaying a support message or a simple explanation. When a confirmation request is sent, the server receives the notification content and analyzes it using a generative AI model. Legal issues and improvement proposals are extracted and sent back to the user.

[0588] The user can modify the notification based on the received results, and the emotion engine continues to monitor the user's emotional state and provide support messages as needed.

[0589] Prompt Sentence Examples

[0590] "Please create a new employment contract. Please use the following information:

[0591] Employer Name: Tanaka

[0592] Employee name: Suzuki

[0593] Employment conditions: Full-time, annual salary of 6 million yen, two days off per week

[0594] Using this prompt, the generative AI model can quickly and accurately generate the required legal documents.

[0595] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0596] Step 1:

[0597] The terminal displays an input form to the user and accepts requests for legal document creation and confirmation. The user enters the necessary information, such as contract holder information and contract terms. The terminal encrypts the entered data and sends it to the server using a secure communication protocol (e.g., HTTPS). Contract holder information and contract terms are provided as input data, and subsequent document generation is based on this.

[0598] Step 2:

[0599] The server receives and analyzes input data sent by the user. The input data includes the name of the contracting party, contract terms, etc., and the server analyzes this data to extract the necessary information. The analysis results are input into a generative AI model to generate the legal document desired by the user. For example, in response to a request to "create a new employment contract," a corresponding employment contract is generated.

[0600] Step 3:

[0601] The server converts the generated legal document into an appropriate format and returns it to the terminal, including checking and formatting the content of the generated document. The output legal document is provided in a format that is easy for the user to review.

[0602] Step 4:

[0603] The terminal receives the legal document returned from the server and displays it to the user. The user checks the displayed document and makes any necessary corrections. The terminal then sends the information added or corrected by the user back to the server. The user's corrected data is re-entered through the interface, thereby improving the accuracy of the document.

[0604] Step 5:

[0605] An emotion engine built into the device analyzes the user's facial expressions and voice to determine their emotional state in real time. If the emotion engine detects the user's emotional state (e.g., tension or anxiety), the device adjusts the interface and display content. For example, it may display a relaxing message. This allows the user to use the system with peace of mind.

[0606] Step 6:

[0607] The final legal document that the user has reviewed and corrected is then sent from the device to the server, where it is reviewed and formatted again.The final output is a legal document that is accurate and takes into account the user's emotional state and is provided to the user.

[0608] These steps result in a system where a comprehensive legal document creation and review process is achieved by combining generative AI models and an emotion engine.

[0609] 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.

[0610] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0611] 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.

[0612] [Second embodiment]

[0613] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0614] 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.

[0615] 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).

[0616] 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.

[0617] 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.

[0618] 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).

[0619] 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.

[0620] 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.

[0621] 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.

[0622] 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.

[0623] In the smart glasses 214, the 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.

[0624] 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."

[0625] The present invention is a system that uses generative AI to support the creation and verification of legal documents, and is implemented as follows.

[0626] Server Roles

[0627] 1. Creating and updating legal databases

[0628] The server periodically collects data such as published laws, precedents, and regulations, and generates and updates the database.

[0629] The collected data is converted into a unified format and used as training data for generative artificial intelligence models.

[0630] 2. Training generative AI models

[0631] The server uses the collected legal data to train a generative artificial intelligence model.

[0632] During training, data is divided into categories and used as training data and test data.

[0633] Evaluate the model's performance and adjust parameters as needed.

[0634] 3. Processing User Requests

[0635] Receive requests from users for legal document creation or verification.

[0636] Based on the request content, the data provided by the user is analyzed and the necessary information is extracted.

[0637] The extracted information is fed into a generative AI model to generate legal documents.

[0638] The generated document is provided to the user.

[0639] Device Role

[0640] 1. Providing a user interface

[0641] The terminal provides an intuitive input form and interface for users to create and request verification of legal documents.

[0642] It has fields for entering required information (e.g., contractor information, contract terms, etc.).

[0643] 2. Data transmission and reception

[0644] The data entered by the user is encrypted and sent securely to the server.

[0645] The generated document is received from the server and displayed to the user.

[0646] User Roles

[0647] 1. Request for document creation

[0648] The user enters the necessary information (e.g., the name of the contract holder, contract terms) into the input form on the terminal and presses the send button.

[0649] Check the input and send it to the server.

[0650] 2. Check and correct the document

[0651] The legal document returned from the server is checked, and any necessary corrections are specified via the input interface.

[0652] If there are any corrections, the document is sent to the server again and the corrected document is received.

[0653] Specific examples

[0654] Example 1: Creating an employment contract

[0655] 1. User Action:

[0656] The user selects "Create a new employment contract" on the terminal and enters the employer's name, employee's name, employment conditions, etc.

[0657] Once you have completed the input, click the send button to send the data to the server.

[0658] 2. Server processing:

[0659] The server receives the user's input data, analyzes it, and inputs it into the generative AI model.

[0660] The generative AI model generates an employment contract and sends its contents back to the user from the server.

[0661] 3. User Action:

[0662] The user checks the returned employment contract, enters corrections as necessary, and resubmits it.

[0663] Example 2: Checking legal notices

[0664] 1. User Action:

[0665] The user selects "Review Legal Notices" on their device and copies and pastes the notice.

[0666] Send a confirmation request.

[0667] 2. Server processing:

[0668] The server receives the notification content and analyzes it using the generative AI model.

[0669] Legal issues and improvement proposals are extracted and returned to the user from the server.

[0670] 3. User Action:

[0671] The user corrects the notification document based on the returned results.

[0672] In this way, the system of the present invention utilizes generative artificial intelligence models to support the rapid and accurate creation and validation of legal documents. Users can efficiently create and validate legal documents through an intuitive interface.

[0673] The processing flow will be explained below.

[0674] Server Processing

[0675] Step 1: Generate and update the legal database

[0676] The server periodically collects publicly available data on laws, precedents, regulations, etc.

[0677] The collected data is converted into a unified format and prepared as training data for the generative artificial intelligence model.

[0678] Update the legal database, add new data and replace old data with the latest.

[0679] Step 2: Training the generative AI model

[0680] The server categorizes the legal documents in the database and prepares training and test data.

[0681] The server initializes the generative AI model and trains it using the training data.

[0682] The server uses test data to evaluate the model's performance and adjusts parameters as needed.

[0683] Step 3: Processing the user request

[0684] A server receives a request from a user for legal document creation or verification.

[0685] The server analyzes the user's input data and extracts the necessary information.

[0686] The extracted information is fed into a generative AI model to generate legal documents.

[0687] The generated legal document is converted into an appropriate format for presentation to the user.

[0688] The generated legal document is transmitted to the user via encrypted communication.

[0689] Terminal processing (client side)

[0690] Step 1: Providing a User Interface

[0691] The terminal displays an input form to the user and provides an interface for accepting requests for legal document creation and confirmation.

[0692] It displays fields for information that the user must enter and provides guide and warning messages to assist with entry.

[0693] Step 2: Sending and Receiving Data

[0694] The device encrypts the user's input and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0695] The terminal receives the generated legal document from the server and displays it to the user.

[0696] Provide interactive feedback so users can make corrections as needed.

[0697] User Action

[0698] Step 1: Request for documentation

[0699] The user accurately enters the required information (contractor information, contract terms, etc.) into the input form on the device.

[0700] The user checks the contents and presses the send button to send the data to the server.

[0701] Step 2: Review and correct the document

[0702] The user receives the generated legal document returned from the server and checks the contents in detail.

[0703] If necessary, enter corrections and press the send button again to send it to the server.

[0704] The corrected document is received again from the server and a final check is performed.

[0705] Detailed processing of specific examples

[0706] Example 1: Creating an employment contract

[0707] Step 1: The user selects "Create a new employment contract" on the terminal and enters the employer name, employee name, employment conditions, etc.

[0708] Step 2: Check the information entered by the user and press the submit button to send it to the server.

[0709] Step 3: The server receives the user's input data and performs analysis.

[0710] Step 4: The server inputs the analysis results into the generation AI model and generates an employment contract.

[0711] Step 5: The server returns the generated employment contract to the user.

[0712] Step 6: The user checks the returned employment contract, enters any necessary corrections, and resubmits it.

[0713] Step 7: The server receives the correction request, regenerates the employment contract, and returns it to the user.

[0714] Example 2: Checking legal notices

[0715] Step 1: The user selects "Review Legal Notices" on their device and copies and pastes the notice.

[0716] Step 2: The user submits a confirmation request.

[0717] Step 3: The server receives the notification and analyzes it using the generative AI model.

[0718] Step 4: The server extracts legal issues and improvement proposals from the analysis results.

[0719] Step 5: The server returns the extracted results to the user.

[0720] Step 6: The user modifies the notification document based on the received results.

[0721] Example 1

[0722] 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."

[0723] Currently, creating and reviewing legal documents requires a great deal of time and effort. It is particularly difficult for users without specialized knowledge to create accurate legal documents. Furthermore, existing document creation systems often have complex user interfaces and raise concerns about data security. To solve these problems, a system that supports the efficient and accurate creation and review of legal documents using generative AI models is needed.

[0724] 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.

[0725] In this invention, the server includes means for collecting legal data, standardizing the format, and converting it into a database, means for training a generative AI model based on the database, means for encrypting and receiving data entered by a user, means for analyzing the user's input data, extracting necessary information, and inputting it into the generative AI model, and means for converting legal documents generated by the generative AI model into PDF format, encrypting the data, and providing the legal documents to the user. This allows users to quickly and accurately create and review legal documents without requiring specialized knowledge, and ensures data security.

[0726] "Legal data" refers to publicly available laws, precedents, regulations, statutes, guidelines, etc.

[0727] A "generative artificial intelligence model" refers to a generative artificial intelligence model that is trained on a large amount of legal data and has the ability to automatically generate legal documents.

[0728] "User" means any person or organization that uses this system to create or review legal documents.

[0729] "Encryption" refers to the technology of converting data being sent and received into a format that cannot be read by third parties, thereby ensuring secure communication.

[0730] "Database" means a structured digital storage system for storing and making readily accessible collected and organized legal data.

[0731] "PDF format" is an abbreviation for Portable Document Format and refers to a standard file format for electronically storing and displaying documents.

[0732] "Input interface" refers to the software window through which a user inputs information and interacts with a system.

[0733] "Analysis" refers to the process of segmenting and classifying received data, extracting specific information, and using it in a useful way.

[0734] "Training" refers to the process of using large amounts of data to train a generative artificial intelligence model and improve its performance.

[0735] The present invention is a system that uses generative AI to support the creation and verification of legal documents, and is implemented as follows.

[0736] Server Roles

[0737] 1. Creating and updating legal databases

[0738] The server periodically collects data on laws, precedents, rules, etc. that are publicly available via the Internet, and generates and updates a database, which is periodically maintained.

[0739] The collected data is converted into a standardized format (e.g., XML or JSON) and used as training data for generative artificial intelligence models.

[0740] 2. Training generative AI models

[0741] The server uses the collected legal data to train a generative AI model (e.g., GPT-3), dividing the data into multiple categories and using them as training and test data.

[0742] After training, the model's performance is evaluated and parameters are adjusted as needed. This training is done using Python libraries such as TensorFlow and PyTorch.

[0743] 3. Processing User Requests

[0744] The server receives requests from users for legal document creation or verification, and the data entered by the user is sent to the server in encrypted form.

[0745] The received data is analyzed on the server side, and the necessary information is extracted and input into the generative artificial intelligence model.

[0746] A generative artificial intelligence model generates legal documents, converts their contents into PDF format, encrypts them, and provides them to users.

[0747] Device Role

[0748] 1. Providing a user interface

[0749] The terminal provides an intuitive input form and interface for users to create and request verification of legal documents.

[0750] It has fields for entering required information (e.g., contractor information, contract terms, etc.).

[0751] The terminal uses HTML and JavaScript to present interactive forms.

[0752] 2. Data transmission and reception

[0753] The terminal encrypts the data entered by the user and sends it securely to the server using the HTTPS protocol.

[0754] The generated document is received from the server, securely decrypted, and displayed to the user using a PDF viewer or similar.

[0755] User Roles

[0756] 1. Request for document creation

[0757] The user enters the necessary information (e.g., contract holder name, contract terms) into the input form on the terminal and presses the send button.

[0758] Check the input and send it to the server.

[0759] 2. Check and correct the document

[0760] The legal document returned from the server is checked, and any necessary corrections are specified via the input interface.

[0761] If there are any corrections, the document is sent to the server again and the corrected document is received.

[0762] Specific examples

[0763] Example 1: Creating an employment contract

[0764] 1. User Action:

[0765] The user selects "Create a new employment contract" on the terminal and enters the employer's name, employee's name, employment conditions, etc.

[0766] Once you have completed the input, click the send button to send the data to the server.

[0767] Example prompt: "To create a new employment contract, please enter the employer name, employee name, and employment terms."

[0768] 2. Server processing:

[0769] The server receives the user's input data, analyzes it, and inputs it into the generative AI model.

[0770] The generative AI model generates an employment contract and returns its contents to the user from the server.

[0771] 3. User Action:

[0772] The user checks the returned employment contract, enters corrections as necessary, and resubmits it.

[0773] Example 2: Checking legal notices

[0774] 1. User Action:

[0775] The user selects "Review Legal Notices" on their device and copies and pastes the notice.

[0776] Send a confirmation request.

[0777] Example prompt: "To view the legal notices, please copy and paste them."

[0778] 2. Server processing:

[0779] The server receives the notification content and analyzes it using the generative AI model.

[0780] Legal issues and improvement proposals are extracted and returned to the user from the server.

[0781] 3. User Action:

[0782] The user corrects the notification document based on the returned results.

[0783] In this way, the system of the present invention utilizes generative AI models to support the rapid and accurate creation and validation of legal documents. Users can efficiently generate and validate legal documents through an intuitive interface.

[0784] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0785] Step 1: Collect and format legal data

[0786] Input: Publicly available data such as laws, precedents, and regulations

[0787] How it works: The server periodically runs a scraping script to collect legal data from the internet, then uses a Python script to parse the raw data and convert it into a standardized format such as XML or JSON.

[0788] Data processing / data calculation: parsing and formatting raw data

[0789] Output: Legal data in a standardized format

[0790] Step 2: Update the database

[0791] Input: Legal data in a standardized format

[0792] Specific operation: The server uses an SQL query to add the converted data to the existing database, checking for duplicate data and ensuring consistency.

[0793] Data processing / data calculation: data insertion and duplication check

[0794] Output: A database containing up-to-date legal data

[0795] Step 3: Training the generative artificial intelligence model

[0796] Input: Legal data stored in a database

[0797] How it works: The server uses Python's Pandas library to load legal data by category and feed it into a generative AI model, which is trained using TensorFlow and PyTorch.

[0798] Data processing / data calculation: data pre-processing and model training

[0799] Output: A trained generative AI model

[0800] Step 4: Receiving user data

[0801] Input: A legal document request entered by a user at a terminal.

[0802] Specific operation: The user enters the required information on the device and presses the send button. The device encrypts the input information using AES and sends it to the server via HTTPS.

[0803] Data processing / data calculation: Encryption and transmission of user data

[0804] Output: Encrypted user data received by the server

[0805] Step 5: Analyze user data

[0806] Input: Encrypted user data received by the server

[0807] What happens: The server decrypts the data and parses it using a natural language processing (NLP) library to extract the necessary information (e.g., contract name, contract terms).

[0808] Data processing / data calculation: data interpretation and information extraction

[0809] Output: Parsed data to feed into a generative AI model

[0810] Step 6: Generate legal documents

[0811] Input: Parsed user data

[0812] Specific operation: The server inputs the analyzed data into the generative AI model and generates the required legal documents.

[0813] Data processing / data calculation: Document generation using generative AI models

[0814] Output: Generated legal document

[0815] Step 7: Encrypt and return the document

[0816] Input: Generated legal document

[0817] What happens: The server converts the generated legal document into PDF format, encrypts it using AES, and then returns the encrypted PDF to the user.

[0818] Data processing / data calculation: document format conversion and encryption

[0819] Output: Encrypted legal document in PDF format

[0820] Step 8: Review the document and request corrections

[0821] Input: Encrypted legal document in PDF format

[0822] Specific operation: The device decrypts the encrypted PDF received from the server and displays it to the user. The user checks the displayed document and, if any corrections are required, enters the corrections again and submits it.

[0823] Data processing / data calculation: Displaying documents and inputting corrections

[0824] Output: Correction request data

[0825] Through these steps, the system can efficiently and safely create and verify legal documents according to user requirements. At each processing step, specific actions and data processing / calculation are performed, resulting in fast and accurate document generation.

[0826] (Application example 1)

[0827] 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."

