system

Abstract

We provide the system. [Solution] A means by which the user inputs input data including the purpose of the regulation, the scope of application, the matters covered, and the specific content of the regulation, A means of sending the input data to the server, The server analyzes the input data and retrieves relevant existing laws and regulations from the database. A natural language generation method in which a server generates drafts of laws and regulations based on search results, A means of displaying the generated draft on the user's terminal, A system that includes this.

Description

Technical Field

[0004] , , , ,

[0005] , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, the rapid and accurate enactment of laws and regulations plays an extremely important role. However, at present, the enactment of laws and regulations is mainly carried out manually by people, and the process is complicated and time-consuming. Furthermore, there is also a problem that the existing laws and regulations have not caught up with the need for new regulations accompanying the progress of technology. Under such circumstances, a system for quickly and accurately generating drafts of laws and regulations according to the purposes and application ranges of various regulations is required.

Means for Solving the Problems

[0005] To solve the above problems, the present invention provides the following system. This system includes means for the user to input input data including the purpose of regulation, scope of application, matters to be covered, and specific regulatory content; means for transmitting the input data to a server; means for the server to analyze the input data and search for relevant existing laws and regulations in a database; means for the server to generate a draft of the law or regulation based on the search results; and means for displaying the generated draft on the user's terminal. Furthermore, this system allows the user to edit the draft of the law or regulation, and by linking with a database of laws and regulations and utilizing appropriate templates to generate the draft, it enables the rapid and accurate drafting of laws and regulations.

[0006] A "user" is an individual or organization that uses the system to input the purpose and content of regulations related to the drafting of laws and ordinances.

[0007] "Input data" refers to information provided by the user to the system, including the purpose of the regulation, its scope, the matters covered, and the specific content of the regulation.

[0008] A "server" is a central processing unit that receives input data, analyzes it, and works with a database to generate drafts of laws and regulations.

[0009] A "database" is a place where data on past laws and regulations is stored and referenced by a server.

[0010] A "natural language generation method" is an algorithm or program in which a server automatically generates drafts of laws and regulations based on user input data and information from a database.

[0011] A "template" is a predefined format or structure used in drafting laws and regulations, into which user input data is embedded.

[0012] A "draft" is an initial version of a legal document generated by the server, which is then edited and reviewed by the user.

[0013] A "terminal" is a computer or device that a user directly operates to send input data or receive generated drafts. [Brief explanation of the drawing]

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

Mode for Carrying Out the Invention

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

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

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

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

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] User input

[0036] Users access the system using their own devices (e.g., personal computers or smartphones). Through a dedicated web form or application, users input necessary information such as the "purpose of the regulation," "scope of application," "subjects covered," and "specific regulatory details." The input is in text field format, allowing users to operate it intuitively.

[0037] Sending input data

[0038] Once input is complete, the terminal sends the entered data to the server. The data is packaged in JSON or XML format and sent using an HTTP POST request.

[0039] Specific example:

[0040] If a user enters data for the purpose of "protecting personal information in financial institutions," that data will be sent to the server.

[0041] {

[0042] "Essential": "Strengthening of personal information protection",

[0043] "scope": "Financial Institutions",

[0044] "subjects": "Management of personal data",

[0045] "regulation_content": "Explicit consent is required for the collection and use of personal data."

[0046] }

[0047] Interaction with the database

[0048] Upon receiving the transmitted data, the server analyzes it and queries existing laws and regulations that relate to the regulations. The server queries the database of legal information and retrieves the relevant data.

[0049] Specific example:

[0050] Search existing laws and regulations concerning "personal information protection" and retrieve relevant articles and regulations.

[0051] Draft generation

[0052] The server generates drafts of laws and regulations using natural language generation (NLG) algorithms based on acquired data and user input data. This is done by dynamically embedding user input data into specific fields using a template engine. The generated drafts meet legally required formats and structures and are provided in a user-friendly format.

[0053] Specific draft generation process:

[0054] Template:

[0055] Chapter 1 General Provisions

[0056] 1. This law aims to {{ purpose}}.

[0057] 2. The scope of application shall be {{ scope}}.

[0058] Chapter 2: Regulations

[0059] 1. Regarding {{subjects}}, {{regulation_content}}.

[0060] Generated draft:

[0061] Chapter 1 General Provisions

[0062] 1. This law aims to strengthen the protection of personal information.

[0063] 2. The scope of application shall be financial institutions.

[0064] Chapter 2: Regulations

[0065] 1. Regarding the management of personal data, explicit consent is required for the collection and use of personal data.

[0066] Viewing and editing results

[0067] The generated draft is sent back to the user's device. The user can review this draft within the web form or application and edit it as needed. Once editing is complete, pressing the save button again will save the edited draft to the server.

[0068] In this way, the system of the present invention quickly and accurately generates drafts of laws and regulations, and provides an environment in which users can easily create and edit legal documents. This enables the digitalization and efficiency of legal infrastructure.

[0069] The following describes the processing flow.

[0070] Step 1:

[0071] Users enter input data, including the purpose, scope, subject matter, and specific content of the regulations, into a dedicated web form or application text field.

[0072] Step 2:

[0073] The terminal packages the input data in JSON or XML format and sends it to the server using an HTTP POST request.

[0074] Step 3:

[0075] The server analyzes the received data and extracts each field (purpose, scope of application, subject matter, and specific regulatory content).

[0076] Step 4:

[0077] Based on the data analyzed by the server, queries are executed against a database of laws and regulations to search for relevant existing laws and ordinances.

[0078] Step 5:

[0079] The server retrieves relevant laws and regulations from the database and matches that data against the appropriate template.

[0080] Step 6:

[0081] The server embeds acquired data and user input data into a template, and uses a natural language generation (NLG) algorithm to generate drafts of laws and regulations.

[0082] Step 7:

[0083] The server sends the generated draft to the user's terminal.

[0084] Step 8:

[0085] The terminal displays the generated draft. Users can review the draft within the web form or application and edit it as needed.

[0086] Step 9:

[0087] Once the user finishes editing and presses the "Save" button, the edited draft is sent back to the server.

[0088] Step 10:

[0089] The server saves the final draft and performs additional processing as needed to prepare it as a legal document.

[0090] (Example 1)

[0091] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0092] Traditionally, drafting rules and standards was a complex, time-consuming, and specialized process. Furthermore, ensuring consistency with existing laws and regulations required significant resources, and manual methods were prone to errors. Additionally, there were limited means to effectively utilize user-generated data and quickly generate legal documents. Consequently, meeting the high efficiency and accuracy demands of modern times proved difficult.

[0093] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0094] In this invention, the server includes means for the user to input input data including the purpose of the regulation, scope of application, matters covered, and specific content of the regulation; means for transmitting the input data to the server; means for the server to analyze the input data and search a database for relevant existing rules and standards data; means for the server to generate a draft of the rules and standards based on the search results; means for displaying the generated draft on the user's terminal; means for the user to review the generated draft and edit it within a web form or application; and means for transmitting the edited draft back to the server and saving it in the database. This enables the rapid and accurate generation of draft rules and standards based on data intuitively entered by the user, and allows for the creation of high-quality legal documents through repeated editing.

[0095] A "user" refers to a person who accesses the system and enters the purpose of the regulation, its scope, the matters covered, and the specific content of the regulation.

[0096] "Input data" refers to information that users input into the system, such as the purpose, scope, subject matter, and specific content of the regulations.

[0097] A "terminal" is a device used by a user to access a system, and includes personal computers, smartphones, and other similar devices.

[0098] A "server" refers to a computer system that receives input data sent from a user's terminal, analyzes it, and generates draft rules and standards in conjunction with a database.

[0099] A "database" is a collection of information that stores existing data on rules and standards, making it searchable by a server using queries.

[0100] "Natural language generation means" refers to algorithms and software for automatically generating draft rules and standards based on input data and information obtained from databases.

[0101] A "draft" refers to an initial version of a legal document containing generated rules or standards, provided in a format that allows users to review and edit it.

[0102] "Display means" refers to the functions and interfaces for displaying the generated draft on the user's terminal.

[0103] "Editing means" refers to the functions and interfaces that allow users to review the generated draft and make corrections and edits as needed.

[0104] "Storage method" refers to the function that sends the user-edited draft back to the server and saves it in the database.

[0105] For this invention to be implemented, it is crucial that the user, terminal, and server each fulfill their respective roles. The following describes how this system will be implemented in detail.

[0106] First, the user accesses a dedicated web form or application using a device (e.g., a PC or smartphone). Using this interface, the user enters necessary information such as the "purpose of the regulation," "scope of application," "matters covered," and "specific regulatory content" in text fields. The input process is designed to be intuitive for the user.

[0107] Once the user enters the necessary information, the terminal packages it in JSON or XML format and sends it to the server using an HTTP POST request. For example, if the following input is given:

[0108] Purpose of regulation: To enhance the protection of personal information.

[0109] Scope of application: Financial institutions

[0110] Topics covered: Management of personal data

[0111] Specific regulations: Explicit consent is required for the collection and use of personal data.

[0112] The server receives the transmitted data in JSON or XML format and parses it. It then queries the rules and standards database to find relevant existing laws and standards. The data obtained as a result of the search is combined with the information entered by the user.

[0113] Next, the server uses a natural language generation (NLG) algorithm to generate drafts of laws and standards based on the acquired data and user input data. This draft generation uses a template engine, dynamically filling in user input data into specific fields. For example, the following template is used:

[0114] Chapter 1 General Provisions

[0115] 1. This law aims to {{ purpose}}.

[0116] 2. The scope of application shall be {{ scope}}.

[0117] Chapter 2: Regulations

[0118] 1. Regarding {{subjects}}, {{regulation_content}}.

[0119] The draft generated based on this will look like this:

[0120] Chapter 1 General Provisions

[0121] 1. This law aims to strengthen the protection of personal information.

[0122] 2. The scope of application shall be financial institutions.

[0123] Chapter 2: Regulations

[0124] 1. Regarding the management of personal data, explicit consent is required for the collection and use of personal data.