[0828] In recent years, the creation and review of security-related legal documents and regulations has increased, requiring rapid and accurate responses. However, creating and reviewing these documents requires specialized knowledge, which is a significant time and labor-intensive process. Furthermore, there is a lack of efficient means for correcting and providing feedback on created documents. Therefore, there is a need for a system that allows users to easily create security-related legal documents and regulations and quickly review and modify them.

[0829] 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.

[0830] In this invention, the server includes means for creating legal documents using a generative AI model, means for generating the legal documents created by the generative AI model based on user input data, means for providing the generated legal documents to the user via a smartphone application, means for creating and confirming security policies and regulations related to the legal documents, and means for correcting the confirmed documents by feeding them back to the AI ​​model. This enables users to efficiently create, confirm, and correct security-related legal documents and regulations without requiring specialized knowledge.

[0831] A "generative artificial intelligence model" is an algorithm that learns from large amounts of legal data and generates legal documents based on user input.

[0832] A "legal document" is an official document created based on laws, regulations, and precedents and has the force of law.

[0833] A "smartphone application" is a software program that runs on a smartphone and can be operated interactively by a user.

[0834] A "security policy" is a document that sets out standards and guidelines for ensuring safety when using an information system.

[0835] A "policy" is a document that sets out the rules and procedures relating to a particular activity or service.

[0836] "Feedback" is the process of sending user instructions for corrections and improvements to a legal document once it has been generated back to the generative AI model.

[0837] System Overview

[0838] This system uses generative AI models to help users create, review, and modify legal documents and security policies, and is implemented primarily through server, smartphone, and user operations.

[0839] Server Roles

[0840] Hardware and Software

[0841] Hardware: High-performance servers (e.g. AWS EC2)

[0842] Software: Legal database management, generative AI models (e.g., OpenAI GPT-4)

[0843] Data Processing

[0844] 1. Data collection and formatting:

[0845] The server periodically collects legal, legal precedent, regulatory and security-related data and converts it into a unified format.

[0846] This data is used as training data for the generative AI model.

[0847] 2. Document generation using AI models:

[0848] The server receives input data from the user and extracts the required information.

[0849] The extracted information is fed into a generative AI model to automatically generate legal documents or security policies.

[0850] 3. Feedback and Corrections:

[0851] Based on user feedback, the generated document is revised.

[0852] The revised document is then fed back into the AI ​​model to generate the final document.

[0853] Device Role

[0854] Hardware and Software

[0855] Hardware: Smartphone

[0856] Software: Mobile app development frameworks (e.g., React Native, Flutter)

[0857] User Interface

[0858] 1. User input form:

[0859] The terminal provides users with intuitive input forms to collect information required for legal documents and security policies.

[0860] 2. Data transmission and reception:

[0861] The terminal encrypts the user's input data and sends it securely to the server.

[0862] Receive the generated document from the server and display it to the user.

[0863] User Roles

[0864] 1. Request and review of documentation:

[0865] The user inputs the necessary information into the input form on the terminal and requests the creation of a document.

[0866] The generated document is checked, and corrections are entered as necessary, and fed back to the server.

[0867] Specific examples

[0868] Creating a New Security Policy

[0869] 1. User Action:

[0870] The user selects "Create a new security policy" on the smartphone app and enters the policy name, scope, key requirements, etc.

[0871] Once you have completed the input, click the send button to send the data to the server.

[0872] 2. Server processing:

[0873] The server receives the user's input data, analyzes it, and inputs it into the generative AI model.

[0874] The generative AI model generates a new security policy and sends it back to the user from the server.

[0875] 3. User Action:

[0876] The user checks the returned security policy, enters corrections as necessary, and resubmits it.

[0877] Example prompt sentence:

[0878] Generate a new security policy document based on the following user-specified information:

[0879] Policy Name: System Access Control Policy

[0880] Scope: Company-wide

[0881] Main requirements:

[0882] Strengthened user authentication measures

[0883] Recording and monitoring access logs

[0884] Regular Security Reviews

[0885] The generated document should be returned in the following format:

[0886] This format allows users to significantly reduce the amount of work required in the past and quickly and accurately create, check, and modify security-related legal documents and regulations.

[0887] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0888] Step 1:

[0889] User Input

[0890] The user opens the application on their smartphone and selects "Create a new security policy." The user enters the required information, such as the policy name, scope, and key requirements. This information is entered as text into the input form fields.

[0891] Input: User's text data (policy name, scope, main requirements, etc.)

[0892] Output: Input data stored on the device

[0893] Step 2:

[0894] Data transmission

[0895] The terminal encrypts the data entered by the user and sends it to the server using a secure communication protocol, with the SSL / TLS protocol used between the server and the terminal.

[0896] Input: Input data stored on the device

[0897] Output: Encrypted data sent to the server

[0898] Step 3:

[0899] Data reception and analysis

[0900] The server decrypts the received data and analyzes the user's input data to extract the necessary information. Python libraries (e.g., pandas, numpy) are used for data analysis.

[0901] Input: Encrypted data sent to the server

[0902] Output: Parsed information (policy name, scope, key requirements, etc.)

[0903] Step 4:

[0904] Input to generative AI models

[0905] The server inputs the analyzed information into a generative AI model (e.g., OpenAI GPT-4) as a prompt sentence, which specifically contains the user's input data.

[0906] Input: Parsed information (policy name, scope, key requirements, etc.)

[0907] Output: Prompt sentence to the generative AI model

[0908] Step 5:

[0909] Document Generation

[0910] The generative AI model generates a new security policy document based on the prompt sentence. In this generation process, the AI ​​model creates the document based on the legal and security-related data it has previously learned.

[0911] Input: Prompt sentence for generative AI model

[0912] Output: Generated security policy document

[0913] Step 6:

[0914] Document Transmission

[0915] The server receives the generated security policy document, re-encrypts it, and sends it to the terminal. Again, the SSL / TLS protocol is used between the server and the terminal.

[0916] Input: The security policy document to be generated

[0917] Output: Encrypted document data sent to the device

[0918] Step 7:

[0919] User confirmation and correction

[0920] The terminal decrypts the received document data and displays it to the user. The user checks the document and, if necessary, enters corrections. The corrections are again entered as text in a field on the terminal.

[0921] Input: Encrypted document data

[0922] Output: The security policy document that is displayed to the user

[0923] Step 8:

[0924] Send corrected data

[0925] The device re-encrypts the data, including any corrections entered by the user, and sends it to the server, which receives the data and repeats the process.

[0926] Input: Corrected input data

[0927] Output: Encrypted data sent to the server

[0928] The system's processing steps integrate the entire process and allow users to operate it intuitively. Rapid feedback and correction of generated documents greatly streamlines the generation and review of legal documents and security policies.

[0929] 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.

[0930] This invention combines a system that uses generative AI to assist in the creation and review of legal documents with an emotion engine that recognizes the user's emotions. This system is implemented as follows.

[0931] Server Roles

[0932] 1. Creating and updating legal databases

[0933] The server periodically collects publicly available data on laws, precedents, rules, etc., and generates and updates the database.

[0934] The collected data is converted into a unified format and prepared as training data for the generative artificial intelligence model.

[0935] 2. Training generative AI models

[0936] The server categorizes the legal documents in the database and prepares training and test data.

[0937] The server initializes the generative AI model and trains it using the training data.

[0938] The server uses test data to evaluate the model's performance and adjusts parameters as needed.

[0939] 3. Processing User Requests

[0940] A server receives a request from a user for legal document creation or verification.

[0941] The server analyzes the user's input data and extracts the necessary information.

[0942] The extracted information is fed into a generative AI model to generate legal documents.

[0943] The generated legal document is converted into an appropriate format for presentation to the user.

[0944] Terminal role (client side)

[0945] 1. Providing a user interface

[0946] The terminal displays an input form to the user and provides an interface for accepting requests for legal document creation and confirmation.

[0947] It has fields for entering necessary information (e.g., contractor information, contract terms, etc.).

[0948] 2. Operation of the Emotion Engine

[0949] An emotion engine built into the device analyzes the user's facial expressions and voice in real time to determine their emotional state.

[0950] The emotion engine adjusts the interface and the content of the presented documents appropriately depending on the user's emotional state.

[0951] 3. Data transmission and reception

[0952] The data entered by the user is encrypted and sent to the server using a secure communication protocol (e.g., HTTPS).

[0953] The generated legal document is received from the server and displayed to the user.

[0954] Provide interactive feedback so users can make corrections as needed.

[0955] User Roles

[0956] 1. Request for document creation

[0957] The user accurately enters the required information (contractor information, contract terms, etc.) into the input form on the device.

[0958] The user checks the contents and presses the send button to send the data to the server.

[0959] 2. Check and correct the document

[0960] The user receives the generated legal document returned from the server and checks the contents in detail.

[0961] If necessary, enter corrections and press the send button again to send it to the server.

[0962] The corrected document is received again from the server and a final check is performed.

[0963] Specific examples

[0964] Example 1: Creating an employment contract

[0965] 1. User Action

[0966] The user selects "Create a new employment contract" on the terminal and enters the employer's name, employee's name, employment conditions, etc.

[0967] The emotion engine analyzes the user's facial expressions and voice, and if it detects emotions such as tension or anxiety, it makes the interface gentler and displays messages encouraging relaxation.

[0968] Once you have completed the input, click the send button to send the data to the server.

[0969] 2. Server Processing

[0970] The server receives the user's input data and performs analysis.

[0971] The analysis results are input into a generative AI model to generate an employment contract.

[0972] The generated employment contract is returned to the user.

[0973] 3. User Actions

[0974] The user checks the returned employment contract, enters corrections as necessary, and resubmits it.

[0975] Depending on the situation, the emotion engine will analyze the user's emotions again and provide a support message.

[0976] Example 2: Checking legal notices

[0977] 1. User Action

[0978] The user selects "Review Legal Notices" on their device and copies and pastes the notice.

[0979] If the emotion engine detects stress or anxiety, it will provide reassurance by displaying supportive messages and brief explanations.

[0980] Send a confirmation request.

[0981] 2. Server Processing

[0982] The server receives the notification content and analyzes it using the generative AI model.

[0983] Legal issues and improvement proposals are extracted and returned to the user.

[0984] 3. User Actions

[0985] The user modifies the notification document based on the received results.

[0986] An emotion engine continues to monitor the user's emotional state and provides supportive messages as needed.

[0987] In this way, the system of the present invention utilizes generative artificial intelligence models to not only assist in the rapid and accurate creation and review of legal documents, but also respond to the user's emotional state, thereby improving the user experience and reducing the stress of legal work.

[0988] The processing flow will be explained below.

[0989] Server Processing

[0990] Step 1: Generate and update the legal database

[0991] The server periodically collects publicly available data on laws, precedents, regulations, etc.

[0992] The collected data is converted into a unified format and prepared as training data for the generative artificial intelligence model.

[0993] Update the legal database, add new data and replace old data with the latest.

[0994] Step 2: Training the generative AI model

[0995] The server categorizes the legal documents in the database and prepares training and test data.

[0996] The server initializes the generative AI model and trains it using the training data.

[0997] The server uses test data to evaluate the model's performance and adjusts parameters as needed.

[0998] Step 3: Processing the user request

[0999] A server receives a request from a user for legal document creation or verification.

[1000] The server analyzes the user's input data and extracts the necessary information.

[1001] The extracted information is fed into a generative AI model to generate legal documents.

[1002] The generated legal document is converted into an appropriate format for presentation to the user.

[1003] The generated legal document is transmitted to the user via encrypted communication.

[1004] Terminal processing (client side)

[1005] Step 1: Providing a User Interface

[1006] The terminal displays an input form to the user and provides an interface for accepting requests for legal document creation and confirmation.

[1007] It displays fields for information that the user must enter and provides guide and warning messages to assist with entry.

[1008] Step 2: Emotion Engine in Action

[1009] An emotion engine built into the device analyzes the user's facial expressions and voice in real time to determine their emotional state.

[1010] The emotion engine adjusts the interface and the content of the presented documents appropriately depending on the user's emotional state.

[1011] Step 3: Sending and Receiving Data

[1012] The device encrypts the user's input and sends it to the server using a secure communication protocol (e.g., HTTPS).

[1013] The terminal receives the generated legal document from the server and displays it to the user.

[1014] Provide interactive feedback so users can make corrections as needed.

[1015] User Action

[1016] Step 1: Request for documentation

[1017] The user accurately enters the required information (contractor information, contract terms, etc.) into the input form on the device.

[1018] The user checks the contents and presses the send button to send the data to the server.

[1019] Step 2: Review and correct the document

[1020] The user receives the generated legal document returned from the server and checks the contents in detail.

[1021] If necessary, enter corrections and press the send button again to send it to the server.

[1022] The corrected document is received again from the server and a final check is performed.

[1023] Detailed processing of specific examples

[1024] Example 1: Creating an employment contract

[1025] Step 1: The user selects "Create a new employment contract" on the terminal and enters the employer name, employee name, employment conditions, etc.

[1026] Step 2: The emotion engine analyzes the user's facial expressions and voice, and if it detects emotions such as tension or anxiety, it softens the interface and displays a relaxing message.

[1027] Step 3: The user checks the input and presses the submit button to send it to the server.

[1028] Step 4: The server receives the user's input data and performs analysis.

[1029] Step 5: The server inputs the analysis results into the generation AI model and generates an employment contract.

[1030] Step 6: The server returns the generated employment contract to the user.

[1031] Step 7: The user checks the returned employment contract, enters corrections as necessary, and resubmits it.

[1032] Step 8: The server receives the correction request, regenerates the employment contract, and returns it to the user.

[1033] Step 9: User gives final review and approval.

[1034] Example 2: Checking legal notices

[1035] Step 1: The user selects "Review Legal Notices" on their device and copies and pastes the notice.

[1036] Step 2: If the emotion engine detects stress or anxiety, it displays a supportive message or brief explanation to provide reassurance.

[1037] Step 3: The user submits a confirmation request.

[1038] Step 4: The server receives the notification and analyzes it using the generative AI model.

[1039] Step 5: The server extracts legal issues and improvement proposals from the analysis results.

[1040] Step 6: The server returns the extracted results to the user.

[1041] Step 7: The user modifies the notification document based on the returned results.

[1042] Step 8: The emotion engine continues to monitor the user's emotional state and provides support messages as needed.

[1043] Step 9: The user performs a final check and completes the correction.

[1044] Example 2

[1045] 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."

[1046] While conventional legal document creation systems have achieved a certain level of accuracy and speed in the creation of documents, they lack the functionality to consider the user's emotional state, which hinders the user experience.Furthermore, there are still concerns about privacy and data confidentiality due to the lack of secure means to send and receive user-entered data and generated documents.

[1047] 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.

[1048] In this invention, the server includes a means for creating legal documents using a generative artificial intelligence model, a means for using an emotion engine that analyzes the user's emotional state and appropriately adjusts the interface and document content, and a means for providing the generated legal documents to the user. This makes it possible to provide an interface that takes the user's emotional state into consideration while maintaining the accuracy and speed of the legal documents. Furthermore, by adding a means for securely sending and receiving data entered by the user and the generated documents, concerns about privacy and confidentiality can be alleviated.

[1049] "Generative artificial intelligence model" refers to a machine learning algorithm for automatically generating legal documents.

[1050] "Legal Document" means a document containing legal content, such as a contract, legal notice, or legal precedent.

[1051] "User-input data" refers to data entered by a user to provide information necessary for generating or requesting validation of a legal document.

[1052] An "emotion engine" refers to technology that analyzes a user's facial expressions and voice to determine the user's emotional state in real time.

[1053] "Interface" refers to the input and output means by which a user interacts with a system.

[1054] "Data transmission and reception" refers to the means of communication for securely exchanging data between the user and the server.

[1055] "Encryption" refers to the technology of converting data into a format that cannot be deciphered by third parties in order to transmit the data securely.

[1056] "Training data" refers to the data set used to train a generative AI model.

[1057] "Test data" refers to a dataset used to evaluate the performance of a generative AI model after training.

[1058] "Analysis" refers to the process of analyzing input data and generated documents to extract necessary information.

[1059] The present invention is a system that uses a generative AI model to assist in the creation and review of legal documents, and also combines it with an emotion engine that analyzes user emotions. Specific embodiments of this system are described below.

[1060] Server Roles

[1061] Hardware and Software Use

[1062] The server should be equipped with a high-performance processor and large memory capacity. Specifically, a server equipped with a GPU (e.g., a machine equipped with an NVIDIA GPU) is preferable. For software, a machine learning framework such as TensorFlow or PyTorch is used to build, train, and run the generative AI model.

[1063] Creating and updating legal databases

[1064] The server regularly collects publicly available data on laws, precedents, rules, etc., and generates and updates the database. The collected data is converted into a unified format and used as training data for the generative AI model. This ensures that the latest legal information is always available.

[1065] Training generative AI models

[1066] The server categorizes legal documents in the database, prepares training data and test data, initializes a generative AI model (e.g., GPT-3, BERT), and trains the model using the training data. Performance is evaluated and parameters are adjusted as needed to enable highly accurate legal document generation.

[1067] Processing user requests

[1068] The server receives a request from a user to create or review a legal document, analyzes the input data, extracts the necessary information, inputs the extracted information into a generative AI model to generate the legal document, and then converts the generated legal document into an appropriate format (e.g., PDF or Word document) and provides it to the user.

[1069] Terminal role (client side)

[1070] Hardware and Software Use

[1071] The devices used include PCs, tablets, and smartphones. The devices require a camera and microphone to support the operation of the emotion engine. The software used is an emotion analysis engine (e.g., OpenFace or Microsoft Azure's Face API).

[1072] Providing a user interface

[1073] The terminal displays an input form to the user, providing an interface for accepting requests for legal document creation and confirmation, along with fields for entering required information (e.g., contract information, contract terms, etc.).

[1074] Emotion Engine Operation

[1075] An emotion engine built into the device analyzes the user's facial expressions and voice in real time to determine their emotional state. The emotion engine then adjusts the interface and the content of the documents provided appropriately based on the user's emotional state. For example, if the user is nervous, it displays a message encouraging them to relax.

[1076] Sending and receiving data

[1077] Encrypts the data entered by the user and sends it to the server using a secure communication protocol (e.g., HTTPS), receives the generated legal document from the server and displays it to the user, and provides interactive feedback to the user to allow them to make corrections, if necessary.

[1078] User Roles

[1079] Document creation request

[1080] The user accurately enters the necessary information into the input form on the terminal and presses the send button to send the data to the server.

[1081] Document review and correction

[1082] The user receives the generated legal document sent from the server, checks its contents in detail, enters corrections as necessary, and presses the send button again to send it to the server. The corrected document is then received again from the server and a final check is made.

[1083] Specific examples

[1084] Example 1: Creating an employment contract

[1085] 1. User Action

[1086] The user selects "Create a new employment contract" on the terminal and enters employer information, employee information, and employment conditions.

[1087] The emotion engine detects tension from the user's facial expressions and voice and displays the message, "Please relax and continue typing."

[1088] 2. Server Processing

[1089] The server receives and analyzes the user's input data and inputs it into the generative AI model.

[1090] Generate an employment contract, convert it to PDF and return it to the device.

[1091] 3. User Actions

[1092] The user reviews the returned contract, enters any necessary corrections, and resubmits it. The emotion engine continues to monitor emotions and displays support messages.

[1093] Example 2: Checking legal notices

[1094] 1. User Action

[1095] The user selects "Review Legal Notice" on the device and enters the notice.

[1096] The emotion engine detects stress and displays, "Don't worry. An expert will check."

[1097] 2. Server Processing

[1098] The server receives the notification content, analyzes it, and inputs it into the generative AI model.

[1099] Legal issues and improvement proposals are extracted and returned to the user.

[1100] 3. User Actions

[1101] The user modifies the notification based on the returned results. The emotion engine continues to monitor emotions and displays support messages as needed.

[1102] Prompt Sentence Examples

[1103] 1. Please create a new employment contract. The employer information is 'Company A', the employee information is 'Mr. B', and the employment conditions are 'Full-time, monthly salary 300,000 yen'.

[1104] 2. What is the problem with this legal notice? The notice reads: 'This is a notice from Company X to Company Y.'

[1105] From the above, this system effectively combines generative AI models and an emotion engine to generate and verify legal documents quickly and accurately, while providing support tailored to the user's emotional state, thereby reducing the stress of legal work and improving the user experience.

[1106] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1107] Step 1:

[1108] Data collection and database generation and updating

[1109] The server collects public legal data (e.g., legal data, precedents, and rules). Inputs include data from internet legal databases and court websites. It stores this data in local storage.

[1110] The server converts the collected data into a unified format, for example, converting data in different formats into XML or JSON.

[1111] The server updates the existing database with new or updated data, checks for duplicates and inconsistencies, and cleans the data.

[1112] The output is a cleaned legal document database in a unified format.

[1113] Step 2:

[1114] Training generative AI models

[1115] The server splits the legal document data in the database into training data and test data. The input is the unified database obtained in the previous step.

[1116] The server initializes a generative AI model (e.g., GPT-3, BERT) and trains it with training data. Specifically, it uses TensorFlow or PyTorch to train the model while feeding it data.

[1117] The server evaluates the trained model on the test data and adjusts the parameters as needed.

[1118] The output is a trained generative AI model capable of generating legal documents with high accuracy.

[1119] Step 3:

[1120] Processing user requests

[1121] The server receives requests from users to create or verify legal documents, and the input includes user input data sent from the terminal via HTTPS.

[1122] The server analyzes the received data and extracts necessary information (e.g., contract information, legal terms). Data analysis uses natural language processing technology. Specifically, it uses a text analysis algorithm.

[1123] The server inputs the extracted information into a generative AI model to generate the necessary legal documents.

[1124] The server converts the generated legal document into an appropriate format (e.g., PDF, Word document) and provides it to the user.

[1125] As an output, a legal document generated based on the user's request is sent to the terminal.

[1126] Step 4:

[1127] Emotion Engine Operation

[1128] The emotion engine built into the device analyzes the user's facial expressions and voice in real time. The inputs include facial expression data and voice data obtained through the camera and microphone.

[1129] The device determines the user's emotional state based on the analysis results of the emotion engine, for example, using facial recognition technology or voice tone analysis.

[1130] The device will adjust the interface and messages provided appropriately depending on the user's emotional state.

[1131] The output is a tailored interface or message that reflects the user's emotional state.

[1132] Step 5:

[1133] User data transmission and feedback

[1134] The user enters the necessary information into the input fields on the terminal and presses the send button. Input includes contract information, legal terms, etc.

[1135] The terminal encrypts the user's input data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[1136] The server transmits the generated legal document to the terminal, where it is received by the user.

[1137] The user checks the generated document and, if necessary, inputs corrections through the terminal interface, which then resends it.

[1138] The device provides interactive feedback, allowing users to make corrections, and the emotion engine continuously monitors the user's emotional state and displays appropriate support messages.

[1139] The output is a final legal document that the user has reviewed and corrected.

[1140] (Application example 2)

[1141] 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."

[1142] Conventional legal document drafting systems do not take into account the user's emotional state, which means they are unable to provide efficient support, especially to users who feel nervous or anxious. Furthermore, the lack of emotion analysis prevents an improved user experience and makes it difficult to reduce stress and anxiety.

[1143] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for creating a legal document using a generative AI model; means for generating the legal document created by the generative AI model based on user input data; means for providing the generated legal document to the user; means for combining an emotion engine that analyzes the user's emotional state; and means for adjusting the interface and the content of the provided document based on the emotion data generated by the emotion engine. This makes it possible to support the creation and review of legal documents individually tailored according to the user's emotional state, thereby improving the user experience and reducing stress and anxiety.

[1144] A "generative artificial intelligence model" is a model that uses machine learning algorithms to create legal documents.

[1145] "Legal documents" refers to legal documents such as contracts, regulations, notices, etc.

[1146] "User input data" refers to information and conditions provided by a user for document creation.

[1147] The "emotion engine" is a system that analyzes the user's facial expressions and voice to determine their emotional state.

[1148] "Emotional Data" refers to information about a user's emotional state generated by an emotion engine.

[1149] "Interface" refers to the screen and operating means that allow a user to interact with a system.

[1150] This invention is a system that combines a generative AI model and an emotion engine. The system is mainly composed of a server and a terminal, and provides emotional support to users when creating and reviewing legal documents. Specific embodiments for implementing this invention are described below.

[1151] Server Roles

[1152] The server performs the following processing.

[1153] 1. Collection and organization of legal data:

[1154] The server periodically collects data on laws, precedents, regulations, etc., creates and updates a database, converts this data into a unified format, and prepares it as training data for generative AI models.

[1155] 2. Training the generative AI model:

[1156] The server categorizes legal document data and uses it to train a generative AI model. After training, the model's performance is evaluated and parameters are adjusted as needed.

[1157] 3. Processing User Requests:

[1158] The server receives a request from a user to create or verify a legal document, analyzes the user's input data, inputs the analyzed data into a generative AI model to generate the required legal document, and then converts the generated legal document into an appropriate format for delivery to the user.

[1159] Terminal role (client side)

[1160] The terminal performs the following processing:

[1161] 1. Providing the user interface:

[1162] The terminal displays an input form to the user, providing an interface for accepting requests for legal document creation and confirmation, and includes fields for entering required information (contractor information, contract terms, etc.).

[1163] 2. How the Emotion Engine works:

[1164] An emotion engine built into the device analyzes the user's facial expressions and voice in real time to determine their emotional state, and then adjusts the interface and the content of the documents presented appropriately based on the user's emotional state.

[1165] 3. Sending and receiving data:

[1166] The device encrypts the data entered by the user and sends it to the server using a secure communication protocol (e.g., HTTPS), and receives the generated legal document from the server and displays it to the user, providing interactive feedback so that the user can make corrections if necessary.

[1167] User Roles

[1168] 1. Document Request:

[1169] The user accurately enters the required information into the input form on the device, and when the input is complete, presses the send button to send the data to the server.

[1170] 2. Document review and correction:

[1171] The user receives the generated legal document returned from the server, checks its contents in detail, enters corrections as necessary, and presses the send button again to send it to the server. The user then receives the corrected document again and makes a final check.

[1172] Specific examples

[1173] Example 1: Creating an employment contract

[1174] The user selects "Create a new employment contract" and enters the employer's name, employee's name, employment conditions, etc. The emotion engine analyzes the user's facial expressions and voice, and if it detects emotions such as tension or anxiety, it makes the interface gentler and displays a message encouraging relaxation. Once the input is complete, the user presses the send button to send the data to the server.

[1175] The server receives the user's input data, analyzes it, inputs the analysis results into the generative AI model to generate an employment contract, and then returns the generated employment contract to the user.

[1176] The user checks the returned employment contract, enters any necessary corrections, and resubmits it. Depending on the situation, the emotion engine will again analyze the user's emotions and provide a support message.

[1177] Example 2: Checking legal notices

[1178] The user selects "Confirm Legal Notice" and enters the notification content. If the emotion engine senses stress or anxiety, it provides a sense of security by displaying a support message or a simple explanation. When a confirmation request is sent, the server receives the notification content and analyzes it using a generative AI model. Legal issues and improvement proposals are extracted and sent back to the user.

[1179] The user can modify the notification based on the received results, and the emotion engine continues to monitor the user's emotional state and provide support messages as needed.

[1180] Prompt Sentence Examples

[1181] "Please create a new employment contract. Please use the following information:

[1182] Employer Name: Tanaka

[1183] Employee name: Suzuki

[1184] Employment conditions: Full-time, annual salary of 6 million yen, two days off per week

[1185] Using this prompt, the generative AI model can quickly and accurately generate the required legal documents.

[1186] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1187] Step 1:

[1188] The terminal displays an input form to the user and accepts requests for legal document creation and confirmation. The user enters the necessary information, such as contract holder information and contract terms. The terminal encrypts the entered data and sends it to the server using a secure communication protocol (e.g., HTTPS). Contract holder information and contract terms are provided as input data, and subsequent document generation is based on this.

[1189] Step 2:

[1190] The server receives and analyzes input data sent by the user. The input data includes the name of the contracting party, contract terms, etc., and the server analyzes this data to extract the necessary information. The analysis results are input into a generative AI model to generate the legal document desired by the user. For example, in response to a request to "create a new employment contract," a corresponding employment contract is generated.

[1191] Step 3:

[1192] The server converts the generated legal document into an appropriate format and returns it to the terminal, including checking and formatting the content of the generated document. The output legal document is provided in a format that is easy for the user to review.

[1193] Step 4:

[1194] The terminal receives the legal document returned from the server and displays it to the user. The user checks the displayed document and makes any necessary corrections. The terminal then sends the information added or corrected by the user back to the server. The user's corrected data is re-entered through the interface, thereby improving the accuracy of the document.

[1195] Step 5:

[1196] An emotion engine built into the device analyzes the user's facial expressions and voice to determine their emotional state in real time. If the emotion engine detects the user's emotional state (e.g., tension or anxiety), the device adjusts the interface and display content. For example, it may display a relaxing message. This allows the user to use the system with peace of mind.

[1197] Step 6:

[1198] The final legal document that the user has reviewed and corrected is then sent from the device to the server, where it is reviewed and formatted again.The final output is a legal document that is accurate and takes into account the user's emotional state and is provided to the user.

[1199] These steps result in a system where a comprehensive legal document creation and review process is achieved by combining generative AI models and an emotion engine.

[1200] 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.

[1201] 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.

[1202] 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.

[1203] [Third embodiment]

[1204] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1205] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1206] 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).

[1207] 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.

[1208] 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.

[1209] 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).

[1210] 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.

[1211] 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.

[1212] 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.

[1213] 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.

[1214] 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.

[1215] 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."

[1216] The present invention is a system that uses generative AI to support the creation and verification of legal documents, and is implemented as follows.

[1217] Server Roles

[1218] 1. Creating and updating legal databases

[1219] The server periodically collects data such as published laws, precedents, and regulations, and generates and updates the database.

[1220] The collected data is converted into a unified format and used as training data for generative artificial intelligence models.

[1221] 2. Training generative AI models

[1222] The server uses the collected legal data to train a generative artificial intelligence model.

[1223] During training, data is divided into categories and used as training data and test data.

[1224] Evaluate the model's performance and adjust parameters as needed.

[1225] 3. Processing User Requests

[1226] Receive requests from users for legal document creation or verification.

[1227] Based on the request content, the data provided by the user is analyzed and the necessary information is extracted.

[1228] The extracted information is fed into a generative AI model to generate legal documents.

[1229] The generated document is provided to the user.

[1230] Device Role

[1231] 1. Providing a user interface

[1232] The terminal provides an intuitive input form and interface for users to create and request verification of legal documents.

[1233] It has fields for entering required information (e.g., contractor information, contract terms, etc.).

[1234] 2. Data transmission and reception

[1235] The data entered by the user is encrypted and sent securely to the server.

[1236] The generated document is received from the server and displayed to the user.

[1237] User Roles

[1238] 1. Request for document creation

[1239] The user enters the necessary information (e.g., the name of the contract holder, contract terms) into the input form on the terminal and presses the send button.

[1240] Check the input and send it to the server.

[1241] 2. Check and correct the document

[1242] The legal document returned from the server is checked, and any necessary corrections are specified via the input interface.

[1243] If there are any corrections, the document is sent to the server again and the corrected document is received.

[1244] Specific examples

[1245] Example 1: Creating an employment contract

[1246] 1. User Action:

[1247] The user selects "Create a new employment contract" on the terminal and enters the employer's name, employee's name, employment conditions, etc.

[1248] Once you have completed the input, click the send button to send the data to the server.

[1249] 2. Server processing:

[1250] The server receives the user's input data, analyzes it, and inputs it into the generative AI model.

[1251] The generative AI model generates an employment contract and sends its contents back to the user from the server.

[1252] 3. User Action:

[1253] The user checks the returned employment contract, enters corrections as necessary, and resubmits it.

[1254] Example 2: Checking legal notices

[1255] 1. User Action:

[1256] The user selects "Review Legal Notices" on their device and copies and pastes the notice.

[1257] Send a confirmation request.

[1258] 2. Server processing:

[1259] The server receives the notification content and analyzes it using the generative AI model.

[1260] Legal issues and improvement proposals are extracted and returned to the user from the server.

[1261] 3. User Action:

[1262] The user corrects the notification document based on the returned results.

[1263] In this way, the system of the present invention utilizes generative artificial intelligence models to support the rapid and accurate creation and validation of legal documents. Users can efficiently create and validate legal documents through an intuitive interface.

[1264] The processing flow will be explained below.

[1265] Server Processing

[1266] Step 1: Generate and update the legal database

[1267] The server periodically collects publicly available data on laws, precedents, regulations, etc.

[1268] The collected data is converted into a unified format and prepared as training data for the generative artificial intelligence model.

[1269] Update the legal database, add new data and replace old data with the latest.

[1270] Step 2: Training the generative AI model

[1271] The server categorizes the legal documents in the database and prepares training and test data.

[1272] The server initializes the generative AI model and trains it using the training data.

[1273] The server uses test data to evaluate the model's performance and adjusts parameters as needed.

[1274] Step 3: Processing the user request

[1275] A server receives a request from a user for legal document creation or verification.

[1276] The server analyzes the user's input data and extracts the necessary information.

[1277] The extracted information is fed into a generative AI model to generate legal documents.

[1278] The generated legal document is converted into an appropriate format for presentation to the user.

[1279] The generated legal document is transmitted to the user via encrypted communication.

[1280] Terminal processing (client side)

[1281] Step 1: Providing a User Interface

[1282] The terminal displays an input form to the user and provides an interface for accepting requests for legal document creation and confirmation.