[0125] The generated draft is resent from the server to the user's device. The user can review this draft within a web form or application and edit it as needed. Once editing is complete, the user presses the save button, and the edited draft is sent back to the server and saved to the database.

[0126] Through this procedure, this invention can quickly and accurately generate drafts of rules and standards, and provide an environment in which users can easily create and edit legal documents. This leads to increased efficiency and digitalization of legal work.

[0127] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0128] Step 1: User Input

[0129] Users access the system using a terminal. They enter the "purpose of the regulation," "scope of application," "subjects covered," and "specific regulatory details" in text fields via a dedicated web form or application. For example:

[0130] Purpose of regulation: To enhance the protection of personal information.

[0131] Scope of application: Financial institutions

[0132] Topics covered: Management of personal data

[0133] Specific regulations: Explicit consent is required for the collection and use of personal data.

[0134] Once the user has finished entering information into all the fields, they click the "Submit" button in the form.

[0135] Step 2: Submit the input data

[0136] The terminal converts the data entered by the user into JSON or XML format and sends it to the server using an HTTP POST request. For example, the following JSON data is generated:

[0137] {

[0138] "Essential": "Strengthening of personal information protection",

[0139] "scope": "Financial Institutions",

[0140] "subjects": "Management of personal data",

[0141] "regulation_content": "Explicit consent is required for the collection and use of personal data."

[0142] }

[0143] The device performs the specific action of sending this JSON data to the server.

[0144] Step 3: Analyze the input data

[0145] The server receives the transmitted JSON data and performs parsing. Here, it reads each field and converts it into a data structure. For example, the data corresponding to the "purpose" field is interpreted as "Enhancing personal information protection." Based on this parsing result, the server constructs its internal data structure.

[0146] Step 4: Execute database query

[0147] The server queries a database of rules and standards based on the analyzed data. For example, it generates a query to search for existing regulations on "personal data protection" and sends it to the database. The database returns data containing the relevant existing regulations. Specifically, this involves generating and executing SQL queries.

[0148] Step 5: Draft Generation

[0149] The server combines existing legal data retrieved from the database with user input data and generates a draft using a natural language generation (NLG) algorithm. A template engine is used to dynamically populate specific fields with user data. This process utilizes an AI model to generate the appropriate document structure. For example, the following template is used:

[0150] Chapter 1 General Provisions

[0151] 1. This law aims to {{ purpose}}.

[0152] 2. The scope of application shall be {{ scope}}.

[0153] Chapter 2: Regulations

[0154] 1. Regarding {{subjects}}, {{regulation_content}}.

[0155] The draft generated based on this would look like this:

[0156] Chapter 1 General Provisions

[0157] 1. This law aims to strengthen the protection of personal information.

[0158] 2. The scope of application shall be financial institutions.

[0159] Chapter 2: Regulations

[0160] 1. Regarding the management of personal data, explicit consent is required for the collection and use of personal data.

[0161] Step 6: Submit the generated draft

[0162] The generated draft is sent again from the server to the user's terminal. The server packages this draft as an HTTP response and sends it to the terminal. This allows the user to review the generated draft.

[0163] Step 7: Editing the draft

[0164] Users review the generated draft on their device and edit it as needed. This editing process takes place within a web form or application, allowing users to modify the draft's content. After making changes, the user clicks the "Save" button.

[0165] Step 8: Save the edited draft

[0166] When the user clicks the save button, the device resends the edited draft to the server. The server receives the submitted edited draft and saves it to the database again. In this specific operation, the JSON data is parsed and the appropriate database entry is updated.

[0167] This processing flow allows the system to efficiently and accurately generate draft rules and standards, providing an environment where users can easily edit them.

[0168] (Application Example 1)

[0169] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0170] In today's digital society, users need to create and edit legal documents and product pages quickly and accurately. However, creating these documents requires specialized knowledge, and understanding and editing them is time-consuming. An efficient system is needed to solve this problem.

[0171] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0172] In this invention, the server includes means for the user to input input data including the purpose, scope, subject matter, and specific content of the regulation; means for transmitting the input data to the server; means for the server to analyze the input data and search a database for relevant existing laws and regulations; means for the server to generate a draft of the law or regulation based on the search results; means for displaying the generated draft on the user's terminal; means for the user to input product name, price, description, and ingredient information; means for transmitting the input product data to the server; means for the server to search a database for relevant product information based on the product data; and means for the server to display the generated product page draft on the user's terminal. This makes it possible to easily generate not only drafts of laws and regulations but also product pages.

[0173] A "user" is an end-user who accesses and operates a system or application.

[0174] The "purpose of regulation" refers to the specific goals and directions that the established regulation should achieve.

[0175] "Scope of application" refers to the specific region, organization, or situation to which the regulation or law applies.

[0176] "Subject matter" refers to specific matters, activities, or areas that are subject to regulation.

[0177] "Specific regulations" refer to actions, conditions, or prohibitions specifically defined in regulations or laws.

[0178] "Input data" refers to data that includes information and content provided by the user to the system.

[0179] "Means" refers to the methods, tools, and processes used to achieve a specific purpose or function.

[0180] A "server" refers to a central system that receives requests from users and processes and provides data.

[0181] "Natural language generation" refers to the technology that allows computers to generate documents and texts in human language.

[0182] "Product name" refers to the name or label used to identify a specific product.

[0183] "Price" refers to the amount or cost set for a product or service.

[0184] A "description" refers to text that describes detailed information and features about a product or service.

[0185] "Ingredient information" refers to a list of materials and ingredients contained in a particular product, especially food and cosmetics.

[0186] A "database" refers to a computer system for efficiently storing, searching, and managing structured information.

[0187] A "product page draft" refers to the initial version or first draft of a page that allows users to view information about a product.

[0188] The system for implementing this invention is one in which a user inputs data using their own terminal, and a server analyzes and processes that data to generate and display drafts of laws and regulations and draft product pages.

[0189] Specifically, users use their devices to input data such as "purpose of regulation," "scope of application," "subjects covered," and "specific regulatory content" via web forms or applications. Similarly, users input data including "product name," "price," "description," and "ingredient information" to generate product pages. This input data is entered through a text-field interface designed for intuitive user interaction.

[0190] The input data is structured in JSON or XML format and sent to the server using an HTTP POST request. The server parses the received data and searches the legal database for relevant laws and regulations. Similarly, for product data, the server searches the database for relevant product information after receiving the data.

[0191] The server generates drafts using natural language generation (NLG) algorithms based on search results. Leveraging a template engine, it dynamically embeds user input data into specific fields, ensuring the generated drafts meet the format and structure requirements of legal or product pages. Not only are legal and regulatory drafts generated, but product page drafts are also templated and provided in a format easily understandable to users.

[0192] The generated draft is sent back to the user's device, where they can review and edit it within a web form or application. Once editing is complete, pressing the save button again saves the edited draft to the server. This system provides an environment for the rapid and accurate creation and editing of legal / regulation drafts and product page drafts.

[0193] Hardware and software to use

[0194] Hardware: User terminals (e.g., personal computers, smartphones), servers

[0195] Software: Flask (web framework), Jinja2 (template engine), requests (HTTP request library)

[0196] Specific example

[0197] User input:

[0198] Purpose of regulation: To enhance the protection of personal information.

[0199] Scope of application: Financial institutions

[0200] Topics covered: Management of personal data

[0201] Specific regulations: Explicit consent is required for the collection and use of personal data.

[0202] Product Name: Premium Black Tea

[0203] Price: 1200

[0204] Description: This is a fragrant black tea made with high-quality tea leaves.

[0205] Ingredient information: black tea leaves, fragrance

[0206] Example of a prompt

[0207] Input prompt: "Please enter the purpose, scope, subject matter, and specific details of the regulation. Also, please enter the product name, price, description, and ingredient information."

[0208] Prompt message for generated draft: "The following draft laws / regulations and product page drafts have been generated. You can edit the content."

[0209] This system allows users to easily create legal documents and product pages without requiring specialized knowledge.

[0210] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0211] Step 1:

[0212] Users input the purpose, scope, and subject matter of the regulation, as well as specific regulatory details, or product name, price, description, and ingredient information through their user terminal. The input data is structured in text field format and is entered via an intuitive web form or application.

[0213] Step 2:

[0214] The user terminal packages the entered data in JSON or XML format and sends it to the server using an HTTP POST request. In this case, the user's input data is sent to the server as the body of the HTTP request.

[0215] Step 3:

[0216] The server analyzes the received data. Specifically, it extracts data about regulations and products entered by the user and stores it as structured information. For example, input data about regulations is analyzed as follows:

[0217] {

[0218] "Essential": "Strengthening of personal information protection",

[0219] "scope": "Financial Institutions",

[0220] "subjects": "Management of personal data",

[0221] "regulation_content": "Explicit consent is required for the collection and use of personal data."

[0222] }

[0223] Product data is analyzed in the same manner.

[0224] {

[0225] "product_name": "Premium Black Tea",

[0226] "price": 1200,

[0227] "Description": "A fragrant black tea made with high-quality tea leaves."

[0228] "ingredients": "Tea leaves, fragrances"

[0229] }

[0230] Step 4:

[0231] Based on the analyzed data, the server searches the database for relevant legal data or product information. For example, it searches and queries existing laws and regulations related to "personal information protection" or existing product information related to "tea." The relevant legal data is retrieved as follows:

[0232] [

[0233] {

[0234] "law": "Personal Information Protection Law",

[0235] "clause": "Article 12",

[0236] "Description": "Explicit consent is required for the collection of personal data."

[0237] }

[0238] ]

[0239] Product information is retrieved in the same manner.

[0240] [

[0241] {

[0242] "name": "Tea A",

[0243] "price": 1000,

[0244] "description": "Fragrant Black Tea A"

[0245] }

[0246] ]

[0247] Step 5:

[0248] The server uses a natural language generation (NLG) algorithm based on the acquired data to generate drafts of laws, regulations, or product pages. The generated drafts utilize a template engine (e.g., Jinja2). User input data is dynamically embedded into the template and formatted as a draft. The generated draft will look like this:

[0249] Chapter 1 General Provisions

[0250] 1. This law aims to strengthen the protection of personal information.