[1283] It displays fields for information that the user must enter and provides guide and warning messages to assist with entry.

[1284] Step 2: Sending and Receiving Data

[1285] The device encrypts the user's input and sends it to the server using a secure communication protocol (e.g., HTTPS).

[1286] The terminal receives the generated legal document from the server and displays it to the user.

[1287] Provide interactive feedback so users can make corrections as needed.

[1288] User Action

[1289] Step 1: Request for documentation

[1290] The user accurately enters the required information (contractor information, contract terms, etc.) into the input form on the device.

[1291] The user checks the contents and presses the send button to send the data to the server.

[1292] Step 2: Review and correct the document

[1293] The user receives the generated legal document returned from the server and checks the contents in detail.

[1294] If necessary, enter corrections and press the send button again to send it to the server.

[1295] The corrected document is received again from the server and a final check is performed.

[1296] Detailed processing of specific examples

[1297] Example 1: Creating an employment contract

[1298] Step 1: The user selects "Create a new employment contract" on the terminal and enters the employer name, employee name, employment conditions, etc.

[1299] Step 2: Check the information entered by the user and press the submit button to send it to the server.

[1300] Step 3: The server receives the user's input data and performs analysis.

[1301] Step 4: The server inputs the analysis results into the generation AI model and generates an employment contract.

[1302] Step 5: The server returns the generated employment contract to the user.

[1303] Step 6: The user checks the returned employment contract, enters any necessary corrections, and resubmits it.

[1304] Step 7: The server receives the correction request, regenerates the employment contract, and returns it to the user.

[1305] Example 2: Checking legal notices

[1306] Step 1: The user selects "Review Legal Notices" on their device and copies and pastes the notice.

[1307] Step 2: The user submits a confirmation request.

[1308] Step 3: The server receives the notification and analyzes it using the generative AI model.

[1309] Step 4: The server extracts legal issues and improvement proposals from the analysis results.

[1310] Step 5: The server returns the extracted results to the user.

[1311] Step 6: The user modifies the notification document based on the received results.

[1312] Example 1

[1313] 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."

[1314] Currently, creating and reviewing legal documents requires a great deal of time and effort. It is particularly difficult for users without specialized knowledge to create accurate legal documents. Furthermore, existing document creation systems often have complex user interfaces and raise concerns about data security. To solve these problems, a system that supports the efficient and accurate creation and review of legal documents using generative AI models is needed.

[1315] 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.

[1316] In this invention, the server includes means for collecting legal data, standardizing the format, and converting it into a database, means for training a generative AI model based on the database, means for encrypting and receiving data entered by a user, means for analyzing the user's input data, extracting necessary information, and inputting it into the generative AI model, and means for converting legal documents generated by the generative AI model into PDF format, encrypting the data, and providing the legal documents to the user. This allows users to quickly and accurately create and review legal documents without requiring specialized knowledge, and ensures data security.

[1317] "Legal data" refers to publicly available laws, precedents, regulations, statutes, guidelines, etc.

[1318] A "generative artificial intelligence model" refers to a generative artificial intelligence model that is trained on a large amount of legal data and has the ability to automatically generate legal documents.

[1319] "User" means any person or organization that uses this system to create or review legal documents.

[1320] "Encryption" refers to the technology of converting data being sent and received into a format that cannot be read by third parties, thereby ensuring secure communication.

[1321] "Database" means a structured digital storage system for storing and making readily accessible collected and organized legal data.

[1322] "PDF format" is an abbreviation for Portable Document Format and refers to a standard file format for electronically storing and displaying documents.

[1323] "Input interface" refers to the software window through which a user inputs information and interacts with a system.

[1324] "Analysis" refers to the process of segmenting and classifying received data, extracting specific information, and using it in a useful way.

[1325] "Training" refers to the process of using large amounts of data to train a generative artificial intelligence model and improve its performance.

[1326] The present invention is a system that uses generative AI to support the creation and verification of legal documents, and is implemented as follows.

[1327] Server Roles

[1328] 1. Creating and updating legal databases

[1329] The server periodically collects data on laws, precedents, rules, etc. that are publicly available via the Internet, and generates and updates a database, which is periodically maintained.

[1330] The collected data is converted into a standardized format (e.g., XML or JSON) and used as training data for generative artificial intelligence models.

[1331] 2. Training generative AI models

[1332] The server uses the collected legal data to train a generative AI model (e.g., GPT-3), dividing the data into multiple categories and using them as training and test data.

[1333] After training, the model's performance is evaluated and parameters are adjusted as needed. This training is done using Python libraries such as TensorFlow and PyTorch.

[1334] 3. Processing User Requests

[1335] The server receives requests from users for legal document creation or verification, and the data entered by the user is sent to the server in encrypted form.

[1336] The received data is analyzed on the server side, and the necessary information is extracted and input into the generative artificial intelligence model.

[1337] A generative artificial intelligence model generates legal documents, converts their contents into PDF format, encrypts them, and provides them to users.

[1338] Device Role

[1339] 1. Providing a user interface

[1340] The terminal provides an intuitive input form and interface for users to create and request verification of legal documents.

[1341] It has fields for entering required information (e.g., contractor information, contract terms, etc.).

[1342] The terminal uses HTML and JavaScript to present interactive forms.

[1343] 2. Data transmission and reception

[1344] The terminal encrypts the data entered by the user and sends it securely to the server using the HTTPS protocol.

[1345] The generated document is received from the server, securely decrypted, and displayed to the user using a PDF viewer or similar.

[1346] User Roles

[1347] 1. Request for document creation

[1348] The user enters the necessary information (e.g., contract holder name, contract terms) into the input form on the terminal and presses the send button.

[1349] Check the input and send it to the server.

[1350] 2. Check and correct the document

[1351] The legal document returned from the server is checked, and any necessary corrections are specified via the input interface.

[1352] If there are any corrections, the document is sent to the server again and the corrected document is received.

[1353] Specific examples

[1354] Example 1: Creating an employment contract

[1355] 1. User Action:

[1356] The user selects "Create a new employment contract" on the terminal and enters the employer's name, employee's name, employment conditions, etc.

[1357] Once you have completed the input, click the send button to send the data to the server.

[1358] Example prompt: "To create a new employment contract, please enter the employer name, employee name, and employment terms."

[1359] 2. Server processing:

[1360] The server receives the user's input data, analyzes it, and inputs it into the generative AI model.

[1361] The generative AI model generates an employment contract and returns its contents to the user from the server.

[1362] 3. User Action:

[1363] The user checks the returned employment contract, enters corrections as necessary, and resubmits it.

[1364] Example 2: Checking legal notices

[1365] 1. User Action:

[1366] The user selects "Review Legal Notices" on their device and copies and pastes the notice.

[1367] Send a confirmation request.

[1368] Example prompt: "To view the legal notices, please copy and paste them."

[1369] 2. Server processing:

[1370] The server receives the notification content and analyzes it using the generative AI model.

[1371] Legal issues and improvement proposals are extracted and returned to the user from the server.

[1372] 3. User Action:

[1373] The user corrects the notification document based on the returned results.

[1374] In this way, the system of the present invention utilizes generative AI models to support the rapid and accurate creation and validation of legal documents. Users can efficiently generate and validate legal documents through an intuitive interface.

[1375] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1376] Step 1: Collect and format legal data

[1377] Input: Publicly available data such as laws, precedents, and regulations

[1378] How it works: The server periodically runs a scraping script to collect legal data from the internet, then uses a Python script to parse the raw data and convert it into a standardized format such as XML or JSON.

[1379] Data processing / data calculation: parsing and formatting raw data

[1380] Output: Legal data in a standardized format

[1381] Step 2: Update the database

[1382] Input: Legal data in a standardized format

[1383] Specific operation: The server uses an SQL query to add the converted data to the existing database, checking for duplicate data and ensuring consistency.

[1384] Data processing / data calculation: data insertion and duplication check

[1385] Output: A database containing up-to-date legal data

[1386] Step 3: Training the generative artificial intelligence model

[1387] Input: Legal data stored in a database

[1388] How it works: The server uses Python's Pandas library to load legal data by category and feed it into a generative AI model, which is trained using TensorFlow and PyTorch.

[1389] Data processing / data calculation: data pre-processing and model training

[1390] Output: A trained generative AI model

[1391] Step 4: Receiving user data

[1392] Input: A legal document request entered by a user at a terminal.

[1393] Specific operation: The user enters the required information on the device and presses the send button. The device encrypts the input information using AES and sends it to the server via HTTPS.

[1394] Data processing / data calculation: Encryption and transmission of user data

[1395] Output: Encrypted user data received by the server

[1396] Step 5: Analyze user data

[1397] Input: Encrypted user data received by the server

[1398] What happens: The server decrypts the data and parses it using a natural language processing (NLP) library to extract the necessary information (e.g., contract name, contract terms).

[1399] Data processing / data calculation: data interpretation and information extraction

[1400] Output: Parsed data to feed into a generative AI model

[1401] Step 6: Generate legal documents

[1402] Input: Parsed user data

[1403] Specific operation: The server inputs the analyzed data into the generative AI model and generates the required legal documents.

[1404] Data processing / data calculation: Document generation using generative AI models

[1405] Output: Generated legal document

[1406] Step 7: Encrypt and return the document

[1407] Input: Generated legal document

[1408] What happens: The server converts the generated legal document into PDF format, encrypts it using AES, and then returns the encrypted PDF to the user.

[1409] Data processing / data calculation: document format conversion and encryption

[1410] Output: Encrypted legal document in PDF format

[1411] Step 8: Review the document and request corrections

[1412] Input: Encrypted legal document in PDF format

[1413] Specific operation: The device decrypts the encrypted PDF received from the server and displays it to the user. The user checks the displayed document and, if any corrections are required, enters the corrections again and submits it.

[1414] Data processing / data calculation: Displaying documents and inputting corrections

[1415] Output: Correction request data

[1416] Through these steps, the system can efficiently and safely create and verify legal documents according to user requirements. At each processing step, specific actions and data processing / calculation are performed, resulting in fast and accurate document generation.

[1417] (Application example 1)

[1418] 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."

[1419] In recent years, the creation and review of security-related legal documents and regulations has increased, requiring rapid and accurate responses. However, creating and reviewing these documents requires specialized knowledge, which is a significant time and labor-intensive process. Furthermore, there is a lack of efficient means for correcting and providing feedback on created documents. Therefore, there is a need for a system that allows users to easily create security-related legal documents and regulations and quickly review and modify them.

[1420] 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.

[1421] In this invention, the server includes means for creating legal documents using a generative AI model, means for generating the legal documents created by the generative AI model based on user input data, means for providing the generated legal documents to the user via a smartphone application, means for creating and confirming security policies and regulations related to the legal documents, and means for correcting the confirmed documents by feeding them back to the AI ​​model. This enables users to efficiently create, confirm, and correct security-related legal documents and regulations without requiring specialized knowledge.

[1422] A "generative artificial intelligence model" is an algorithm that learns from large amounts of legal data and generates legal documents based on user input.

[1423] A "legal document" is an official document created based on laws, regulations, and precedents and has the force of law.

[1424] A "smartphone application" is a software program that runs on a smartphone and can be operated interactively by a user.

[1425] A "security policy" is a document that sets out standards and guidelines for ensuring safety when using an information system.

[1426] A "policy" is a document that sets out the rules and procedures relating to a particular activity or service.

[1427] "Feedback" is the process of sending user instructions for corrections and improvements to a legal document once it has been generated back to the generative AI model.

[1428] System Overview

[1429] This system uses generative AI models to help users create, review, and modify legal documents and security policies, and is implemented primarily through server, smartphone, and user operations.

[1430] Server Roles

[1431] Hardware and Software

[1432] Hardware: High-performance servers (e.g. AWS EC2)

[1433] Software: Legal database management, generative AI models (e.g., OpenAI GPT-4)

[1434] Data Processing

[1435] 1. Data collection and formatting:

[1436] The server periodically collects legal, legal precedent, regulatory and security-related data and converts it into a unified format.

[1437] This data is used as training data for the generative AI model.

[1438] 2. Document generation using AI models:

[1439] The server receives input data from the user and extracts the required information.

[1440] The extracted information is fed into a generative AI model to automatically generate legal documents or security policies.

[1441] 3. Feedback and Corrections:

[1442] Based on user feedback, the generated document is revised.

[1443] The revised document is then fed back into the AI ​​model to generate the final document.

[1444] Device Role

[1445] Hardware and Software

[1446] Hardware: Smartphone

[1447] Software: Mobile app development frameworks (e.g., React Native, Flutter)

[1448] User Interface

[1449] 1. User input form:

[1450] The terminal provides users with intuitive input forms to collect information required for legal documents and security policies.

[1451] 2. Data transmission and reception:

[1452] The terminal encrypts the user's input data and sends it securely to the server.

[1453] Receive the generated document from the server and display it to the user.

[1454] User Roles

[1455] 1. Request and review of documentation:

[1456] The user inputs the necessary information into the input form on the terminal and requests the creation of a document.

[1457] The generated document is checked, and corrections are entered as necessary, and fed back to the server.

[1458] Specific examples

[1459] Creating a New Security Policy

[1460] 1. User Action:

[1461] The user selects "Create a new security policy" on the smartphone app and enters the policy name, scope, key requirements, etc.

[1462] Once you have completed the input, click the send button to send the data to the server.

[1463] 2. Server processing:

[1464] The server receives the user's input data, analyzes it, and inputs it into the generative AI model.

[1465] The generative AI model generates a new security policy and sends it back to the user from the server.

[1466] 3. User Action:

[1467] The user checks the returned security policy, enters corrections as necessary, and resubmits it.

[1468] Example prompt sentence:

[1469] Generate a new security policy document based on the following user-specified information:

[1470] Policy Name: System Access Control Policy

[1471] Scope: Company-wide

[1472] Main requirements:

[1473] Strengthened user authentication measures

[1474] Recording and monitoring access logs

[1475] Regular Security Reviews

[1476] The generated document should be returned in the following format:

[1477] This format allows users to significantly reduce the amount of work required in the past and quickly and accurately create, check, and modify security-related legal documents and regulations.

[1478] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1479] Step 1:

[1480] User Input

[1481] The user opens the application on their smartphone and selects "Create a new security policy." The user enters the required information, such as the policy name, scope, and key requirements. This information is entered as text into the input form fields.

[1482] Input: User's text data (policy name, scope, main requirements, etc.)

[1483] Output: Input data stored on the device

[1484] Step 2:

[1485] Data transmission

[1486] The terminal encrypts the data entered by the user and sends it to the server using a secure communication protocol, with the SSL / TLS protocol used between the server and the terminal.

[1487] Input: Input data stored on the device

[1488] Output: Encrypted data sent to the server

[1489] Step 3:

[1490] Data reception and analysis

[1491] The server decrypts the received data and analyzes the user's input data to extract the necessary information. Python libraries (e.g., pandas, numpy) are used for data analysis.

[1492] Input: Encrypted data sent to the server

[1493] Output: Parsed information (policy name, scope, key requirements, etc.)

[1494] Step 4:

[1495] Input to generative AI models

[1496] The server inputs the analyzed information into a generative AI model (e.g., OpenAI GPT-4) as a prompt sentence, which specifically contains the user's input data.

[1497] Input: Parsed information (policy name, scope, key requirements, etc.)

[1498] Output: Prompt sentence to the generative AI model

[1499] Step 5:

[1500] Document Generation

[1501] The generative AI model generates a new security policy document based on the prompt sentence. In this generation process, the AI ​​model creates the document based on the legal and security-related data it has previously learned.

[1502] Input: Prompt sentence for generative AI model

[1503] Output: Generated security policy document

[1504] Step 6:

[1505] Document Transmission

[1506] The server receives the generated security policy document, re-encrypts it, and sends it to the terminal. Again, the SSL / TLS protocol is used between the server and the terminal.

[1507] Input: The security policy document to be generated

[1508] Output: Encrypted document data sent to the device

[1509] Step 7:

[1510] User confirmation and correction

[1511] The terminal decrypts the received document data and displays it to the user. The user checks the document and, if necessary, enters corrections. The corrections are again entered as text in a field on the terminal.

[1512] Input: Encrypted document data

[1513] Output: The security policy document that is displayed to the user

[1514] Step 8:

[1515] Send corrected data

[1516] The device re-encrypts the data, including any corrections entered by the user, and sends it to the server, which receives the data and repeats the process.

[1517] Input: Corrected input data

[1518] Output: Encrypted data sent to the server

[1519] The system's processing steps integrate the entire process and allow users to operate it intuitively. Rapid feedback and correction of generated documents greatly streamlines the generation and review of legal documents and security policies.

[1520] 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.

[1521] This invention combines a system that uses generative AI to assist in the creation and review of legal documents with an emotion engine that recognizes the user's emotions. This system is implemented as follows.

[1522] Server Roles

[1523] 1. Creating and updating legal databases

[1524] The server periodically collects publicly available data on laws, precedents, rules, etc., and generates and updates the database.

[1525] The collected data is converted into a unified format and prepared as training data for the generative artificial intelligence model.

[1526] 2. Training generative AI models

[1527] The server categorizes the legal documents in the database and prepares training and test data.

[1528] The server initializes the generative AI model and trains it using the training data.

[1529] The server uses test data to evaluate the model's performance and adjusts parameters as needed.

[1530] 3. Processing User Requests

[1531] A server receives a request from a user for legal document creation or verification.

[1532] The server analyzes the user's input data and extracts the necessary information.

[1533] The extracted information is fed into a generative AI model to generate legal documents.

[1534] The generated legal document is converted into an appropriate format for presentation to the user.

[1535] Terminal role (client side)

[1536] 1. Providing a user interface

[1537] The terminal displays an input form to the user and provides an interface for accepting requests for legal document creation and confirmation.

[1538] It has fields for entering necessary information (e.g., contractor information, contract terms, etc.).

[1539] 2. Operation of the Emotion Engine

[1540] An emotion engine built into the device analyzes the user's facial expressions and voice in real time to determine their emotional state.

[1541] The emotion engine adjusts the interface and the content of the presented documents appropriately depending on the user's emotional state.

[1542] 3. Data transmission and reception

[1543] The data entered by the user is encrypted and sent to the server using a secure communication protocol (e.g., HTTPS).

[1544] The generated legal document is received from the server and displayed to the user.

[1545] Provide interactive feedback so users can make corrections as needed.

[1546] User Roles

[1547] 1. Request for document creation

[1548] The user accurately enters the required information (contractor information, contract terms, etc.) into the input form on the device.

[1549] The user checks the contents and presses the send button to send the data to the server.

[1550] 2. Check and correct the document

[1551] The user receives the generated legal document returned from the server and checks the contents in detail.

[1552] If necessary, enter corrections and press the send button again to send it to the server.

[1553] The corrected document is received again from the server and a final check is performed.

[1554] Specific examples

[1555] Example 1: Creating an employment contract

[1556] 1. User Action

[1557] The user selects "Create a new employment contract" on the terminal and enters the employer's name, employee's name, employment conditions, etc.

[1558] The emotion engine analyzes the user's facial expressions and voice, and if it detects emotions such as tension or anxiety, it makes the interface gentler and displays messages encouraging relaxation.

[1559] Once you have completed the input, click the send button to send the data to the server.

[1560] 2. Server Processing

[1561] The server receives the user's input data and performs analysis.

[1562] The analysis results are input into a generative AI model to generate an employment contract.

[1563] The generated employment contract is returned to the user.

[1564] 3. User Actions

[1565] The user checks the returned employment contract, enters corrections as necessary, and resubmits it.

[1566] Depending on the situation, the emotion engine will analyze the user's emotions again and provide a support message.

[1567] Example 2: Checking legal notices

[1568] 1. User Action

[1569] The user selects "Review Legal Notices" on their device and copies and pastes the notice.

[1570] If the emotion engine detects stress or anxiety, it will provide reassurance by displaying supportive messages and brief explanations.

[1571] Send a confirmation request.

[1572] 2. Server Processing

[1573] The server receives the notification content and analyzes it using the generative AI model.

[1574] Legal issues and improvement proposals are extracted and returned to the user.

[1575] 3. User Actions

[1576] The user modifies the notification document based on the received results.

[1577] An emotion engine continues to monitor the user's emotional state and provides supportive messages as needed.

[1578] In this way, the system of the present invention utilizes generative artificial intelligence models to not only assist in the rapid and accurate creation and review of legal documents, but also respond to the user's emotional state, thereby improving the user experience and reducing the stress of legal work.

[1579] The processing flow will be explained below.

[1580] Server Processing

[1581] Step 1: Generate and update the legal database

[1582] The server periodically collects publicly available data on laws, precedents, regulations, etc.

[1583] The collected data is converted into a unified format and prepared as training data for the generative artificial intelligence model.

[1584] Update the legal database, add new data and replace old data with the latest.

[1585] Step 2: Training the generative AI model

[1586] The server categorizes the legal documents in the database and prepares training and test data.

[1587] The server initializes the generative AI model and trains it using the training data.

[1588] The server uses test data to evaluate the model's performance and adjusts parameters as needed.

[1589] Step 3: Processing the user request

[1590] A server receives a request from a user for legal document creation or verification.

[1591] The server analyzes the user's input data and extracts the necessary information.

[1592] The extracted information is fed into a generative AI model to generate legal documents.

[1593] The generated legal document is converted into an appropriate format for presentation to the user.

[1594] The generated legal document is transmitted to the user via encrypted communication.

[1595] Terminal processing (client side)

[1596] Step 1: Providing a User Interface

[1597] The terminal displays an input form to the user and provides an interface for accepting requests for legal document creation and confirmation.

[1598] It displays fields for information that the user must enter and provides guide and warning messages to assist with entry.

[1599] Step 2: Emotion Engine in Action

[1600] An emotion engine built into the device analyzes the user's facial expressions and voice in real time to determine their emotional state.

[1601] The emotion engine adjusts the interface and the content of the presented documents appropriately depending on the user's emotional state.

[1602] Step 3: Sending and Receiving Data

[1603] The device encrypts the user's input and sends it to the server using a secure communication protocol (e.g., HTTPS).

[1604] The terminal receives the generated legal document from the server and displays it to the user.

[1605] Provide interactive feedback so users can make corrections as needed.

[1606] User Action

[1607] Step 1: Request for documentation

[1608] The user accurately enters the required information (contractor information, contract terms, etc.) into the input form on the device.

[1609] The user checks the contents and presses the send button to send the data to the server.

[1610] Step 2: Review and correct the document

[1611] The user receives the generated legal document returned from the server and checks the contents in detail.

[1612] If necessary, enter corrections and press the send button again to send it to the server.

[1613] The corrected document is received again from the server and a final check is performed.

[1614] Detailed processing of specific examples

[1615] Example 1: Creating an employment contract

[1616] Step 1: The user selects "Create a new employment contract" on the terminal and enters the employer name, employee name, employment conditions, etc.

[1617] Step 2: The emotion engine analyzes the user's facial expressions and voice, and if it detects emotions such as tension or anxiety, it softens the interface and displays a relaxing message.

[1618] Step 3: The user checks the input and presses the submit button to send it to the server.

[1619] Step 4: The server receives the user's input data and performs analysis.

[1620] Step 5: The server inputs the analysis results into the generation AI model and generates an employment contract.

[1621] Step 6: The server returns the generated employment contract to the user.

[1622] Step 7: The user checks the returned employment contract, enters corrections as necessary, and resubmits it.

[1623] Step 8: The server receives the correction request, regenerates the employment contract, and returns it to the user.

[1624] Step 9: User gives final review and approval.

[1625] Example 2: Checking legal notices

[1626] Step 1: The user selects "Review Legal Notices" on their device and copies and pastes the notice.

[1627] Step 2: If the emotion engine detects stress or anxiety, it displays a supportive message or brief explanation to provide reassurance.

[1628] Step 3: The user submits a confirmation request.

[1629] Step 4: The server receives the notification and analyzes it using the generative AI model.

[1630] Step 5: The server extracts legal issues and improvement proposals from the analysis results.

[1631] Step 6: The server returns the extracted results to the user.

[1632] Step 7: The user modifies the notification document based on the returned results.

[1633] Step 8: The emotion engine continues to monitor the user's emotional state and provides support messages as needed.

[1634] Step 9: The user performs a final check and completes the correction.

[1635] Example 2

[1636] 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."

[1637] While conventional legal document creation systems have achieved a certain level of accuracy and speed in the creation of documents, they lack the functionality to consider the user's emotional state, which hinders the user experience.Furthermore, there are still concerns about privacy and data confidentiality due to the lack of secure means to send and receive user-entered data and generated documents.

[1638] 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.

[1639] In this invention, the server includes a means for creating legal documents using a generative artificial intelligence model, a means for using an emotion engine that analyzes the user's emotional state and appropriately adjusts the interface and document content, and a means for providing the generated legal documents to the user. This makes it possible to provide an interface that takes the user's emotional state into consideration while maintaining the accuracy and speed of the legal documents. Furthermore, by adding a means for securely sending and receiving data entered by the user and the generated documents, concerns about privacy and confidentiality can be alleviated.

[1640] "Generative artificial intelligence model" refers to a machine learning algorithm for automatically generating legal documents.

[1641] "Legal Document" means a document containing legal content, such as a contract, legal notice, or legal precedent.

[1642] "User-input data" refers to data entered by a user to provide information necessary for generating or requesting validation of a legal document.

[1643] An "emotion engine" refers to technology that analyzes a user's facial expressions and voice to determine the user's emotional state in real time.

[1644] "Interface" refers to the input and output means by which a user interacts with a system.

[1645] "Data transmission and reception" refers to the means of communication for securely exchanging data between the user and the server.

[1646] "Encryption" refers to the technology of converting data into a format that cannot be deciphered by third parties in order to transmit the data securely.

[1647] "Training data" refers to the data set used to train a generative AI model.

[1648] "Test data" refers to a dataset used to evaluate the performance of a generative AI model after training.

[1649] "Analysis" refers to the process of analyzing input data and generated documents to extract necessary information.

[1650] The present invention is a system that uses a generative AI model to assist in the creation and review of legal documents, and also combines it with an emotion engine that analyzes user emotions. Specific embodiments of this system are described below.

[1651] Server Roles

[1652] Hardware and Software Use

[1653] The server should be equipped with a high-performance processor and large memory capacity. Specifically, a server equipped with a GPU (e.g., a machine equipped with an NVIDIA GPU) is preferable. For software, a machine learning framework such as TensorFlow or PyTorch is used to build, train, and run the generative AI model.

[1654] Creating and updating legal databases

[1655] The server regularly collects publicly available data on laws, precedents, rules, etc., and generates and updates the database. The collected data is converted into a unified format and used as training data for the generative AI model. This ensures that the latest legal information is always available.

[1656] Training generative AI models

[1657] The server categorizes legal documents in the database, prepares training data and test data, initializes a generative AI model (e.g., GPT-3, BERT), and trains the model using the training data. Performance is evaluated and parameters are adjusted as needed to enable highly accurate legal document generation.

[1658] Processing user requests

[1659] The server receives a request from a user to create or review a legal document, analyzes the input data, extracts the necessary information, inputs the extracted information into a generative AI model to generate the legal document, and then converts the generated legal document into an appropriate format (e.g., PDF or Word document) and provides it to the user.

[1660] Terminal role (client side)

[1661] Hardware and Software Use

[1662] The devices used include PCs, tablets, and smartphones. The devices require a camera and microphone to support the operation of the emotion engine. The software used is an emotion analysis engine (e.g., OpenFace or Microsoft Azure's Face API).

[1663] Providing a user interface

[1664] The terminal displays an input form to the user, providing an interface for accepting requests for legal document creation and confirmation, along with fields for entering required information (e.g., contract information, contract terms, etc.).

[1665] Emotion Engine Operation

[1666] An emotion engine built into the device analyzes the user's facial expressions and voice in real time to determine their emotional state. The emotion engine then adjusts the interface and the content of the documents provided appropriately based on the user's emotional state. For example, if the user is nervous, it displays a message encouraging them to relax.

[1667] Sending and receiving data

[1668] Encrypts the data entered by the user and sends it to the server using a secure communication protocol (e.g., HTTPS), receives the generated legal document from the server and displays it to the user, and provides interactive feedback to the user to allow them to make corrections, if necessary.

[1669] User Roles

[1670] Document creation request

[1671] The user accurately enters the necessary information into the input form on the terminal and presses the send button to send the data to the server.

[1672] Document review and correction

[1673] The user receives the generated legal document sent from the server, checks its contents in detail, enters corrections as necessary, and presses the send button again to send it to the server. The corrected document is then received again from the server and a final check is made.

[1674] Specific examples

[1675] Example 1: Creating an employment contract

[1676] 1. User Action

[1677] The user selects "Create a new employment contract" on the terminal and enters employer information, employee information, and employment conditions.

[1678] The emotion engine detects tension from the user's facial expressions and voice and displays the message, "Please relax and continue typing."

[1679] 2. Server Processing

[1680] The server receives and analyzes the user's input data and inputs it into the generative AI model.

[1681] Generate an employment contract, convert it to PDF and return it to the device.

[1682] 3. User Actions

[1683] The user reviews the returned contract, enters any necessary corrections, and resubmits it. The emotion engine continues to monitor emotions and displays support messages.

[1684] Example 2: Checking legal notices

[1685] 1. User Action

[1686] The user selects "Review Legal Notice" on the device and enters the notice.

[1687] The emotion engine detects stress and displays, "Don't worry. An expert will check."

[1688] 2. Server Processing

[1689] The server receives the notification content, analyzes it, and inputs it into the generative AI model.

[1690] Legal issues and improvement proposals are extracted and returned to the user.

[1691] 3. User Actions

[1692] The user modifies the notification based on the returned results. The emotion engine continues to monitor emotions and displays support messages as needed.

[1693] Prompt Sentence Examples

[1694] 1. Please create a new employment contract. The employer information is 'Company A', the employee information is 'Mr. B', and the employment conditions are 'Full-time, monthly salary 300,000 yen'.

[1695] 2. What is the problem with this legal notice? The notice reads: 'This is a notice from Company X to Company Y.'

[1696] From the above, this system effectively combines generative AI models and an emotion engine to generate and verify legal documents quickly and accurately, while providing support tailored to the user's emotional state, thereby reducing the stress of legal work and improving the user experience.

[1697] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1698] Step 1:

[1699] Data collection and database generation and updating

[1700] The server collects public legal data (e.g., legal data, precedents, and rules). Inputs include data from internet legal databases and court websites. It stores this data in local storage.

[1701] The server converts the collected data into a unified format, for example, converting data in different formats into XML or JSON.

[1702] The server updates the existing database with new or updated data, checks for duplicates and inconsistencies, and cleans the data.

[1703] The output is a cleaned legal document database in a unified format.

[1704] Step 2:

[1705] Training generative AI models

[1706] The server splits the legal document data in the database into training data and test data. The input is the unified database obtained in the previous step.

[1707] The server initializes a generative AI model (e.g., GPT-3, BERT) and trains it with training data. Specifically, it uses TensorFlow or PyTorch to train the model while feeding it data.

[1708] The server evaluates the trained model on the test data and adjusts the parameters as needed.

[1709] The output is a trained generative AI model capable of generating legal documents with high accuracy.

[1710] Step 3:

[1711] Processing user requests

[1712] The server receives requests from users to create or verify legal documents, and the input includes user input data sent from the terminal via HTTPS.

[1713] The server analyzes the received data and extracts necessary information (e.g., contract information, legal terms). Data analysis uses natural language processing technology. Specifically, it uses a text analysis algorithm.

[1714] The server inputs the extracted information into a generative AI model to generate the necessary legal documents.

[1715] The server converts the generated legal document into an appropriate format (e.g., PDF, Word document) and provides it to the user.

[1716] As an output, a legal document generated based on the user's request is sent to the terminal.

[1717] Step 4:

[1718] Emotion Engine Operation

[1719] The emotion engine built into the device analyzes the user's facial expressions and voice in real time. The inputs include facial expression data and voice data obtained through the camera and microphone.

[1720] The device determines the user's emotional state based on the analysis results of the emotion engine, for example, using facial recognition technology or voice tone analysis.

[1721] The device will adjust the interface and messages provided appropriately depending on the user's emotional state.

[1722] The output is a tailored interface or message that reflects the user's emotional state.

[1723] Step 5:

[1724] User data transmission and feedback

[1725] The user enters the necessary information into the input fields on the terminal and presses the send button. Input includes contract information, legal terms, etc.

[1726] The terminal encrypts the user's input data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[1727] The server transmits the generated legal document to the terminal, where it is received by the user.

[1728] The user checks the generated document and, if necessary, inputs corrections through the terminal interface, which then resends it.

[1729] The device provides interactive feedback, allowing users to make corrections, and the emotion engine continuously monitors the user's emotional state and displays appropriate support messages.

[1730] The output is a final legal document that the user has reviewed and corrected.

[1731] (Application example 2)

[1732] 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."

[1733] Conventional legal document drafting systems do not take into account the user's emotional state, which means they are unable to provide efficient support, especially to users who feel nervous or anxious. Furthermore, the lack of emotion analysis prevents an improved user experience and makes it difficult to reduce stress and anxiety.

[1734] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for creating a legal document using a generative AI model; means for generating the legal document created by the generative AI model based on user input data; means for providing the generated legal document to the user; means for combining an emotion engine that analyzes the user's emotional state; and means for adjusting the interface and the content of the provided document based on the emotion data generated by the emotion engine. This makes it possible to support the creation and review of legal documents individually tailored according to the user's emotional state, thereby improving the user experience and reducing stress and anxiety.