[0251] 2. The scope of application shall be financial institutions.

[0252] Chapter 2: Regulations

[0253] 1. Regarding the management of personal data, explicit consent is required for the collection and use of personal data.

[0254] Product Name: Premium Black Tea

[0255] Price: ¥1200

[0256] Description: This is a fragrant black tea made with high-quality tea leaves.

[0257] Ingredient information: black tea leaves, fragrance

[0258] Related products:

[0259] Black Tea A - ¥1000: Aromatic Black Tea A

[0260] Step 6:

[0261] The generated draft is sent back to the user's terminal in JSON or HTML format. The user's terminal displays the received draft in the user interface, allowing the user to review and edit it. The user edits the draft and sends it back to the server by pressing the save button.

[0262] Step 7:

[0263] The draft edited by the user is sent to the server and ultimately stored in the database. This allows users to efficiently generate and edit high-quality drafts of laws and regulations, as well as product pages.

[0264] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0265] User input and emotion recognition

[0266] Users access the system using a device (e.g., a personal computer or smartphone). Through a dedicated web form or application, users input necessary information such as the "purpose of the regulation," "scope of application," "subjects covered," and "specific regulatory content." Simultaneously, an emotion engine analyzes the user's emotions (e.g., joy, anger, sadness) in real time. The emotion engine identifies emotions based on the user's facial recognition and text analysis.

[0267] Sending input data and emotion data

[0268] Once input is complete, the device sends the entered data, along with sentiment data, to the server. The data is packaged in JSON or XML format and sent using an HTTP POST request.

[0269] Specific example:

[0270] When a user enters data for the purpose of "protecting personal information in financial institutions," that data is sent to the server in the following format.

[0271] {

[0272] "Essential": "Strengthening of personal information protection",

[0273] "scope": "Financial Institutions",

[0274] "subjects": "Management of personal data",

[0275] "regulation_content": "Explicit consent is required for the collection and use of personal data",

[0276] "emotion": "frustration" / / Example of emotion

[0277] }

[0278] Interaction with the database and consideration of emotions

[0279] Upon receiving the transmitted data, the server analyzes it and extracts each field (purpose, scope, subject matter, specific regulations, and sentiment information). Based on the analyzed data, the server queries a database of laws and regulations, seeking relevant existing laws and ordinances.

[0280] Specific example:

[0281] Search for existing laws and regulations regarding "personal information protection" and obtain relevant articles and regulatory content.

[0282] Draft generation and emotion response

[0283] The server embeds the acquired data and the user's input data into a template and uses a natural language generation (NLG) algorithm to generate drafts of laws and regulations. At this time, based on the emotion data from the emotion engine, the word selection and expression of the draft are adjusted. For example, when the user has an emotion of "dissatisfaction", by making the expression of the draft more specific and clear, the user's intention is maximally reflected.

[0284] Specific draft generation process:

[0285] Template:

[0286] Chapter 1 General Provisions

[0287] 1. This law aims to {{ purpose}}.

[0288] 2. The scope of application shall be {{ scope}}.

[0289] Chapter 2 Regulatory Content

[0290] 1. Regarding {{ subjects}}, {{ regulation_content}}.

[0291] Generated draft:

[0292] Chapter 1 General Provisions

[0293] 1. This law aims to strengthen the protection of personal information.

[0294] 2. The scope of application shall be financial institutions.

[0295] Chapter 2: Regulations

[0296] 1. Regarding the management of personal data, explicit consent is required for the collection and use of personal data. In particular, the user's wishes must not be disregarded.

[0297] Viewing and editing results

[0298] The generated draft is sent back to the user's device. The user can review this draft within the web form or application and edit it as needed. Once editing is complete, pressing the save button again will save the edited draft to the server.

[0299] In this way, the system of the present invention provides an environment in which drafts of laws and regulations can be generated quickly and accurately, and which can create and edit legal documents with higher precision by taking into account the user's feelings. This enables the digitalization and efficiency of legal infrastructure.

[0300] The following describes the processing flow.

[0301] Step 1:

[0302] The user enters the purpose, scope, target items, and specific details of the regulation into a dedicated web form or application text field. Simultaneously, an emotion engine monitors the user's facial expressions and typing speed, analyzing their emotions in real time.

[0303] Step 2:

[0304] The terminal packages the emotion data obtained from the emotion engine, along with the entered restriction information, in JSON or XML format and sends it to the server using an HTTP POST request.

[0305] Step 3:

[0306] The server analyzes the received regulatory content data and sentiment data, and extracts each field (purpose, scope of application, matters to be targeted, specific regulatory content, and sentiment information).

[0307] Step 4:

[0308] Based on the data analyzed by the server, a query is executed against the database related to laws and regulations to search for relevant existing law and regulation data. For example, extract past laws and regulations related to "personal information protection".

[0309] Step 5:

[0310] The server collates the relevant law and regulation data obtained from the database with the template, and further generates a draft considering the sentiment information. By using the sentiment engine, when the user expresses "dissatisfaction", more specific expressions and additional explanations are incorporated.

[0311] Step 6:

[0312] The server sends the generated draft to the user's terminal. The draft reflects the adjustments based on the sentiment data.

[0313] Specific example:

[0314] Template:

[0315] Chapter 1 General Provisions

[0316] 1. This law aims to {{ purpose}}

[0317] 2. The scope of application shall be {{ scope}}

[0318] Chapter 2 Regulatory Content

[0319] 1. Regarding {{ subjects}}, {{ regulation_content}}

[0320] Adjustments during draft generation:

[0321] Chapter 1 General Provisions

[0322] 1. This law aims to strengthen the protection of personal information.

[0323] 2. The scope of application shall be financial institutions.

[0324] Chapter 2: Regulations

[0325] 1. Regarding the management of personal data, explicit consent is required for the collection and use of personal data. In particular, the user's wishes must not be disregarded.

[0326] Step 7:

[0327] The terminal displays the generated draft. Users can review the draft within the web form or application and edit it as needed.

[0328] Step 8:

[0329] The user finishes editing the draft and presses the "Save" button to resubmit the edited draft to the server.

[0330] Step 9:

[0331] The server saves the final draft and performs any additional processing necessary for further legal refinement.

[0332] (Example 2)

[0333] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0334] Conventional systems for generating drafts of laws and regulations generate them based on user input data, but their accuracy and suitability are limited. Furthermore, because document generation does not take user emotions into consideration, it is difficult to create legal documents that fully reflect the user's intentions and feelings. As a result, users are often dissatisfied when generating drafts of laws and regulations, leading to a decrease in work efficiency.

[0335] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user to input input data including the purpose of regulation, scope of application, matters to be covered, and specific content of regulation; means for transmitting the input data and the user's sentiment data to the server; means for the server to analyze the input data and sentiment data and search for relevant existing laws and regulations data from a database; natural language generation means for the server to generate a draft of the law or regulation based on the search results and the user's sentiment data; and means for displaying the generated draft on the user's terminal. This makes it possible to generate an appropriate draft of the law or regulation that reflects the user's sentiment.

[0336] A "user" refers to an individual or legal entity that uses the system to generate drafts of laws and regulations.

[0337] "Input data" refers to data entered by the user that includes the purpose, scope, subject matter, and specific content of the regulations.

[0338] "Emotional data" refers to information about the user's emotions (e.g., joy, anger, sadness, etc.) detected during the user's input process.

[0339] A "server" refers to a computer system that receives and analyzes data sent by users and generates drafts of laws and regulations.

[0340] "Natural language generation means" refers to a function or algorithm that automatically generates drafts of laws and regulations using natural language based on input data.

[0341] A "template" refers to a framework containing standard phrases used to draft laws and regulations.

[0342] A "database" refers to an information storage system that stores and makes searchable information related to laws and regulations.

[0343] A "terminal" refers to an electronic device (such as a personal computer or smartphone) that a user uses to access a system, input data, or view and edit generated drafts.

[0344] A "draft" refers to a draft document of a law or ordinance that is automatically generated by a generative AI model.

[0345] "Means of input" refers to the interface or method by which a user supplies input data to the system.

[0346] "Means of transmission" refers to the means of communication used to send input data and emotional data from a terminal to a server.

[0347] "Means of analysis" refers to algorithms and processes used by a server to analyze received data and extract necessary information.

[0348] "Means of display" refers to the interface or method for displaying the generated draft on the user's device.

[0349] The system of this invention is for the rapid and accurate generation of drafts of laws and regulations, and includes various means for user input, sentiment recognition, data analysis, draft generation, display, and editing. Specific embodiments of the system are described below.

[0350] Users access the system using devices such as personal computers or smartphones. They input necessary information, including the "purpose of the regulation," "scope of application," "subjects covered," and "specific regulatory content," via a dedicated web form or application. During this input process, the emotion engine analyzes the user's emotions in real time. The emotion engine is a software module that identifies emotions through facial recognition and text analysis.

[0351] Once input is complete, the terminal sends the entered data and sentiment data to the server. The data is packaged in JSON or XML format and sent using an HTTP POST request. The server parses the received data and extracts each field (purpose, scope, subject matter, specific regulations, and sentiment information). Based on the parsed data, the server queries a database of laws and regulations to find relevant existing laws and regulations.

[0352] The server embeds acquired data and user input data into a template and uses a generative AI model (e.g., GPT-3®) to generate drafts of laws and regulations. At this time, the wording and expression of the draft are adjusted based on sentiment data from the sentiment engine. For example, if the user has the emotion of "dissatisfaction," the wording of the draft is made more specific and detailed.

[0353] The generated draft is resent from the server to the user's device. Users can review the draft through a web form or application and edit it as needed. Once editing is complete, pressing the save button again will save the edited draft to the server.

[0354] The following are examples of specific prompt messages.

[0355] "(Input data): Purpose: Strengthening personal information protection; Scope of application: Financial institutions; Target: Management of personal data; Specific regulations: Clear consent is required for the collection and use of personal data; (Emotion): Dissatisfaction"

[0356] "(Input data): Purpose: Promotion of environmental protection; Scope of application: All companies; Target: Waste management; Specific regulations: Proper disposal of waste; (Emotion) Joy"

[0357] This system enables the generation of appropriate draft laws and regulations that reflect user sentiment, providing more accurate legal documents. Furthermore, it allows for smooth editing of documents to suit user intent, contributing to the digitalization and efficiency of legal procedures.

[0358] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0359] Step 1:

[0360] Users access the system using devices such as personal computers or smartphones. Through a dedicated web form or application, users input information such as the "purpose of the regulation," "scope of application," "subjects covered," and "specific regulatory content." During this input process, an emotion engine performs facial recognition of the user and identifies the user's emotions through text analysis. This process generates user input data and emotion data, which are then passed on to the next step.

[0361] Step 2:

[0362] The terminal packages the input data and sentiment data into JSON or XML format and sends it to the server using an HTTP POST request. Specifically, it sets values ​​for each field (purpose, scope, target items, specific regulations, and sentiment information) in a JSON object and includes it in the body of the HTTP request. As a result, the server receives this data.

[0363] Step 3:

[0364] The server analyzes the received data. The data analysis module parses the data in JSON format and extracts each field (purpose, scope, subject matter, specific regulations, and sentiment information). Based on this, the server prepares the data to send queries to databases related to laws and regulations in the next step.

[0365] Step 4:

[0366] Based on the analysis results, the server executes queries against a database of laws and regulations. Specifically, a query generation module executes the generated queries against the legal database to search for existing laws and regulations related to fields such as "personal information protection" and "financial institutions." As a result of this query execution, the relevant legal data is returned to the server.

[0367] Step 5:

[0368] The server embeds acquired data and user input data into a template and uses a generative AI model (e.g., GPT-3) to generate drafts of laws and regulations. This process adjusts the wording and phrasing of the draft based on sentiment data acquired from an emotion engine. The generative AI model references the template and forms the draft document based on user-provided data and sentiment data. As a result, a completed draft is generated.

[0369] Step 6:

[0370] The server sends the generated draft to the terminal in JSON or plain text format. The terminal displays the received draft within the application. Users can review the draft through web forms or applications and edit it as needed. The user's edits are sent back to the server for saving. This process ensures that the legal document best reflects the user's intent.

[0371] (Application Example 2)

[0372] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0373] Conventional regulatory draft generation systems often failed to adequately reflect user intentions because they did not consider user sentiment. Furthermore, the generated drafts were often formal and did not address user dissatisfaction in terms of specific content or expression. This resulted in low user satisfaction and a need for improved quality in the generated drafts.

[0374] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0375] In this invention, the server includes means for analyzing user input data and sentiment data, means for searching a database for relevant existing regulatory data, and means for generating a draft of regulations based on the search results and sentiment data. This makes it possible to generate an appropriate draft of regulations that takes user sentiment into consideration.

[0376] A "user" is an entity that utilizes the system, and is a person or group that inputs data for a specific purpose.

[0377] A "regulation" is a law, ordinance, or rule enacted to achieve a specific purpose.

[0378] "Input data" refers to information provided by the user to the system, including the purpose of the regulation, its scope, the matters covered, and the specific content of the regulation.

[0379] "Emotional data" refers to data that indicates the user's emotions at the time of input, and is extracted by analyzing emotional states such as joy, anger, and sadness.

[0380] A "server" is a computing system that receives data sent by users, analyzes it, and generates draft regulations.

[0381] A "database" is an information aggregation system that stores relevant existing regulatory data and allows servers to perform searches.

[0382] "Searching" is the act of a server retrieving information related to user input data and sentiment data from a database.

[0383] "Natural language generation" refers to algorithms or techniques for generating draft regulations based on analyzed data.

[0384] A "template" is a standard document format used in the generation of draft regulations.

[0385] The system for implementing this invention first provides a means for the user to input data including the purpose of the regulation, its scope, the matters covered, and the specific content of the regulation. The user accesses a dedicated web form or application using a terminal such as a smartphone or personal computer and inputs the data. At the same time, the user's sentiment data is also collected. The sentiment data is collected by the smartphone's camera and microphone and analyzed in real time. Software such as OpenCV or TENSORFLOW® is used for this sentiment analysis.

[0386] Input data and sentiment data are packaged in JSON or XML format and sent to the server using the HTTPS protocol. The transmitted data is received and analyzed on the server. The server extracts the "purpose of the regulation," "scope of application," "targeted matters," "specific regulatory content," and "sentiment data" from the analyzed data.

[0387] Based on the extracted data, the server interacts with the database to search for relevant existing regulatory data. Based on this search, the server embeds the retrieved data into a template and generates a draft of the regulation using a natural language generation (NLG) algorithm. At this time, the wording of the draft is adjusted to align with the user's intentions based on sentiment data. For example, if the user has the emotion of "dissatisfaction," the wording of the draft is made more specific and clear to best reflect the user's intentions.

[0388] The generated draft is sent back to the user's device, where the user can review and edit it. The editing function is designed to allow the user to modify the draft content as needed and save it again to the server.

[0389] As a specific example, if a user enters information with the purpose of "protecting personal information in financial institutions," the following prompt message will be generated.

[0390] The user entered the data targeting financial institutions with the aim of "strengthening personal information protection." The sentiment data indicates "dissatisfaction."

[0391] Input data:

[0392] Objective: To strengthen the protection of personal information.

[0393] Scope of application: Financial institutions

[0394] Topics covered: Management of personal data

[0395] Regulations: Explicit consent is required for the collection and use of personal data.

[0396] User sentiment: Dissatisfaction

[0397] Prompt message:

[0398] "The user feels frustration. Draft an appropriate response for strengthening personal data protection within financial institutions."

[0399] This allows the system to quickly and accurately generate regulatory drafts that reflect user intentions and sentiments, providing users with more satisfying results.

[0400] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0401] Step 1:

[0402] Users access a dedicated web form or application and enter the purpose, scope, subject matter, and specific details of the regulation. The entered data is saved on the device in text format, and sentiment data is collected in real time using the camera and microphone. Sentiment data is extracted through image analysis and voice analysis.

[0403] Input: User input data (text format), user's facial image, and audio.

[0404] Output: Input data and sentiment data

[0405] Step 2:

[0406] The device packages input data and sentiment data in JSON format and sends it to the server using the HTTPS protocol. Encryption is applied to enhance security during transmission.

[0407] Input: Input data, sentiment data

[0408] Output: Data package in JSON format

[0409] Step 3:

[0410] The server receives the transmitted data in JSON format and performs analysis. The analysis extracts the "purpose of the regulation," "scope of application," "targeted matters," "specific regulatory details," and "sentiment data" from the input data. This analysis is performed to facilitate database searching.

[0411] Input: Data package in JSON format

[0412] Output: Extracted input data and sentiment data

[0413] Step 4:

[0414] Based on the data extracted by the server, relevant existing regulatory data is searched for in the database. SQL queries are used for the search to identify database entries related to the regulations.

[0415] Input: Extracted input data and sentiment data

[0416] Output: Search results (existing regulatory data)

[0417] Step 5:

[0418] The server uses search results and sentiment data to apply a natural language generation (NLG) algorithm to generate a draft of the regulations. During this process, the draft text is refined based on sentiment data, selecting expressions that align with user preferences.

[0419] Input: Search results (existing regulatory data), sentiment data

[0420] Output: Generated regulatory draft

[0421] Step 6:

[0422] The server packages the generated draft back into JSON format and sends it to the user's terminal. An editing function for the draft is also provided at this time.

[0423] Input: Generated regulatory draft

[0424] Output: Draft data in JSON format

[0425] Step 7:

[0426] The user's device displays the received draft and makes it editable. The user reviews the draft and makes corrections to complete the final regulations. The edits are then saved back to the server.

[0427] Input: Draft data in JSON format

[0428] Output: Edited regulatory draft

[0429] Step 8:

[0430] Once the user finishes editing and presses the save button, the device sends the edited data back to the server. The server then saves it to the database as the final regulatory draft.

[0431] Input: Edited regulatory draft

[0432] Output: Saved final regulatory draft

[0433] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0434] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0435] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0436] [Second Embodiment]

[0437] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0438] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0439] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0440] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0441] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0442] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0443] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0444] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0445] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0446] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0447] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0448] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0449] User input

[0450] Users access the system using their own devices (e.g., personal computers or smartphones). Through a dedicated web form or application, users input necessary information such as the "purpose of the regulation," "scope of application," "subjects covered," and "specific regulatory details." The input is in text field format, allowing users to operate it intuitively.

[0451] Sending input data

[0452] Once input is complete, the terminal sends the entered data to the server. The data is packaged in JSON or XML format and sent using an HTTP POST request.

[0453] Specific example:

[0454] If a user enters data for the purpose of "protecting personal information in financial institutions," that data will be sent to the server.

[0455] {

[0456] "Essential": "Strengthening of personal information protection",

[0457] "scope": "Financial Institutions",

[0458] "subjects": "Management of personal data",

[0459] "regulation_content": "Explicit consent is required for the collection and use of personal data."

[0460] }

[0461] Interaction with the database

[0462] Upon receiving the transmitted data, the server analyzes it and queries existing laws and regulations that relate to the regulations. The server queries the database of legal information and retrieves the relevant data.

[0463] Specific example:

[0464] Search existing laws and regulations concerning "personal information protection" and retrieve relevant articles and regulations.

[0465] Draft generation

[0466] The server generates drafts of laws and regulations using natural language generation (NLG) algorithms based on acquired data and user input data. This is done by dynamically embedding user input data into specific fields using a template engine. The generated drafts meet legally required formats and structures and are provided in a user-friendly format.

[0467] Specific draft generation process:

[0468] Template:

[0469] Chapter 1 General Provisions

[0470] 1. This law aims to {{ purpose}}.

[0471] 2. The scope of application shall be {{ scope}}.

[0472] Chapter 2: Regulations

[0473] 1. Regarding {{subjects}}, {{regulation_content}}.

[0474] Generated draft:

[0475] Chapter 1 General Provisions

[0476] 1. This law aims to strengthen the protection of personal information.