[1735] A "generative artificial intelligence model" is a model that uses machine learning algorithms to create legal documents.

[1736] "Legal documents" refers to legal documents such as contracts, regulations, notices, etc.

[1737] "User input data" refers to information and conditions provided by a user for document creation.

[1738] The "emotion engine" is a system that analyzes the user's facial expressions and voice to determine their emotional state.

[1739] "Emotional Data" refers to information about a user's emotional state generated by an emotion engine.

[1740] "Interface" refers to the screen and operating means that allow a user to interact with a system.

[1741] This invention is a system that combines a generative AI model and an emotion engine. The system is mainly composed of a server and a terminal, and provides emotional support to users when creating and reviewing legal documents. Specific embodiments for implementing this invention are described below.

[1742] Server Roles

[1743] The server performs the following processing.

[1744] 1. Collection and organization of legal data:

[1745] The server periodically collects data on laws, precedents, regulations, etc., creates and updates a database, converts this data into a unified format, and prepares it as training data for generative AI models.

[1746] 2. Training the generative AI model:

[1747] The server categorizes legal document data and uses it to train a generative AI model. After training, the model's performance is evaluated and parameters are adjusted as needed.

[1748] 3. Processing User Requests:

[1749] The server receives a request from a user to create or verify a legal document, analyzes the user's input data, inputs the analyzed data into a generative AI model to generate the required legal document, and then converts the generated legal document into an appropriate format for delivery to the user.

[1750] Terminal role (client side)

[1751] The terminal performs the following processing:

[1752] 1. Providing the user interface:

[1753] The terminal displays an input form to the user, providing an interface for accepting requests for legal document creation and confirmation, and includes fields for entering required information (contractor information, contract terms, etc.).

[1754] 2. How the Emotion Engine works:

[1755] An emotion engine built into the device analyzes the user's facial expressions and voice in real time to determine their emotional state, and then adjusts the interface and the content of the documents presented appropriately based on the user's emotional state.

[1756] 3. Sending and receiving data:

[1757] The device encrypts the data entered by the user and sends it to the server using a secure communication protocol (e.g., HTTPS), and receives the generated legal document from the server and displays it to the user, providing interactive feedback so that the user can make corrections if necessary.

[1758] User Roles

[1759] 1. Document Request:

[1760] The user accurately enters the required information into the input form on the device, and when the input is complete, presses the send button to send the data to the server.

[1761] 2. Document review and correction:

[1762] The user receives the generated legal document returned from the server, checks its contents in detail, enters corrections as necessary, and presses the send button again to send it to the server. The user then receives the corrected document again and makes a final check.

[1763] Specific examples

[1764] Example 1: Creating an employment contract

[1765] The user selects "Create a new employment contract" and enters the employer's name, employee's name, employment conditions, etc. The emotion engine analyzes the user's facial expressions and voice, and if it detects emotions such as tension or anxiety, it makes the interface gentler and displays a message encouraging relaxation. Once the input is complete, the user presses the send button to send the data to the server.

[1766] The server receives the user's input data, analyzes it, inputs the analysis results into the generative AI model to generate an employment contract, and then returns the generated employment contract to the user.

[1767] The user checks the returned employment contract, enters any necessary corrections, and resubmits it. Depending on the situation, the emotion engine will again analyze the user's emotions and provide a support message.

[1768] Example 2: Checking legal notices

[1769] The user selects "Confirm Legal Notice" and enters the notification content. If the emotion engine senses stress or anxiety, it provides a sense of security by displaying a support message or a simple explanation. When a confirmation request is sent, the server receives the notification content and analyzes it using a generative AI model. Legal issues and improvement proposals are extracted and sent back to the user.

[1770] The user can modify the notification based on the received results, and the emotion engine continues to monitor the user's emotional state and provide support messages as needed.

[1771] Prompt Sentence Examples

[1772] "Please create a new employment contract. Please use the following information:

[1773] Employer Name: Tanaka

[1774] Employee name: Suzuki

[1775] Employment conditions: Full-time, annual salary of 6 million yen, two days off per week

[1776] Using this prompt, the generative AI model can quickly and accurately generate the required legal documents.

[1777] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1778] Step 1:

[1779] The terminal displays an input form to the user and accepts requests for legal document creation and confirmation. The user enters the necessary information, such as contract holder information and contract terms. The terminal encrypts the entered data and sends it to the server using a secure communication protocol (e.g., HTTPS). Contract holder information and contract terms are provided as input data, and subsequent document generation is based on this.

[1780] Step 2:

[1781] The server receives and analyzes input data sent by the user. The input data includes the name of the contracting party, contract terms, etc., and the server analyzes this data to extract the necessary information. The analysis results are input into a generative AI model to generate the legal document desired by the user. For example, in response to a request to "create a new employment contract," a corresponding employment contract is generated.

[1782] Step 3:

[1783] The server converts the generated legal document into an appropriate format and returns it to the terminal, including checking and formatting the content of the generated document. The output legal document is provided in a format that is easy for the user to review.

[1784] Step 4:

[1785] The terminal receives the legal document returned from the server and displays it to the user. The user checks the displayed document and makes any necessary corrections. The terminal then sends the information added or corrected by the user back to the server. The user's corrected data is re-entered through the interface, thereby improving the accuracy of the document.

[1786] Step 5:

[1787] An emotion engine built into the device analyzes the user's facial expressions and voice to determine their emotional state in real time. If the emotion engine detects the user's emotional state (e.g., tension or anxiety), the device adjusts the interface and display content. For example, it may display a relaxing message. This allows the user to use the system with peace of mind.

[1788] Step 6:

[1789] The final legal document that the user has reviewed and corrected is then sent from the device to the server, where it is reviewed and formatted again.The final output is a legal document that is accurate and takes into account the user's emotional state and is provided to the user.

[1790] These steps result in a system where a comprehensive legal document creation and review process is achieved by combining generative AI models and an emotion engine.

[1791] 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.

[1792] 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.

[1793] 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.

[1794] [Fourth embodiment]

[1795] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1796] 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.

[1797] 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).

[1798] 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.

[1799] 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.

[1800] 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).

[1801] 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.

[1802] 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.

[1803] 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.

[1804] 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.

[1805] 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.

[1806] 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.

[1807] 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."

[1808] The present invention is a system that uses generative AI to support the creation and verification of legal documents, and is implemented as follows.

[1809] Server Roles

[1810] 1. Creating and updating legal databases

[1811] The server periodically collects data such as published laws, precedents, and regulations, and generates and updates the database.

[1812] The collected data is converted into a unified format and used as training data for generative artificial intelligence models.

[1813] 2. Training generative AI models

[1814] The server uses the collected legal data to train a generative artificial intelligence model.

[1815] During training, data is divided into categories and used as training data and test data.

[1816] Evaluate the model's performance and adjust parameters as needed.

[1817] 3. Processing User Requests

[1818] Receive requests from users for legal document creation or verification.

[1819] Based on the request content, the data provided by the user is analyzed and the necessary information is extracted.

[1820] The extracted information is fed into a generative AI model to generate legal documents.

[1821] The generated document is provided to the user.

[1822] Device Role

[1823] 1. Providing a user interface

[1824] The terminal provides an intuitive input form and interface for users to create and request verification of legal documents.

[1825] It has fields for entering required information (e.g., contractor information, contract terms, etc.).

[1826] 2. Data transmission and reception

[1827] The data entered by the user is encrypted and sent securely to the server.

[1828] The generated document is received from the server and displayed to the user.

[1829] User Roles

[1830] 1. Request for document creation

[1831] The user enters the necessary information (e.g., the name of the contract holder, contract terms) into the input form on the terminal and presses the send button.

[1832] Check the input and send it to the server.

[1833] 2. Check and correct the document

[1834] The legal document returned from the server is checked, and any necessary corrections are specified via the input interface.

[1835] If there are any corrections, the document is sent to the server again and the corrected document is received.

[1836] Specific examples

[1837] Example 1: Creating an employment contract

[1838] 1. User Action:

[1839] The user selects "Create a new employment contract" on the terminal and enters the employer's name, employee's name, employment conditions, etc.

[1840] Once you have completed the input, click the send button to send the data to the server.

[1841] 2. Server processing:

[1842] The server receives the user's input data, analyzes it, and inputs it into the generative AI model.

[1843] The generative AI model generates an employment contract and sends its contents back to the user from the server.

[1844] 3. User Action:

[1845] The user checks the returned employment contract, enters corrections as necessary, and resubmits it.

[1846] Example 2: Checking legal notices

[1847] 1. User Action:

[1848] The user selects "Review Legal Notices" on their device and copies and pastes the notice.

[1849] Send a confirmation request.

[1850] 2. Server processing:

[1851] The server receives the notification content and analyzes it using the generative AI model.

[1852] Legal issues and improvement proposals are extracted and returned to the user from the server.

[1853] 3. User Action:

[1854] The user corrects the notification document based on the returned results.

[1855] In this way, the system of the present invention utilizes generative artificial intelligence models to support the rapid and accurate creation and validation of legal documents. Users can efficiently create and validate legal documents through an intuitive interface.

[1856] The processing flow will be explained below.

[1857] Server Processing

[1858] Step 1: Generate and update the legal database

[1859] The server periodically collects publicly available data on laws, precedents, regulations, etc.

[1860] The collected data is converted into a unified format and prepared as training data for the generative artificial intelligence model.

[1861] Update the legal database, add new data and replace old data with the latest.

[1862] Step 2: Training the generative AI model

[1863] The server categorizes the legal documents in the database and prepares training and test data.

[1864] The server initializes the generative AI model and trains it using the training data.

[1865] The server uses test data to evaluate the model's performance and adjusts parameters as needed.

[1866] Step 3: Processing the user request

[1867] A server receives a request from a user for legal document creation or verification.

[1868] The server analyzes the user's input data and extracts the necessary information.

[1869] The extracted information is fed into a generative AI model to generate legal documents.

[1870] The generated legal document is converted into an appropriate format for presentation to the user.

[1871] The generated legal document is transmitted to the user via encrypted communication.

[1872] Terminal processing (client side)

[1873] Step 1: Providing a User Interface

[1874] The terminal displays an input form to the user and provides an interface for accepting requests for legal document creation and confirmation.

[1875] It displays fields for information that the user must enter and provides guide and warning messages to assist with entry.

[1876] Step 2: Sending and Receiving Data

[1877] The device encrypts the user's input and sends it to the server using a secure communication protocol (e.g., HTTPS).

[1878] The terminal receives the generated legal document from the server and displays it to the user.

[1879] Provide interactive feedback so users can make corrections as needed.

[1880] User Action

[1881] Step 1: Request for documentation

[1882] The user accurately enters the required information (contractor information, contract terms, etc.) into the input form on the device.

[1883] The user checks the contents and presses the send button to send the data to the server.

[1884] Step 2: Review and correct the document

[1885] The user receives the generated legal document returned from the server and checks the contents in detail.

[1886] If necessary, enter corrections and press the send button again to send it to the server.

[1887] The corrected document is received again from the server and a final check is performed.

[1888] Detailed processing of specific examples

[1889] Example 1: Creating an employment contract

[1890] Step 1: The user selects "Create a new employment contract" on the terminal and enters the employer name, employee name, employment conditions, etc.

[1891] Step 2: Check the information entered by the user and press the submit button to send it to the server.

[1892] Step 3: The server receives the user's input data and performs analysis.

[1893] Step 4: The server inputs the analysis results into the generation AI model and generates an employment contract.

[1894] Step 5: The server returns the generated employment contract to the user.

[1895] Step 6: The user checks the returned employment contract, enters any necessary corrections, and resubmits it.

[1896] Step 7: The server receives the correction request, regenerates the employment contract, and returns it to the user.

[1897] Example 2: Checking legal notices

[1898] Step 1: The user selects "Review Legal Notices" on their device and copies and pastes the notice.

[1899] Step 2: The user submits a confirmation request.

[1900] Step 3: The server receives the notification and analyzes it using the generative AI model.

[1901] Step 4: The server extracts legal issues and improvement proposals from the analysis results.

[1902] Step 5: The server returns the extracted results to the user.

[1903] Step 6: The user modifies the notification document based on the received results.

[1904] Example 1

[1905] 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."

[1906] Currently, creating and reviewing legal documents requires a great deal of time and effort. It is particularly difficult for users without specialized knowledge to create accurate legal documents. Furthermore, existing document creation systems often have complex user interfaces and raise concerns about data security. To solve these problems, a system that supports the efficient and accurate creation and review of legal documents using generative AI models is needed.

[1907] 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.

[1908] In this invention, the server includes means for collecting legal data, standardizing the format, and converting it into a database, means for training a generative AI model based on the database, means for encrypting and receiving data entered by a user, means for analyzing the user's input data, extracting necessary information, and inputting it into the generative AI model, and means for converting legal documents generated by the generative AI model into PDF format, encrypting the data, and providing the legal documents to the user. This allows users to quickly and accurately create and review legal documents without requiring specialized knowledge, and ensures data security.

[1909] "Legal data" refers to publicly available laws, precedents, regulations, statutes, guidelines, etc.

[1910] A "generative artificial intelligence model" refers to a generative artificial intelligence model that is trained on a large amount of legal data and has the ability to automatically generate legal documents.

[1911] "User" means any person or organization that uses this system to create or review legal documents.

[1912] "Encryption" refers to the technology of converting data being sent and received into a format that cannot be read by third parties, thereby ensuring secure communication.

[1913] "Database" means a structured digital storage system for storing and making readily accessible collected and organized legal data.

[1914] "PDF format" is an abbreviation for Portable Document Format and refers to a standard file format for electronically storing and displaying documents.

[1915] "Input interface" refers to the software window through which a user inputs information and interacts with a system.

[1916] "Analysis" refers to the process of segmenting and classifying received data, extracting specific information, and using it in a useful way.

[1917] "Training" refers to the process of using large amounts of data to train a generative artificial intelligence model and improve its performance.

[1918] The present invention is a system that uses generative AI to support the creation and verification of legal documents, and is implemented as follows.

[1919] Server Roles

[1920] 1. Creating and updating legal databases

[1921] The server periodically collects data on laws, precedents, rules, etc. that are publicly available via the Internet, and generates and updates a database, which is periodically maintained.

[1922] The collected data is converted into a standardized format (e.g., XML or JSON) and used as training data for generative artificial intelligence models.

[1923] 2. Training generative AI models

[1924] The server uses the collected legal data to train a generative AI model (e.g., GPT-3), dividing the data into multiple categories and using them as training and test data.

[1925] After training, the model's performance is evaluated and parameters are adjusted as needed. This training is done using Python libraries such as TensorFlow and PyTorch.

[1926] 3. Processing User Requests

[1927] The server receives requests from users for legal document creation or verification, and the data entered by the user is sent to the server in encrypted form.

[1928] The received data is analyzed on the server side, and the necessary information is extracted and input into the generative artificial intelligence model.

[1929] A generative artificial intelligence model generates legal documents, converts their contents into PDF format, encrypts them, and provides them to users.

[1930] Device Role

[1931] 1. Providing a user interface

[1932] The terminal provides an intuitive input form and interface for users to create and request verification of legal documents.

[1933] It has fields for entering required information (e.g., contractor information, contract terms, etc.).

[1934] The terminal uses HTML and JavaScript to present interactive forms.

[1935] 2. Data transmission and reception

[1936] The terminal encrypts the data entered by the user and sends it securely to the server using the HTTPS protocol.

[1937] The generated document is received from the server, securely decrypted, and displayed to the user using a PDF viewer or similar.

[1938] User Roles

[1939] 1. Request for document creation

[1940] The user enters the necessary information (e.g., contract holder name, contract terms) into the input form on the terminal and presses the send button.

[1941] Check the input and send it to the server.

[1942] 2. Check and correct the document

[1943] The legal document returned from the server is checked, and any necessary corrections are specified via the input interface.

[1944] If there are any corrections, the document is sent to the server again and the corrected document is received.

[1945] Specific examples

[1946] Example 1: Creating an employment contract

[1947] 1. User Action:

[1948] The user selects "Create a new employment contract" on the terminal and enters the employer's name, employee's name, employment conditions, etc.

[1949] Once you have completed the input, click the send button to send the data to the server.

[1950] Example prompt: "To create a new employment contract, please enter the employer name, employee name, and employment terms."

[1951] 2. Server processing:

[1952] The server receives the user's input data, analyzes it, and inputs it into the generative AI model.

[1953] The generative AI model generates an employment contract and returns its contents to the user from the server.

[1954] 3. User Action:

[1955] The user checks the returned employment contract, enters corrections as necessary, and resubmits it.

[1956] Example 2: Checking legal notices

[1957] 1. User Action:

[1958] The user selects "Review Legal Notices" on their device and copies and pastes the notice.

[1959] Send a confirmation request.

[1960] Example prompt: "To view the legal notices, please copy and paste them."