[0477] 2. The scope of application shall be financial institutions.

[0478] Chapter 2: Regulations

[0479] 1. Regarding the management of personal data, explicit consent is required for the collection and use of personal data.

[0480] Viewing and editing results

[0481] The generated draft is sent back to the user's device. The user can review this draft within the web form or application and edit it as needed. Once editing is complete, pressing the save button again will save the edited draft to the server.

[0482] In this way, the system of the present invention quickly and accurately generates drafts of laws and regulations, and provides an environment in which users can easily create and edit legal documents. This enables the digitalization and efficiency of legal infrastructure.

[0483] The following describes the processing flow.

[0484] Step 1:

[0485] Users enter input data, including the purpose, scope, subject matter, and specific content of the regulations, into a dedicated web form or application text field.

[0486] Step 2:

[0487] The terminal packages the input data in JSON or XML format and sends it to the server using an HTTP POST request.

[0488] Step 3:

[0489] The server analyzes the received data and extracts each field (purpose, scope of application, subject matter, and specific regulatory content).

[0490] Step 4:

[0491] Based on the data analyzed by the server, queries are executed against a database of laws and regulations to search for relevant existing laws and ordinances.

[0492] Step 5:

[0493] The server retrieves relevant laws and regulations from the database and matches that data against the appropriate template.

[0494] Step 6:

[0495] The server embeds acquired data and user input data into a template, and uses a natural language generation (NLG) algorithm to generate drafts of laws and regulations.

[0496] Step 7:

[0497] The server sends the generated draft to the user's terminal.

[0498] Step 8:

[0499] The terminal displays the generated draft. Users can review the draft within the web form or application and edit it as needed.

[0500] Step 9:

[0501] Once the user finishes editing and presses the "Save" button, the edited draft is sent back to the server.

[0502] Step 10:

[0503] The server saves the final draft and performs additional processing as needed to prepare it as a legal document.

[0504] (Example 1)

[0505] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0506] Traditionally, drafting rules and standards was a complex, time-consuming, and specialized process. Furthermore, ensuring consistency with existing laws and regulations required significant resources, and manual methods were prone to errors. Additionally, there were limited means to effectively utilize user-generated data and quickly generate legal documents. Consequently, meeting the high efficiency and accuracy demands of modern times proved difficult.

[0507] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0508] In this invention, the server includes means for the user to input input data including the purpose of the regulation, scope of application, matters covered, and specific content of the regulation; means for transmitting the input data to the server; means for the server to analyze the input data and search a database for relevant existing rules and standards data; means for the server to generate a draft of the rules and standards based on the search results; means for displaying the generated draft on the user's terminal; means for the user to review the generated draft and edit it within a web form or application; and means for transmitting the edited draft back to the server and saving it in the database. This enables the rapid and accurate generation of draft rules and standards based on data intuitively entered by the user, and allows for the creation of high-quality legal documents through repeated editing.

[0509] A "user" refers to a person who accesses the system and enters the purpose of the regulation, its scope, the matters covered, and the specific content of the regulation.

[0510] "Input data" refers to information that users input into the system, such as the purpose, scope, subject matter, and specific content of the regulations.

[0511] A "terminal" is a device used by a user to access a system, and includes personal computers, smartphones, and other similar devices.

[0512] A "server" refers to a computer system that receives input data sent from a user's terminal, analyzes it, and generates draft rules and standards in conjunction with a database.

[0513] A "database" is a collection of information that stores existing data on rules and standards, making it searchable by a server using queries.

[0514] "Natural language generation means" refers to algorithms and software for automatically generating draft rules and standards based on input data and information obtained from databases.

[0515] A "draft" refers to an initial version of a legal document containing generated rules or standards, provided in a format that allows users to review and edit it.

[0516] "Display means" refers to the functions and interfaces for displaying the generated draft on the user's terminal.

[0517] "Editing means" refers to the functions and interfaces that allow users to review the generated draft and make corrections and edits as needed.

[0518] "Storage method" refers to the function that sends the user-edited draft back to the server and saves it in the database.

[0519] For this invention to be implemented, it is crucial that the user, terminal, and server each fulfill their respective roles. The following describes how this system will be implemented in detail.

[0520] First, the user accesses a dedicated web form or application using a device (e.g., a PC or smartphone). Using this interface, the user enters necessary information such as the "purpose of the regulation," "scope of application," "matters covered," and "specific regulatory content" in text fields. The input process is designed to be intuitive for the user.

[0521] Once the user enters the necessary information, the terminal packages it in JSON or XML format and sends it to the server using an HTTP POST request. For example, if the following input is given:

[0522] Purpose of regulation: To enhance the protection of personal information.

[0523] Scope of application: Financial institutions

[0524] Topics covered: Management of personal data

[0525] Specific regulations: Explicit consent is required for the collection and use of personal data.

[0526] The server receives the transmitted data in JSON or XML format and parses it. It then queries the rules and standards database to find relevant existing laws and standards. The data obtained as a result of the search is combined with the information entered by the user.

[0527] Next, the server uses a natural language generation (NLG) algorithm to generate drafts of laws and standards based on the acquired data and user input data. This draft generation uses a template engine, dynamically filling in user input data into specific fields. For example, the following template is used:

[0528] Chapter 1 General Provisions

[0529] 1. This law aims to {{ purpose}}.

[0530] 2. The scope of application shall be {{ scope}}.

[0531] Chapter 2: Regulations

[0532] 1. Regarding {{subjects}}, {{regulation_content}}.

[0533] The draft generated based on this will look like this:

[0534] Chapter 1 General Provisions

[0535] 1. This law aims to strengthen the protection of personal information.

[0536] 2. The scope of application shall be financial institutions.

[0537] Chapter 2: Regulations

[0538] 1. Regarding the management of personal data, explicit consent is required for the collection and use of personal data.

[0539] The generated draft is resent from the server to the user's device. The user can review this draft within a web form or application and edit it as needed. Once editing is complete, the user presses the save button, and the edited draft is sent back to the server and saved to the database.

[0540] Through this procedure, this invention can quickly and accurately generate drafts of rules and standards, and provide an environment in which users can easily create and edit legal documents. This leads to increased efficiency and digitalization of legal work.

[0541] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0542] Step 1: User Input

[0543] Users access the system using a terminal. They enter the "purpose of the regulation," "scope of application," "subjects covered," and "specific regulatory details" in text fields via a dedicated web form or application. For example:

[0544] Purpose of regulation: To enhance the protection of personal information.

[0545] Scope of application: Financial institutions

[0546] Topics covered: Management of personal data

[0547] Specific regulations: Explicit consent is required for the collection and use of personal data.

[0548] Once the user has finished entering information into all the fields, they click the "Submit" button in the form.

[0549] Step 2: Submit the input data

[0550] The terminal converts the data entered by the user into JSON or XML format and sends it to the server using an HTTP POST request. For example, the following JSON data is generated:

[0551] {

[0552] "Essential": "Strengthening of personal information protection",

[0553] "scope": "Financial Institutions",

[0554] "subjects": "Management of personal data",

[0555] "regulation_content": "Explicit consent is required for the collection and use of personal data."

[0556] }

[0557] The device performs the specific action of sending this JSON data to the server.

[0558] Step 3: Analyze the input data

[0559] The server receives the transmitted JSON data and performs parsing. Here, it reads each field and converts it into a data structure. For example, the data corresponding to the "purpose" field is interpreted as "Enhancing personal information protection." Based on this parsing result, the server constructs its internal data structure.

[0560] Step 4: Execute database query

[0561] The server queries a database of rules and standards based on the analyzed data. For example, it generates a query to search for existing regulations on "personal data protection" and sends it to the database. The database returns data containing the relevant existing regulations. Specifically, this involves generating and executing SQL queries.

[0562] Step 5: Draft Generation

[0563] The server combines existing legal data retrieved from the database with user input data and generates a draft using a natural language generation (NLG) algorithm. A template engine is used to dynamically populate specific fields with user data. This process utilizes an AI model to generate the appropriate document structure. For example, the following template is used:

[0564] Chapter 1 General Provisions

[0565] 1. This law aims to {{ purpose}}.

[0566] 2. The scope of application shall be {{ scope}}.

[0567] Chapter 2: Regulations

[0568] 1. Regarding {{subjects}}, {{regulation_content}}.

[0569] The draft generated based on this would look like this:

[0570] Chapter 1 General Provisions

[0571] 1. This law aims to strengthen the protection of personal information.

[0572] 2. The scope of application shall be financial institutions.

[0573] Chapter 2: Regulations

[0574] 1. Regarding the management of personal data, explicit consent is required for the collection and use of personal data.

[0575] Step 6: Submit the generated draft

[0576] The generated draft is sent again from the server to the user's terminal. The server packages this draft as an HTTP response and sends it to the terminal. This allows the user to review the generated draft.

[0577] Step 7: Editing the draft

[0578] Users review the generated draft on their device and edit it as needed. This editing process takes place within a web form or application, allowing users to modify the draft's content. After making changes, the user clicks the "Save" button.

[0579] Step 8: Save the edited draft

[0580] When the user clicks the save button, the device resends the edited draft to the server. The server receives the submitted edited draft and saves it to the database again. In this specific operation, the JSON data is parsed and the appropriate database entry is updated.

[0581] This processing flow allows the system to efficiently and accurately generate draft rules and standards, providing an environment where users can easily edit them.

[0582] (Application Example 1)

[0583] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0584] In today's digital society, users need to create and edit legal documents and product pages quickly and accurately. However, creating these documents requires specialized knowledge, and understanding and editing them is time-consuming. An efficient system is needed to solve this problem.

[0585] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0586] In this invention, the server includes means for the user to input input data including the purpose, scope, subject matter, and specific content of the regulation; means for transmitting the input data to the server; means for the server to analyze the input data and search a database for relevant existing laws and regulations; means for the server to generate a draft of the law or regulation based on the search results; means for displaying the generated draft on the user's terminal; means for the user to input product name, price, description, and ingredient information; means for transmitting the input product data to the server; means for the server to search a database for relevant product information based on the product data; and means for the server to display the generated product page draft on the user's terminal. This makes it possible to easily generate not only drafts of laws and regulations but also product pages.

[0587] A "user" is an end-user who accesses and operates a system or application.

[0588] The "purpose of regulation" refers to the specific goals and directions that the established regulation should achieve.

[0589] "Scope of application" refers to the specific region, organization, or situation to which the regulation or law applies.

[0590] "Subject matter" refers to specific matters, activities, or areas that are subject to regulation.

[0591] "Specific regulations" refer to actions, conditions, or prohibitions specifically defined in regulations or laws.

[0592] "Input data" refers to data that includes information and content provided by the user to the system.

[0593] "Means" refers to the methods, tools, and processes used to achieve a specific purpose or function.

[0594] A "server" refers to a central system that receives requests from users and processes and provides data.

[0595] "Natural language generation" refers to the technology that allows computers to generate documents and texts in human language.

[0596] "Product name" refers to the name or label used to identify a specific product.

[0597] "Price" refers to the amount or cost set for a product or service.

[0598] A "description" refers to text that describes detailed information and features about a product or service.

[0599] "Ingredient information" refers to a list of materials and ingredients contained in a particular product, especially food and cosmetics.

[0600] A "database" refers to a computer system for efficiently storing, searching, and managing structured information.

[0601] A "product page draft" refers to the initial version or first draft of a page that allows users to view information about a product.

[0602] The system for implementing this invention is one in which a user inputs data using their own terminal, and a server analyzes and processes that data to generate and display drafts of laws and regulations and draft product pages.

[0603] Specifically, users use their devices to input data such as "purpose of regulation," "scope of application," "subjects covered," and "specific regulatory content" via web forms or applications. Similarly, users input data including "product name," "price," "description," and "ingredient information" to generate product pages. This input data is entered through a text-field interface designed for intuitive user interaction.

[0604] The input data is structured in JSON or XML format and sent to the server using an HTTP POST request. The server parses the received data and searches the legal database for relevant laws and regulations. Similarly, for product data, the server searches the database for relevant product information after receiving the data.

[0605] The server generates drafts using natural language generation (NLG) algorithms based on search results. Leveraging a template engine, it dynamically embeds user input data into specific fields, ensuring the generated drafts meet the format and structure requirements of legal or product pages. Not only are legal and regulatory drafts generated, but product page drafts are also templated and provided in a format easily understandable to users.

[0606] The generated draft is sent back to the user's device, where they can review and edit it within a web form or application. Once editing is complete, pressing the save button again saves the edited draft to the server. This system provides an environment for the rapid and accurate creation and editing of legal / regulation drafts and product page drafts.

[0607] Hardware and software to use

[0608] Hardware: User terminals (e.g., personal computers, smartphones), servers

[0609] Software: Flask (web framework), Jinja2 (template engine), requests (HTTP request library)

[0610] Specific example

[0611] User input:

[0612] Purpose of regulation: To enhance the protection of personal information.

[0613] Scope of application: Financial institutions

[0614] Topics covered: Management of personal data

[0615] Specific regulations: Explicit consent is required for the collection and use of personal data.

[0616] Product Name: Premium Black Tea

[0617] Price: 1200

[0618] Description: This is a fragrant black tea made with high-quality tea leaves.

[0619] Ingredient information: black tea leaves, fragrance

[0620] Example of a prompt

[0621] Input prompt: "Please enter the purpose, scope, subject matter, and specific details of the regulation. Also, please enter the product name, price, description, and ingredient information."

[0622] Prompt message for generated draft: "The following draft laws / regulations and product page drafts have been generated. You can edit the content."

[0623] This system allows users to easily create legal documents and product pages without requiring specialized knowledge.

[0624] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0625] Step 1:

[0626] Users input the purpose, scope, and subject matter of the regulation, as well as specific regulatory details, or product name, price, description, and ingredient information through their user terminal. The input data is structured in text field format and is entered via an intuitive web form or application.

[0627] Step 2:

[0628] The user terminal packages the entered data in JSON or XML format and sends it to the server using an HTTP POST request. In this case, the user's input data is sent to the server as the body of the HTTP request.

[0629] Step 3:

[0630] The server analyzes the received data. Specifically, it extracts data about regulations and products entered by the user and stores it as structured information. For example, input data about regulations is analyzed as follows:

[0631] {

[0632] "Essential": "Strengthening of personal information protection",

[0633] "scope": "Financial Institutions",

[0634] "subjects": "Management of personal data",

[0635] "regulation_content": "Explicit consent is required for the collection and use of personal data."

[0636] }

[0637] Product data is analyzed in the same manner.

[0638] {

[0639] "product_name": "Premium Black Tea",

[0640] "price": 1200,

[0641] "Description": "A fragrant black tea made with high-quality tea leaves."

[0642] "ingredients": "Tea leaves, fragrances"

[0643] }

[0644] Step 4:

[0645] Based on the analyzed data, the server searches the database for relevant legal data or product information. For example, it searches and queries existing laws and regulations related to "personal information protection" or existing product information related to "tea." The relevant legal data is retrieved as follows:

[0646] [

[0647] {

[0648] "law": "Personal Information Protection Law",

[0649] "clause": "Article 12",

[0650] "Description": "Explicit consent is required for the collection of personal data."

[0651] }

[0652] ]

[0653] Product information is retrieved in the same manner.

[0654] [

[0655] {

[0656] "name": "Tea A",

[0657] "price": 1000,

[0658] "description": "Fragrant Black Tea A"

[0659] }

[0660] ]

[0661] Step 5:

[0662] The server uses a natural language generation (NLG) algorithm based on the acquired data to generate drafts of laws, regulations, or product pages. The generated drafts utilize a template engine (e.g., Jinja2). User input data is dynamically embedded into the template and formatted as a draft. The generated draft will look like this:

[0663] Chapter 1 General Provisions

[0664] 1. This law aims to strengthen the protection of personal information.

[0665] 2. The scope of application shall be financial institutions.

[0666] Chapter 2: Regulations

[0667] 1. Regarding the management of personal data, explicit consent is required for the collection and use of personal data.

[0668] Product Name: Premium Black Tea

[0669] Price: ¥1200

[0670] Description: This is a fragrant black tea made with high-quality tea leaves.

[0671] Ingredient information: black tea leaves, fragrance

[0672] Related products:

[0673] Black Tea A - ¥1000: Aromatic Black Tea A

[0674] Step 6:

[0675] The generated draft is sent back to the user's terminal in JSON or HTML format. The user's terminal displays the received draft in the user interface, allowing the user to review and edit it. The user edits the draft and sends it back to the server by pressing the save button.

[0676] Step 7:

[0677] The draft edited by the user is sent to the server and ultimately stored in the database. This allows users to efficiently generate and edit high-quality drafts of laws and regulations, as well as product pages.

[0678] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0679] User input and emotion recognition

[0680] Users access the system using a device (e.g., a personal computer or smartphone). Through a dedicated web form or application, users input necessary information such as the "purpose of the regulation," "scope of application," "subjects covered," and "specific regulatory content." Simultaneously, an emotion engine analyzes the user's emotions (e.g., joy, anger, sadness) in real time. The emotion engine identifies emotions based on the user's facial recognition and text analysis.

[0681] Sending input data and emotion data

[0682] Once input is complete, the device sends the entered data, along with sentiment data, to the server. The data is packaged in JSON or XML format and sent using an HTTP POST request.

[0683] Specific example:

[0684] When a user enters data for the purpose of "protecting personal information in financial institutions," that data is sent to the server in the following format.

[0685] {

[0686] "Essential": "Strengthening of personal information protection",

[0687] "scope": "Financial Institutions",

[0688] "subjects": "Management of personal data",

[0689] "regulation_content": "Explicit consent is required for the collection and use of personal data",

[0690] "emotion": "frustration" / / Example of emotion

[0691] }

[0692] Interaction with the database and consideration of emotions

[0693] Upon receiving the transmitted data, the server analyzes it and extracts each field (purpose, scope, subject matter, specific regulations, and sentiment information). Based on the analyzed data, the server queries a database of laws and regulations, seeking relevant existing laws and ordinances.

[0694] Specific example:

[0695] Search existing laws and regulations concerning "personal information protection" and retrieve relevant articles and regulations.

[0696] Draft generation and emotional response

[0697] The server embeds acquired data and user input data into a template and generates drafts of laws and regulations using a natural language generation (NLG) algorithm. During this process, the wording and expression of the draft are adjusted based on sentiment data from the sentiment engine. For example, if a user expresses dissatisfaction, the draft's expression is made more specific and clear to best reflect the user's intent.

[0698] Specific draft generation process:

[0699] Template:

[0700] Chapter 1 General Provisions

[0701] 1. This law aims to {{ purpose}}.

[0702] 2. The scope of application shall be {{ scope}}.

[0703] Chapter 2: Regulations

[0704] 1. Regarding {{subjects}}, {{regulation_content}}.

[0705] Generated draft:

[0706] Chapter 1 General Provisions

[0707] 1. This law aims to strengthen the protection of personal information.

[0708] 2. The scope of application shall be financial institutions.

[0709] Chapter 2: Regulations

[0710] 1. Regarding the management of personal data, explicit consent is required for the collection and use of personal data. In particular, the user's wishes must not be disregarded.

[0711] Viewing and editing results

[0712] The generated draft is sent back to the user's device. The user can review this draft within the web form or application and edit it as needed. Once editing is complete, pressing the save button again will save the edited draft to the server.

[0713] In this way, the system of the present invention provides an environment in which drafts of laws and regulations can be generated quickly and accurately, and which can create and edit legal documents with higher precision by taking into account the user's feelings. This enables the digitalization and efficiency of legal infrastructure.

[0714] The following describes the processing flow.

[0715] Step 1:

[0716] The user enters the purpose, scope, target items, and specific details of the regulation into a dedicated web form or application text field. Simultaneously, an emotion engine monitors the user's facial expressions and typing speed, analyzing their emotions in real time.