[1961] 2. Server processing:

[1962] The server receives the notification content and analyzes it using the generative AI model.

[1963] Legal issues and improvement proposals are extracted and returned to the user from the server.

[1964] 3. User Action:

[1965] The user corrects the notification document based on the returned results.

[1966] In this way, the system of the present invention utilizes generative AI models to support the rapid and accurate creation and validation of legal documents. Users can efficiently generate and validate legal documents through an intuitive interface.

[1967] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1968] Step 1: Collect and format legal data

[1969] Input: Publicly available data such as laws, precedents, and regulations

[1970] How it works: The server periodically runs a scraping script to collect legal data from the internet, then uses a Python script to parse the raw data and convert it into a standardized format such as XML or JSON.

[1971] Data processing / data calculation: parsing and formatting raw data

[1972] Output: Legal data in a standardized format

[1973] Step 2: Update the database

[1974] Input: Legal data in a standardized format

[1975] Specific operation: The server uses an SQL query to add the converted data to the existing database, checking for duplicate data and ensuring consistency.

[1976] Data processing / data calculation: data insertion and duplication check

[1977] Output: A database containing up-to-date legal data

[1978] Step 3: Training the generative artificial intelligence model

[1979] Input: Legal data stored in a database

[1980] How it works: The server uses Python's Pandas library to load legal data by category and feed it into a generative AI model, which is trained using TensorFlow and PyTorch.

[1981] Data processing / data calculation: data pre-processing and model training

[1982] Output: A trained generative AI model

[1983] Step 4: Receiving user data

[1984] Input: A legal document request entered by a user at a terminal.

[1985] Specific operation: The user enters the required information on the device and presses the send button. The device encrypts the input information using AES and sends it to the server via HTTPS.

[1986] Data processing / data calculation: Encryption and transmission of user data

[1987] Output: Encrypted user data received by the server

[1988] Step 5: Analyze user data

[1989] Input: Encrypted user data received by the server

[1990] What happens: The server decrypts the data and parses it using a natural language processing (NLP) library to extract the necessary information (e.g., contract name, contract terms).

[1991] Data processing / data calculation: data interpretation and information extraction

[1992] Output: Parsed data to feed into a generative AI model

[1993] Step 6: Generate legal documents

[1994] Input: Parsed user data

[1995] Specific operation: The server inputs the analyzed data into the generative AI model and generates the required legal documents.

[1996] Data processing / data calculation: Document generation using generative AI models

[1997] Output: Generated legal document

[1998] Step 7: Encrypt and return the document

[1999] Input: Generated legal document

[2000] What happens: The server converts the generated legal document into PDF format, encrypts it using AES, and then returns the encrypted PDF to the user.

[2001] Data processing / data calculation: document format conversion and encryption

[2002] Output: Encrypted legal document in PDF format

[2003] Step 8: Review the document and request corrections

[2004] Input: Encrypted legal document in PDF format

[2005] Specific operation: The device decrypts the encrypted PDF received from the server and displays it to the user. The user checks the displayed document and, if any corrections are required, enters the corrections again and submits it.

[2006] Data processing / data calculation: Displaying documents and inputting corrections

[2007] Output: Correction request data

[2008] Through these steps, the system can efficiently and safely create and verify legal documents according to user requirements. At each processing step, specific actions and data processing / calculation are performed, resulting in fast and accurate document generation.

[2009] (Application example 1)

[2010] 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."

[2011] In recent years, the creation and review of security-related legal documents and regulations has increased, requiring rapid and accurate responses. However, creating and reviewing these documents requires specialized knowledge, which is a significant time and labor-intensive process. Furthermore, there is a lack of efficient means for correcting and providing feedback on created documents. Therefore, there is a need for a system that allows users to easily create security-related legal documents and regulations and quickly review and modify them.

[2012] 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.

[2013] In this invention, the server includes means for creating legal documents using a generative AI model, means for generating the legal documents created by the generative AI model based on user input data, means for providing the generated legal documents to the user via a smartphone application, means for creating and confirming security policies and regulations related to the legal documents, and means for correcting the confirmed documents by feeding them back to the AI ​​model. This enables users to efficiently create, confirm, and correct security-related legal documents and regulations without requiring specialized knowledge.

[2014] A "generative artificial intelligence model" is an algorithm that learns from large amounts of legal data and generates legal documents based on user input.

[2015] A "legal document" is an official document created based on laws, regulations, and precedents and has the force of law.

[2016] A "smartphone application" is a software program that runs on a smartphone and can be operated interactively by a user.

[2017] A "security policy" is a document that sets out standards and guidelines for ensuring safety when using an information system.

[2018] A "policy" is a document that sets out the rules and procedures relating to a particular activity or service.

[2019] "Feedback" is the process of sending user instructions for corrections and improvements to a legal document once it has been generated back to the generative AI model.

[2020] System Overview

[2021] This system uses generative AI models to help users create, review, and modify legal documents and security policies, and is implemented primarily through server, smartphone, and user operations.

[2022] Server Roles

[2023] Hardware and Software

[2024] Hardware: High-performance servers (e.g. AWS EC2)

[2025] Software: Legal database management, generative AI models (e.g., OpenAI GPT-4)

[2026] Data Processing

[2027] 1. Data collection and formatting:

[2028] The server periodically collects legal, legal precedent, regulatory and security-related data and converts it into a unified format.

[2029] This data is used as training data for the generative AI model.

[2030] 2. Document generation using AI models:

[2031] The server receives input data from the user and extracts the required information.

[2032] The extracted information is fed into a generative AI model to automatically generate legal documents or security policies.

[2033] 3. Feedback and Corrections:

[2034] Based on user feedback, the generated document is revised.

[2035] The revised document is then fed back into the AI ​​model to generate the final document.

[2036] Device Role

[2037] Hardware and Software

[2038] Hardware: Smartphone

[2039] Software: Mobile app development frameworks (e.g., React Native, Flutter)

[2040] User Interface

[2041] 1. User input form:

[2042] The terminal provides users with intuitive input forms to collect information required for legal documents and security policies.

[2043] 2. Data transmission and reception:

[2044] The terminal encrypts the user's input data and sends it securely to the server.

[2045] Receive the generated document from the server and display it to the user.

[2046] User Roles

[2047] 1. Request and review of documentation:

[2048] The user inputs the necessary information into the input form on the terminal and requests the creation of a document.

[2049] The generated document is checked, and corrections are entered as necessary, and fed back to the server.

[2050] Specific examples

[2051] Creating a New Security Policy

[2052] 1. User Action:

[2053] The user selects "Create a new security policy" on the smartphone app and enters the policy name, scope, key requirements, etc.

[2054] Once you have completed the input, click the send button to send the data to the server.

[2055] 2. Server processing:

[2056] The server receives the user's input data, analyzes it, and inputs it into the generative AI model.

[2057] The generative AI model generates a new security policy and sends it back to the user from the server.

[2058] 3. User Action:

[2059] The user checks the returned security policy, enters corrections as necessary, and resubmits it.

[2060] Example prompt sentence:

[2061] Generate a new security policy document based on the following user-specified information:

[2062] Policy Name: System Access Control Policy

[2063] Scope: Company-wide

[2064] Main requirements:

[2065] Strengthened user authentication measures

[2066] Recording and monitoring access logs

[2067] Regular Security Reviews

[2068] The generated document should be returned in the following format:

[2069] This format allows users to significantly reduce the amount of work required in the past and quickly and accurately create, check, and modify security-related legal documents and regulations.

[2070] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2071] Step 1:

[2072] User Input

[2073] The user opens the application on their smartphone and selects "Create a new security policy." The user enters the required information, such as the policy name, scope, and key requirements. This information is entered as text into the input form fields.

[2074] Input: User's text data (policy name, scope, main requirements, etc.)

[2075] Output: Input data stored on the device

[2076] Step 2:

[2077] Data transmission

[2078] The terminal encrypts the data entered by the user and sends it to the server using a secure communication protocol, with the SSL / TLS protocol used between the server and the terminal.

[2079] Input: Input data stored on the device

[2080] Output: Encrypted data sent to the server

[2081] Step 3:

[2082] Data reception and analysis

[2083] The server decrypts the received data and analyzes the user's input data to extract the necessary information. Python libraries (e.g., pandas, numpy) are used for data analysis.

[2084] Input: Encrypted data sent to the server

[2085] Output: Parsed information (policy name, scope, key requirements, etc.)

[2086] Step 4:

[2087] Input to generative AI models

[2088] The server inputs the analyzed information into a generative AI model (e.g., OpenAI GPT-4) as a prompt sentence, which specifically contains the user's input data.

[2089] Input: Parsed information (policy name, scope, key requirements, etc.)

[2090] Output: Prompt sentence to the generative AI model

[2091] Step 5:

[2092] Document Generation

[2093] The generative AI model generates a new security policy document based on the prompt sentence. In this generation process, the AI ​​model creates the document based on the legal and security-related data it has previously learned.

[2094] Input: Prompt sentence for generative AI model

[2095] Output: Generated security policy document

[2096] Step 6:

[2097] Document Transmission

[2098] The server receives the generated security policy document, re-encrypts it, and sends it to the terminal. Again, the SSL / TLS protocol is used between the server and the terminal.

[2099] Input: The security policy document to be generated

[2100] Output: Encrypted document data sent to the device

[2101] Step 7:

[2102] User confirmation and correction

[2103] The terminal decrypts the received document data and displays it to the user. The user checks the document and, if necessary, enters corrections. The corrections are again entered as text in a field on the terminal.

[2104] Input: Encrypted document data

[2105] Output: The security policy document that is displayed to the user

[2106] Step 8:

[2107] Send corrected data

[2108] The device re-encrypts the data, including any corrections entered by the user, and sends it to the server, which receives the data and repeats the process.

[2109] Input: Corrected input data

[2110] Output: Encrypted data sent to the server

[2111] The system's processing steps integrate the entire process and allow users to operate it intuitively. Rapid feedback and correction of generated documents greatly streamlines the generation and review of legal documents and security policies.

[2112] 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.

[2113] This invention combines a system that uses generative AI to assist in the creation and review of legal documents with an emotion engine that recognizes the user's emotions. This system is implemented as follows.

[2114] Server Roles

[2115] 1. Creating and updating legal databases

[2116] The server periodically collects publicly available data on laws, precedents, rules, etc., and generates and updates the database.

[2117] The collected data is converted into a unified format and prepared as training data for the generative artificial intelligence model.

[2118] 2. Training generative AI models

[2119] The server categorizes the legal documents in the database and prepares training and test data.

[2120] The server initializes the generative AI model and trains it using the training data.

[2121] The server uses test data to evaluate the model's performance and adjusts parameters as needed.

[2122] 3. Processing User Requests

[2123] A server receives a request from a user for legal document creation or verification.

[2124] The server analyzes the user's input data and extracts the necessary information.

[2125] The extracted information is fed into a generative AI model to generate legal documents.

[2126] The generated legal document is converted into an appropriate format for presentation to the user.

[2127] Terminal role (client side)

[2128] 1. Providing a user interface

[2129] The terminal displays an input form to the user and provides an interface for accepting requests for legal document creation and confirmation.

[2130] It has fields for entering necessary information (e.g., contractor information, contract terms, etc.).

[2131] 2. Operation of the Emotion Engine

[2132] An emotion engine built into the device analyzes the user's facial expressions and voice in real time to determine their emotional state.

[2133] The emotion engine adjusts the interface and the content of the presented documents appropriately depending on the user's emotional state.

[2134] 3. Data transmission and reception

[2135] The data entered by the user is encrypted and sent to the server using a secure communication protocol (e.g., HTTPS).

[2136] The generated legal document is received from the server and displayed to the user.

[2137] Provide interactive feedback so users can make corrections as needed.

[2138] User Roles

[2139] 1. Request for document creation

[2140] The user accurately enters the required information (contractor information, contract terms, etc.) into the input form on the device.

[2141] The user checks the contents and presses the send button to send the data to the server.

[2142] 2. Check and correct the document

[2143] The user receives the generated legal document returned from the server and checks the contents in detail.

[2144] If necessary, enter corrections and press the send button again to send it to the server.

[2145] The corrected document is received again from the server and a final check is performed.

[2146] Specific examples

[2147] Example 1: Creating an employment contract

[2148] 1. User Action

[2149] The user selects "Create a new employment contract" on the terminal and enters the employer's name, employee's name, employment conditions, etc.

[2150] The emotion engine analyzes the user's facial expressions and voice, and if it detects emotions such as tension or anxiety, it makes the interface gentler and displays messages encouraging relaxation.

[2151] Once you have completed the input, click the send button to send the data to the server.

[2152] 2. Server Processing

[2153] The server receives the user's input data and performs analysis.

[2154] The analysis results are input into a generative AI model to generate an employment contract.

[2155] The generated employment contract is returned to the user.

[2156] 3. User Actions

[2157] The user checks the returned employment contract, enters corrections as necessary, and resubmits it.

[2158] Depending on the situation, the emotion engine will analyze the user's emotions again and provide a support message.

[2159] Example 2: Checking legal notices

[2160] 1. User Action

[2161] The user selects "Review Legal Notices" on their device and copies and pastes the notice.

[2162] If the emotion engine detects stress or anxiety, it will provide reassurance by displaying supportive messages and brief explanations.

[2163] Send a confirmation request.

[2164] 2. Server Processing

[2165] The server receives the notification content and analyzes it using the generative AI model.

[2166] Legal issues and improvement proposals are extracted and returned to the user.

[2167] 3. User Actions

[2168] The user modifies the notification document based on the received results.

[2169] An emotion engine continues to monitor the user's emotional state and provides supportive messages as needed.

[2170] In this way, the system of the present invention utilizes generative artificial intelligence models to not only assist in the rapid and accurate creation and review of legal documents, but also respond to the user's emotional state, thereby improving the user experience and reducing the stress of legal work.

[2171] The processing flow will be explained below.

[2172] Server Processing

[2173] Step 1: Generate and update the legal database

[2174] The server periodically collects publicly available data on laws, precedents, regulations, etc.

[2175] The collected data is converted into a unified format and prepared as training data for the generative artificial intelligence model.

[2176] Update the legal database, add new data and replace old data with the latest.

[2177] Step 2: Training the generative AI model

[2178] The server categorizes the legal documents in the database and prepares training and test data.

[2179] The server initializes the generative AI model and trains it using the training data.

[2180] The server uses test data to evaluate the model's performance and adjusts parameters as needed.

[2181] Step 3: Processing the user request

[2182] A server receives a request from a user for legal document creation or verification.

[2183] The server analyzes the user's input data and extracts the necessary information.

[2184] The extracted information is fed into a generative AI model to generate legal documents.

[2185] The generated legal document is converted into an appropriate format for presentation to the user.

[2186] The generated legal document is transmitted to the user via encrypted communication.

[2187] Terminal processing (client side)

[2188] Step 1: Providing a User Interface

[2189] The terminal displays an input form to the user and provides an interface for accepting requests for legal document creation and confirmation.

[2190] It displays fields for information that the user must enter and provides guide and warning messages to assist with entry.

[2191] Step 2: Emotion Engine in Action

[2192] An emotion engine built into the device analyzes the user's facial expressions and voice in real time to determine their emotional state.

[2193] The emotion engine adjusts the interface and the content of the presented documents appropriately depending on the user's emotional state.

[2194] Step 3: Sending and Receiving Data

[2195] The device encrypts the user's input and sends it to the server using a secure communication protocol (e.g., HTTPS).

[2196] The terminal receives the generated legal document from the server and displays it to the user.

[2197] Provide interactive feedback so users can make corrections as needed.

[2198] User Action

[2199] Step 1: Request for documentation

[2200] The user accurately enters the required information (contractor information, contract terms, etc.) into the input form on the device.

[2201] The user checks the contents and presses the send button to send the data to the server.

[2202] Step 2: Review and correct the document

[2203] The user receives the generated legal document returned from the server and checks the contents in detail.

[2204] If necessary, enter corrections and press the send button again to send it to the server.

[2205] The corrected document is received again from the server and a final check is performed.

[2206] Detailed processing of specific examples

[2207] Example 1: Creating an employment contract

[2208] Step 1: The user selects "Create a new employment contract" on the terminal and enters the employer name, employee name, employment conditions, etc.

[2209] Step 2: The emotion engine analyzes the user's facial expressions and voice, and if it detects emotions such as tension or anxiety, it softens the interface and displays a relaxing message.

[2210] Step 3: The user checks the input and presses the submit button to send it to the server.

[2211] Step 4: The server receives the user's input data and performs analysis.

[2212] Step 5: The server inputs the analysis results into the generation AI model and generates an employment contract.

[2213] Step 6: The server returns the generated employment contract to the user.

[2214] Step 7: The user checks the returned employment contract, enters corrections as necessary, and resubmits it.

[2215] Step 8: The server receives the correction request, regenerates the employment contract, and returns it to the user.