[0717] Step 2:

[0718] The terminal packages the emotion data obtained from the emotion engine, along with the entered restriction information, in JSON or XML format and sends it to the server using an HTTP POST request.

[0719] Step 3:

[0720] The server analyzes the received regulatory data and sentiment data, and extracts each field (purpose, scope of application, subject matter, specific regulatory content, and sentiment information).

[0721] Step 4:

[0722] Based on the data analyzed by the server, queries are executed against a database of laws and regulations to search for relevant existing laws and regulations. For example, past laws and regulations related to "personal information protection" may be extracted.

[0723] Step 5:

[0724] The server retrieves relevant laws and regulations from the database and compares them with a template, then generates a draft that also takes sentiment information into account. By using the sentiment engine, if a user expresses "dissatisfaction," more specific language and additional explanations will be included.

[0725] Step 6:

[0726] The server sends the generated draft to the user's device. The draft incorporates adjustments based on sentiment data.

[0727] Specific example:

[0728] Template:

[0729] Chapter 1 General Provisions

[0730] 1. This law aims to {{ purpose}}.

[0731] 2. The scope of application shall be {{ scope}}.

[0732] Chapter 2: Regulations

[0733] 1. Regarding {{subjects}}, {{regulation_content}}.

[0734] Adjustments during draft generation:

[0735] Chapter 1 General Provisions

[0736] 1. This law aims to strengthen the protection of personal information.

[0737] 2. The scope of application shall be financial institutions.

[0738] Chapter 2: Regulations

[0739] 1. Regarding the management of personal data, explicit consent is required for the collection and use of personal data. In particular, the user's wishes must not be disregarded.

[0740] Step 7:

[0741] The terminal displays the generated draft. Users can review the draft within the web form or application and edit it as needed.

[0742] Step 8:

[0743] The user finishes editing the draft and presses the "Save" button to resubmit the edited draft to the server.

[0744] Step 9:

[0745] The server saves the final draft and performs any additional processing necessary for further legal refinement.

[0746] (Example 2)

[0747] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0748] Conventional systems for generating drafts of laws and regulations generate them based on user input data, but their accuracy and suitability are limited. Furthermore, because document generation does not take user emotions into consideration, it is difficult to create legal documents that fully reflect the user's intentions and feelings. As a result, users are often dissatisfied when generating drafts of laws and regulations, leading to a decrease in work efficiency.

[0749] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user to input input data including the purpose of regulation, scope of application, matters to be covered, and specific content of regulation; means for transmitting the input data and the user's sentiment data to the server; means for the server to analyze the input data and sentiment data and search for relevant existing laws and regulations data from a database; natural language generation means for the server to generate a draft of the law or regulation based on the search results and the user's sentiment data; and means for displaying the generated draft on the user's terminal. This makes it possible to generate an appropriate draft of the law or regulation that reflects the user's sentiment.

[0750] A "user" refers to an individual or legal entity that uses the system to generate drafts of laws and regulations.

[0751] "Input data" refers to data entered by the user that includes the purpose, scope, subject matter, and specific content of the regulations.

[0752] "Emotional data" refers to information about the user's emotions (e.g., joy, anger, sadness, etc.) detected during the user's input process.

[0753] A "server" refers to a computer system that receives and analyzes data sent by users and generates drafts of laws and regulations.

[0754] "Natural language generation means" refers to a function or algorithm that automatically generates drafts of laws and regulations using natural language based on input data.

[0755] A "template" refers to a framework containing standard phrases used to draft laws and regulations.

[0756] A "database" refers to an information storage system that stores and makes searchable information related to laws and regulations.

[0757] A "terminal" refers to an electronic device (such as a personal computer or smartphone) that a user uses to access a system, input data, or view and edit generated drafts.

[0758] A "draft" refers to a draft document of a law or ordinance that is automatically generated by a generative AI model.

[0759] "Means of input" refers to the interface or method by which a user supplies input data to the system.

[0760] "Means of transmission" refers to the means of communication used to send input data and emotional data from a terminal to a server.

[0761] "Means of analysis" refers to algorithms and processes used by a server to analyze received data and extract necessary information.

[0762] "Means of display" refers to the interface or method for displaying the generated draft on the user's device.

[0763] The system of this invention is for the rapid and accurate generation of drafts of laws and regulations, and includes various means for user input, sentiment recognition, data analysis, draft generation, display, and editing. Specific embodiments of the system are described below.

[0764] Users access the system using devices such as personal computers or smartphones. They input necessary information, including the "purpose of the regulation," "scope of application," "subjects covered," and "specific regulatory content," via a dedicated web form or application. During this input process, the emotion engine analyzes the user's emotions in real time. The emotion engine is a software module that identifies emotions through facial recognition and text analysis.

[0765] Once input is complete, the terminal sends the entered data and sentiment data to the server. The data is packaged in JSON or XML format and sent using an HTTP POST request. The server parses the received data and extracts each field (purpose, scope, subject matter, specific regulations, and sentiment information). Based on the parsed data, the server queries a database of laws and regulations to find relevant existing laws and regulations.

[0766] The server embeds acquired data and user input data into a template and uses a generative AI model (e.g., GPT-3) to generate drafts of laws and regulations. During this process, the wording and expression of the draft are adjusted based on sentiment data from the sentiment engine. For example, if a user expresses dissatisfaction, the draft's wording becomes more specific and detailed.

[0767] The generated draft is resent from the server to the user's device. Users can review the draft through a web form or application and edit it as needed. Once editing is complete, pressing the save button again will save the edited draft to the server.

[0768] The following are examples of specific prompt messages.

[0769] "(Input data): Purpose: Strengthening personal information protection; Scope of application: Financial institutions; Target: Management of personal data; Specific regulations: Clear consent is required for the collection and use of personal data; (Emotion): Dissatisfaction"

[0770] "(Input data): Purpose: Promotion of environmental protection; Scope of application: All companies; Target: Waste management; Specific regulations: Proper disposal of waste; (Emotion) Joy"

[0771] This system enables the generation of appropriate draft laws and regulations that reflect user sentiment, providing more accurate legal documents. Furthermore, it allows for smooth editing of documents to suit user intent, contributing to the digitalization and efficiency of legal procedures.

[0772] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0773] Step 1:

[0774] Users access the system using devices such as personal computers or smartphones. Through a dedicated web form or application, users input information such as the "purpose of the regulation," "scope of application," "subjects covered," and "specific regulatory content." During this input process, an emotion engine performs facial recognition of the user and identifies the user's emotions through text analysis. This process generates user input data and emotion data, which are then passed on to the next step.

[0775] Step 2:

[0776] The terminal packages the input data and sentiment data into JSON or XML format and sends it to the server using an HTTP POST request. Specifically, it sets values ​​for each field (purpose, scope, target items, specific regulations, and sentiment information) in a JSON object and includes it in the body of the HTTP request. As a result, the server receives this data.

[0777] Step 3:

[0778] The server analyzes the received data. The data analysis module parses the data in JSON format and extracts each field (purpose, scope, subject matter, specific regulations, and sentiment information). Based on this, the server prepares the data to send queries to databases related to laws and regulations in the next step.

[0779] Step 4:

[0780] Based on the analysis results, the server executes queries against a database of laws and regulations. Specifically, a query generation module executes the generated queries against the legal database to search for existing laws and regulations related to fields such as "personal information protection" and "financial institutions." As a result of this query execution, the relevant legal data is returned to the server.

[0781] Step 5:

[0782] The server embeds acquired data and user input data into a template and uses a generative AI model (e.g., GPT-3) to generate drafts of laws and regulations. This process adjusts the wording and phrasing of the draft based on sentiment data acquired from an emotion engine. The generative AI model references the template and forms the draft document based on user-provided data and sentiment data. As a result, a completed draft is generated.

[0783] Step 6:

[0784] The server sends the generated draft to the terminal in JSON or plain text format. The terminal displays the received draft within the application. Users can review the draft through web forms or applications and edit it as needed. The user's edits are sent back to the server for saving. This process ensures that the legal document best reflects the user's intent.

[0785] (Application Example 2)

[0786] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0787] Conventional regulatory draft generation systems often failed to adequately reflect user intentions because they did not consider user sentiment. Furthermore, the generated drafts were often formal and did not address user dissatisfaction in terms of specific content or expression. This resulted in low user satisfaction and a need for improved quality in the generated drafts.

[0788] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0789] In this invention, the server includes means for analyzing user input data and sentiment data, means for searching a database for relevant existing regulatory data, and means for generating a draft of regulations based on the search results and sentiment data. This makes it possible to generate an appropriate draft of regulations that takes user sentiment into consideration.

[0790] A "user" is an entity that utilizes the system, and is a person or group that inputs data for a specific purpose.

[0791] A "regulation" is a law, ordinance, or rule enacted to achieve a specific purpose.

[0792] "Input data" refers to information provided by the user to the system, including the purpose of the regulation, its scope, the matters covered, and the specific content of the regulation.

[0793] "Emotional data" refers to data that indicates the user's emotions at the time of input, and is extracted by analyzing emotional states such as joy, anger, and sadness.

[0794] A "server" is a computing system that receives data sent by users, analyzes it, and generates draft regulations.

[0795] A "database" is an information aggregation system that stores relevant existing regulatory data and allows servers to perform searches.

[0796] "Searching" is the act of a server retrieving information related to user input data and sentiment data from a database.

[0797] "Natural language generation" refers to algorithms or techniques for generating draft regulations based on analyzed data.

[0798] A "template" is a standard document format used in the generation of draft regulations.

[0799] The system for implementing this invention first provides a means for the user to input data including the purpose of the regulation, its scope, the matters covered, and the specific content of the regulation. The user accesses a dedicated web form or application using a terminal such as a smartphone or personal computer and inputs the data. At the same time, the user's sentiment data is also collected. The sentiment data is collected by the smartphone's camera and microphone and analyzed in real time. Software such as OpenCV or TensorFlow is used for this sentiment analysis.

[0800] Input data and sentiment data are packaged in JSON or XML format and sent to the server using the HTTPS protocol. The transmitted data is received and analyzed on the server. The server extracts the "purpose of the regulation," "scope of application," "targeted matters," "specific regulatory content," and "sentiment data" from the analyzed data.