[2216] Step 9: User gives final review and approval.

[2217] Example 2: Checking legal notices

[2218] Step 1: The user selects "Review Legal Notices" on their device and copies and pastes the notice.

[2219] Step 2: If the emotion engine detects stress or anxiety, it displays a supportive message or brief explanation to provide reassurance.

[2220] Step 3: The user submits a confirmation request.

[2221] Step 4: The server receives the notification and analyzes it using the generative AI model.

[2222] Step 5: The server extracts legal issues and improvement proposals from the analysis results.

[2223] Step 6: The server returns the extracted results to the user.

[2224] Step 7: The user modifies the notification document based on the returned results.

[2225] Step 8: The emotion engine continues to monitor the user's emotional state and provides support messages as needed.

[2226] Step 9: The user performs a final check and completes the correction.

[2227] Example 2

[2228] 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."

[2229] While conventional legal document creation systems have achieved a certain level of accuracy and speed in the creation of documents, they lack the functionality to consider the user's emotional state, which hinders the user experience.Furthermore, there are still concerns about privacy and data confidentiality due to the lack of secure means to send and receive user-entered data and generated documents.

[2230] 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.

[2231] In this invention, the server includes a means for creating legal documents using a generative artificial intelligence model, a means for using an emotion engine that analyzes the user's emotional state and appropriately adjusts the interface and document content, and a means for providing the generated legal documents to the user. This makes it possible to provide an interface that takes the user's emotional state into consideration while maintaining the accuracy and speed of the legal documents. Furthermore, by adding a means for securely sending and receiving data entered by the user and the generated documents, concerns about privacy and confidentiality can be alleviated.

[2232] "Generative artificial intelligence model" refers to a machine learning algorithm for automatically generating legal documents.

[2233] "Legal Document" means a document containing legal content, such as a contract, legal notice, or legal precedent.

[2234] "User-input data" refers to data entered by a user to provide information necessary for generating or requesting validation of a legal document.

[2235] An "emotion engine" refers to technology that analyzes a user's facial expressions and voice to determine the user's emotional state in real time.

[2236] "Interface" refers to the input and output means by which a user interacts with a system.

[2237] "Data transmission and reception" refers to the means of communication for securely exchanging data between the user and the server.

[2238] "Encryption" refers to the technology of converting data into a format that cannot be deciphered by third parties in order to transmit the data securely.

[2239] "Training data" refers to the data set used to train a generative AI model.

[2240] "Test data" refers to a dataset used to evaluate the performance of a generative AI model after training.

[2241] "Analysis" refers to the process of analyzing input data and generated documents to extract necessary information.

[2242] The present invention is a system that uses a generative AI model to assist in the creation and review of legal documents, and also combines it with an emotion engine that analyzes user emotions. Specific embodiments of this system are described below.

[2243] Server Roles

[2244] Hardware and Software Use

[2245] The server should be equipped with a high-performance processor and large memory capacity. Specifically, a server equipped with a GPU (e.g., a machine equipped with an NVIDIA GPU) is preferable. For software, a machine learning framework such as TensorFlow or PyTorch is used to build, train, and run the generative AI model.

[2246] Creating and updating legal databases

[2247] The server regularly collects publicly available data on laws, precedents, rules, etc., and generates and updates the database. The collected data is converted into a unified format and used as training data for the generative AI model. This ensures that the latest legal information is always available.

[2248] Training generative AI models

[2249] The server categorizes legal documents in the database, prepares training data and test data, initializes a generative AI model (e.g., GPT-3, BERT), and trains the model using the training data. Performance is evaluated and parameters are adjusted as needed to enable highly accurate legal document generation.

[2250] Processing user requests

[2251] The server receives a request from a user to create or review a legal document, analyzes the input data, extracts the necessary information, inputs the extracted information into a generative AI model to generate the legal document, and then converts the generated legal document into an appropriate format (e.g., PDF or Word document) and provides it to the user.

[2252] Terminal role (client side)

[2253] Hardware and Software Use

[2254] The devices used include PCs, tablets, and smartphones. The devices require a camera and microphone to support the operation of the emotion engine. The software used is an emotion analysis engine (e.g., OpenFace or Microsoft Azure's Face API).

[2255] Providing a user interface

[2256] The terminal displays an input form to the user, providing an interface for accepting requests for legal document creation and confirmation, along with fields for entering required information (e.g., contract information, contract terms, etc.).

[2257] Emotion Engine Operation

[2258] An emotion engine built into the device analyzes the user's facial expressions and voice in real time to determine their emotional state. The emotion engine then adjusts the interface and the content of the documents provided appropriately based on the user's emotional state. For example, if the user is nervous, it displays a message encouraging them to relax.

[2259] Sending and receiving data

[2260] Encrypts the data entered by the user and sends it to the server using a secure communication protocol (e.g., HTTPS), receives the generated legal document from the server and displays it to the user, and provides interactive feedback to the user to allow them to make corrections, if necessary.

[2261] User Roles

[2262] Document creation request

[2263] The user accurately enters the necessary information into the input form on the terminal and presses the send button to send the data to the server.

[2264] Document review and correction

[2265] The user receives the generated legal document sent from the server, checks its contents in detail, enters corrections as necessary, and presses the send button again to send it to the server. The corrected document is then received again from the server and a final check is made.

[2266] Specific examples

[2267] Example 1: Creating an employment contract

[2268] 1. User Action

[2269] The user selects "Create a new employment contract" on the terminal and enters employer information, employee information, and employment conditions.

[2270] The emotion engine detects tension from the user's facial expressions and voice and displays the message, "Please relax and continue typing."

[2271] 2. Server Processing

[2272] The server receives and analyzes the user's input data and inputs it into the generative AI model.

[2273] Generate an employment contract, convert it to PDF and return it to the device.

[2274] 3. User Actions

[2275] The user reviews the returned contract, enters any necessary corrections, and resubmits it. The emotion engine continues to monitor emotions and displays support messages.

[2276] Example 2: Checking legal notices

[2277] 1. User Action

[2278] The user selects "Review Legal Notice" on the device and enters the notice.

[2279] The emotion engine detects stress and displays, "Don't worry. An expert will check."

[2280] 2. Server Processing

[2281] The server receives the notification content, analyzes it, and inputs it into the generative AI model.

[2282] Legal issues and improvement proposals are extracted and returned to the user.

[2283] 3. User Actions

[2284] The user modifies the notification based on the returned results. The emotion engine continues to monitor emotions and displays support messages as needed.

[2285] Prompt Sentence Examples

[2286] 1. Please create a new employment contract. The employer information is 'Company A', the employee information is 'Mr. B', and the employment conditions are 'Full-time, monthly salary 300,000 yen'.

[2287] 2. What is the problem with this legal notice? The notice reads: 'This is a notice from Company X to Company Y.'

[2288] From the above, this system effectively combines generative AI models and an emotion engine to generate and verify legal documents quickly and accurately, while providing support tailored to the user's emotional state, thereby reducing the stress of legal work and improving the user experience.

[2289] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2290] Step 1:

[2291] Data collection and database generation and updating

[2292] The server collects public legal data (e.g., legal data, precedents, and rules). Inputs include data from internet legal databases and court websites. It stores this data in local storage.

[2293] The server converts the collected data into a unified format, for example, converting data in different formats into XML or JSON.

[2294] The server updates the existing database with new or updated data, checks for duplicates and inconsistencies, and cleans the data.

[2295] The output is a cleaned legal document database in a unified format.

[2296] Step 2:

[2297] Training generative AI models

[2298] The server splits the legal document data in the database into training data and test data. The input is the unified database obtained in the previous step.

[2299] The server initializes a generative AI model (e.g., GPT-3, BERT) and trains it with training data. Specifically, it uses TensorFlow or PyTorch to train the model while feeding it data.

[2300] The server evaluates the trained model on the test data and adjusts the parameters as needed.

[2301] The output is a trained generative AI model capable of generating legal documents with high accuracy.

[2302] Step 3:

[2303] Processing user requests

[2304] The server receives requests from users to create or verify legal documents, and the input includes user input data sent from the terminal via HTTPS.

[2305] The server analyzes the received data and extracts necessary information (e.g., contract information, legal terms). Data analysis uses natural language processing technology. Specifically, it uses a text analysis algorithm.

[2306] The server inputs the extracted information into a generative AI model to generate the necessary legal documents.

[2307] The server converts the generated legal document into an appropriate format (e.g., PDF, Word document) and provides it to the user.

[2308] As an output, a legal document generated based on the user's request is sent to the terminal.

[2309] Step 4:

[2310] Emotion Engine Operation

[2311] The emotion engine built into the device analyzes the user's facial expressions and voice in real time. The inputs include facial expression data and voice data obtained through the camera and microphone.

[2312] The device determines the user's emotional state based on the analysis results of the emotion engine, for example, using facial recognition technology or voice tone analysis.

[2313] The device will adjust the interface and messages provided appropriately depending on the user's emotional state.

[2314] The output is a tailored interface or message that reflects the user's emotional state.

[2315] Step 5:

[2316] User data transmission and feedback

[2317] The user enters the necessary information into the input fields on the terminal and presses the send button. Input includes contract information, legal terms, etc.

[2318] The terminal encrypts the user's input data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[2319] The server transmits the generated legal document to the terminal, where it is received by the user.

[2320] The user checks the generated document and, if necessary, inputs corrections through the terminal interface, which then resends it.

[2321] The device provides interactive feedback, allowing users to make corrections, and the emotion engine continuously monitors the user's emotional state and displays appropriate support messages.

[2322] The output is a final legal document that the user has reviewed and corrected.

[2323] (Application example 2)

[2324] 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."

[2325] Conventional legal document drafting systems do not take into account the user's emotional state, which means they are unable to provide efficient support, especially to users who feel nervous or anxious. Furthermore, the lack of emotion analysis prevents an improved user experience and makes it difficult to reduce stress and anxiety.

[2326] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for creating a legal document using a generative AI model; means for generating the legal document created by the generative AI model based on user input data; means for providing the generated legal document to the user; means for combining an emotion engine that analyzes the user's emotional state; and means for adjusting the interface and the content of the provided document based on the emotion data generated by the emotion engine. This makes it possible to support the creation and review of legal documents individually tailored according to the user's emotional state, thereby improving the user experience and reducing stress and anxiety.

[2327] A "generative artificial intelligence model" is a model that uses machine learning algorithms to create legal documents.

[2328] "Legal documents" refers to legal documents such as contracts, regulations, notices, etc.

[2329] "User input data" refers to information and conditions provided by a user for document creation.

[2330] The "emotion engine" is a system that analyzes the user's facial expressions and voice to determine their emotional state.

[2331] "Emotional Data" refers to information about a user's emotional state generated by an emotion engine.

[2332] "Interface" refers to the screen and operating means that allow a user to interact with a system.

[2333] This invention is a system that combines a generative AI model and an emotion engine. The system is mainly composed of a server and a terminal, and provides emotional support to users when creating and reviewing legal documents. Specific embodiments for implementing this invention are described below.

[2334] Server Roles

[2335] The server performs the following processing.

[2336] 1. Collection and organization of legal data:

[2337] The server periodically collects data on laws, precedents, regulations, etc., creates and updates a database, converts this data into a unified format, and prepares it as training data for generative AI models.

[2338] 2. Training the generative AI model:

[2339] The server categorizes legal document data and uses it to train a generative AI model. After training, the model's performance is evaluated and parameters are adjusted as needed.

[2340] 3. Processing User Requests:

[2341] The server receives a request from a user to create or verify a legal document, analyzes the user's input data, inputs the analyzed data into a generative AI model to generate the required legal document, and then converts the generated legal document into an appropriate format for delivery to the user.

[2342] Terminal role (client side)

[2343] The terminal performs the following processing:

[2344] 1. Providing the user interface:

[2345] The terminal displays an input form to the user, providing an interface for accepting requests for legal document creation and confirmation, and includes fields for entering required information (contractor information, contract terms, etc.).

[2346] 2. How the Emotion Engine works:

[2347] An emotion engine built into the device analyzes the user's facial expressions and voice in real time to determine their emotional state, and then adjusts the interface and the content of the documents presented appropriately based on the user's emotional state.

[2348] 3. Sending and receiving data:

[2349] The device encrypts the data entered by the user and sends it to the server using a secure communication protocol (e.g., HTTPS), and receives the generated legal document from the server and displays it to the user, providing interactive feedback so that the user can make corrections if necessary.

[2350] User Roles

[2351] 1. Document Request:

[2352] The user accurately enters the required information into the input form on the device, and when the input is complete, presses the send button to send the data to the server.

[2353] 2. Document review and correction:

[2354] The user receives the generated legal document returned from the server, checks its contents in detail, enters corrections as necessary, and presses the send button again to send it to the server. The user then receives the corrected document again and makes a final check.

[2355] Specific examples

[2356] Example 1: Creating an employment contract

[2357] The user selects "Create a new employment contract" and enters the employer's name, employee's name, employment conditions, etc. The emotion engine analyzes the user's facial expressions and voice, and if it detects emotions such as tension or anxiety, it makes the interface gentler and displays a message encouraging relaxation. Once the input is complete, the user presses the send button to send the data to the server.

[2358] The server receives the user's input data, analyzes it, inputs the analysis results into the generative AI model to generate an employment contract, and then returns the generated employment contract to the user.

[2359] The user checks the returned employment contract, enters any necessary corrections, and resubmits it. Depending on the situation, the emotion engine will again analyze the user's emotions and provide a support message.

[2360] Example 2: Checking legal notices

[2361] The user selects "Confirm Legal Notice" and enters the notification content. If the emotion engine senses stress or anxiety, it provides a sense of security by displaying a support message or a simple explanation. When a confirmation request is sent, the server receives the notification content and analyzes it using a generative AI model. Legal issues and improvement proposals are extracted and sent back to the user.

[2362] The user can modify the notification based on the received results, and the emotion engine continues to monitor the user's emotional state and provide support messages as needed.

[2363] Prompt Sentence Examples

[2364] "Please create a new employment contract. Please use the following information:

[2365] Employer Name: Tanaka

[2366] Employee name: Suzuki

[2367] Employment conditions: Full-time, annual salary of 6 million yen, two days off per week

[2368] Using this prompt, the generative AI model can quickly and accurately generate the required legal documents.

[2369] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2370] Step 1:

[2371] The terminal displays an input form to the user and accepts requests for legal document creation and confirmation. The user enters the necessary information, such as contract holder information and contract terms. The terminal encrypts the entered data and sends it to the server using a secure communication protocol (e.g., HTTPS). Contract holder information and contract terms are provided as input data, and subsequent document generation is based on this.

[2372] Step 2:

[2373] The server receives and analyzes input data sent by the user. The input data includes the name of the contracting party, contract terms, etc., and the server analyzes this data to extract the necessary information. The analysis results are input into a generative AI model to generate the legal document desired by the user. For example, in response to a request to "create a new employment contract," a corresponding employment contract is generated.

[2374] Step 3:

[2375] The server converts the generated legal document into an appropriate format and returns it to the terminal, including checking and formatting the content of the generated document. The output legal document is provided in a format that is easy for the user to review.

[2376] Step 4:

[2377] The terminal receives the legal document returned from the server and displays it to the user. The user checks the displayed document and makes any necessary corrections. The terminal then sends the information added or corrected by the user back to the server. The user's corrected data is re-entered through the interface, thereby improving the accuracy of the document.

[2378] Step 5:

[2379] An emotion engine built into the device analyzes the user's facial expressions and voice to determine their emotional state in real time. If the emotion engine detects the user's emotional state (e.g., tension or anxiety), the device adjusts the interface and display content. For example, it may display a relaxing message. This allows the user to use the system with peace of mind.

[2380] Step 6:

[2381] The final legal document that the user has reviewed and corrected is then sent from the device to the server, where it is reviewed and formatted again.The final output is a legal document that is accurate and takes into account the user's emotional state and is provided to the user.

[2382] These steps result in a system where a comprehensive legal document creation and review process is achieved by combining generative AI models and an emotion engine.

[2383] 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.

[2384] 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.

[2385] 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.

[2386] 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.

[2387] FIG. 9 is a diagram illustrating 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 actions 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.

[2388] 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.

[2389] 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).

[2390] 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.

[2391] 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."

[2392] 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.

[2393] 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).

[2394] 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.

[2395] 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.

[2396] 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.

[2397] 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.

[2398] 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.

[2399] 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.

[2400] 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.

[2401] 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.

[2402] 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 il...

Claims

1. a means for generating legal documents using a generative artificial intelligence model; means for generating legal documents created by the generative artificial intelligence model based on user input data; means for providing the generated legal document to a user; A system including:

2. 2. The system of claim 1, wherein the artificial intelligence model comprises means for learning data such as laws, precedents, and regulations.

3. 2. The system according to claim 1, further comprising means for providing a user with an input interface for confirming and amending the contents of the generated legal document.

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