[0801] Based on the extracted data, the server interacts with the database to search for relevant existing regulatory data. Based on this search, the server embeds the retrieved data into a template and generates a draft of the regulation using a natural language generation (NLG) algorithm. At this time, the wording of the draft is adjusted to align with the user's intentions based on sentiment data. For example, if the user has the emotion of "dissatisfaction," the wording of the draft is made more specific and clear to best reflect the user's intentions.

[0802] The generated draft is sent back to the user's device, where the user can review and edit it. The editing function is designed to allow the user to modify the draft content as needed and save it again to the server.

[0803] As a specific example, if a user enters information with the purpose of "protecting personal information in financial institutions," the following prompt message will be generated.

[0804] The user entered the data targeting financial institutions with the aim of "strengthening personal information protection." The sentiment data indicates "dissatisfaction."

[0805] Input data:

[0806] Objective: To strengthen the protection of personal information.

[0807] Scope of application: Financial institutions

[0808] Topics covered: Management of personal data

[0809] Regulations: Explicit consent is required for the collection and use of personal data.

[0810] User sentiment: Dissatisfaction

[0811] Prompt message:

[0812] "The user feels frustration. Draft an appropriate response for strengthening personal data protection within financial institutions."

[0813] This allows the system to quickly and accurately generate regulatory drafts that reflect user intentions and sentiments, providing users with more satisfying results.

[0814] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0815] Step 1:

[0816] Users access a dedicated web form or application and enter the purpose, scope, subject matter, and specific details of the regulation. The entered data is saved on the device in text format, and sentiment data is collected in real time using the camera and microphone. Sentiment data is extracted through image analysis and voice analysis.

[0817] Input: User input data (text format), user's facial image, and audio.

[0818] Output: Input data and sentiment data

[0819] Step 2:

[0820] The device packages input data and sentiment data in JSON format and sends it to the server using the HTTPS protocol. Encryption is applied to enhance security during transmission.

[0821] Input: Input data, sentiment data

[0822] Output: Data package in JSON format

[0823] Step 3:

[0824] The server receives the transmitted data in JSON format and performs analysis. The analysis extracts the "purpose of the regulation," "scope of application," "targeted matters," "specific regulatory details," and "sentiment data" from the input data. This analysis is performed to facilitate database searching.

[0825] Input: Data package in JSON format

[0826] Output: Extracted input data and sentiment data

[0827] Step 4:

[0828] Based on the data extracted by the server, relevant existing regulatory data is searched for in the database. SQL queries are used for the search to identify database entries related to the regulations.

[0829] Input: Extracted input data and sentiment data

[0830] Output: Search results (existing regulatory data)

[0831] Step 5:

[0832] The server uses search results and sentiment data to apply a natural language generation (NLG) algorithm to generate a draft of the regulations. During this process, the draft text is refined based on sentiment data, selecting expressions that align with user preferences.

[0833] Input: Search results (existing regulatory data), sentiment data

[0834] Output: Generated regulatory draft

[0835] Step 6:

[0836] The server packages the generated draft back into JSON format and sends it to the user's terminal. An editing function for the draft is also provided at this time.

[0837] Input: Generated regulatory draft

[0838] Output: Draft data in JSON format

[0839] Step 7:

[0840] The user's device displays the received draft and makes it editable. The user reviews the draft and makes corrections to complete the final regulations. The edits are then saved back to the server.

[0841] Input: Draft data in JSON format

[0842] Output: Edited regulatory draft

[0843] Step 8:

[0844] Once the user finishes editing and presses the save button, the device sends the edited data back to the server. The server then saves it to the database as the final regulatory draft.

[0845] Input: Edited regulatory draft

[0846] Output: Saved final regulatory draft

[0847] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0848] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0849] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0850] [Third Embodiment]

[0851] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0852] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0853] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0854] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0855] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0856] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0857] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0858] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0859] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0860] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0861] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0862] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0863] User input

[0864] Users access the system using their own devices (e.g., personal computers or smartphones). Through a dedicated web form or application, users input necessary information such as the "purpose of the regulation," "scope of application," "subjects covered," and "specific regulatory details." The input is in text field format, allowing users to operate it intuitively.

[0865] Sending input data

[0866] Once input is complete, the terminal sends the entered data to the server. The data is packaged in JSON or XML format and sent using an HTTP POST request.

[0867] Specific example:

[0868] If a user enters data for the purpose of "protecting personal information in financial institutions," that data will be sent to the server.

[0869] {

[0870] "Essential": "Strengthening of personal information protection",

[0871] "scope": "Financial Institutions",

[0872] "subjects": "Management of personal data",

[0873] "regulation_content": "Explicit consent is required for the collection and use of personal data."

[0874] }

[0875] Interaction with the database

[0876] Upon receiving the transmitted data, the server analyzes it and queries existing laws and regulations that relate to the regulations. The server queries the database of legal information and retrieves the relevant data.

[0877] Specific example:

[0878] Search existing laws and regulations concerning "personal information protection" and retrieve relevant articles and regulations.

[0879] Draft generation

[0880] The server generates drafts of laws and regulations using natural language generation (NLG) algorithms based on acquired data and user input data. This is done by dynamically embedding user input data into specific fields using a template engine. The generated drafts meet legally required formats and structures and are provided in a user-friendly format.

[0881] Specific draft generation process:

[0882] Template:

[0883] Chapter 1 General Provisions

[0884] 1. This law aims to {{ purpose}}.

[0885] 2. The scope of application shall be {{ scope}}.

[0886] Chapter 2: Regulations

[0887] 1. Regarding {{subjects}}, {{regulation_content}}.

[0888] Generated draft:

[0889] Chapter 1 General Provisions

[0890] 1. This law aims to strengthen the protection of personal information.

[0891] 2. The scope of application shall be financial institutions.

[0892] Chapter 2: Regulations

[0893] 1. Regarding the management of personal data, explicit consent is required for the collection and use of personal data.

[0894] Viewing and editing results

[0895] The generated draft is sent back to the user's device. The user can review this draft within the web form or application and edit it as needed. Once editing is complete, pressing the save button again will save the edited draft to the server.

[0896] In this way, the system of the present invention quickly and accurately generates drafts of laws and regulations, and provides an environment in which users can easily create and edit legal documents. This enables the digitalization and efficiency of legal infrastructure.

[0897] The following describes the processing flow.

[0898] Step 1:

[0899] Users enter input data, including the purpose, scope, subject matter, and specific content of the regulations, into a dedicated web form or application text field.

[0900] Step 2:

[0901] The terminal packages the input data in JSON or XML format and sends it to the server using an HTTP POST request.

[0902] Step 3:

[0903] The server analyzes the received data and extracts each field (purpose, scope of application, subject matter, and specific regulatory content).

[0904] Step 4:

[0905] Based on the data analyzed by the server, queries are executed against a database of laws and regulations to search for relevant existing laws and ordinances.

[0906] Step 5:

[0907] The server retrieves relevant laws and regulations from the database and matches that data against the appropriate template.

[0908] Step 6:

[0909] The server embeds acquired data and user input data into a template, and uses a natural language generation (NLG) algorithm to generate drafts of laws and regulations.

[0910] Step 7:

[0911] The server sends the generated draft to the user's terminal.

[0912] Step 8:

[0913] The terminal displays the generated draft. Users can review the draft within the web form or application and edit it as needed.

[0914] Step 9:

[0915] Once the user finishes editing and presses the "Save" button, the edited draft is sent back to the server.

[0916] Step 10:

[0917] The server saves the final draft and performs additional processing as needed to prepare it as a legal document.

[0918] (Example 1)

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

[0920] Traditionally, drafting rules and standards was a complex, time-consuming, and specialized process. Furthermore, ensuring consistency with existing laws and regulations required significant resources, and manual methods were prone to errors. Additionally, there were limited means to effectively utilize user-generated data and quickly generate legal documents. Consequently, meeting the high efficiency and accuracy demands of modern times proved difficult.

[0921] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0922] In this invention, the server includes means for the user to input input data including the purpose of the regulation, scope of application, matters covered, and specific content of the regulation; means for transmitting the input data to the server; means for the server to analyze the input data and search a database for relevant existing rules and standards data; means for the server to generate a draft of the rules and standards based on the search results; means for displaying the generated draft on the user's terminal; means for the user to review the generated draft and edit it within a web form or application; and means for transmitting the edited draft back to the server and saving it in the database. This enables the rapid and accurate generation of draft rules and standards based on data intuitively entered by the user, and allows for the creation of high-quality legal documents through repeated editing.

[0923] A "user" refers to a person who accesses the system and enters the purpose of the regulation, its scope, the matters covered, and the specific content of the regulation.

[0924] "Input data" refers to information that users input into the system, such as the purpose, scope, subject matter, and specific content of the regulations.

[0925] A "terminal" is a device used by a user to access a system, and includes personal computers, smartphones, and other similar devices.

[0926] A "server" refers to a computer system that receives input data sent from a user's terminal, analyzes it, and generates draft rules and standards in conjunction with a database.

[0927] A "database" is a collection of information that stores existing data on rules and standards, making it searchable by a server using queries.

[0928] "Natural language generation means" refers to algorithms and software for automatically generating draft rules and standards based on input data and information obtained from databases.

[0929] A "draft" refers to an initial version of a legal document containing generated rules or standards, provided in a format that allows users to review and edit it.

[0930] "Display means" refers to the functions and interfaces for displaying the generated draft on the user's terminal.

[0931] "Editing means" refers to the functions and interfaces that allow users to review the generated draft and make corrections and edits as needed.

[0932] "Storage method" refers to the function that sends the user-edited draft back to the server and saves it in the database.

[0933] For this invention to be implemented, it is crucial that the user, terminal, and server each fulfill their respective roles. The following describes how this system will be implemented in detail.

[0934] First, the user accesses a dedicated web form or application using a device (e.g., a PC or smartphone). Using this interface, the user enters necessary information such as the "purpose of the regulation," "scope of application," "matters covered," and "specific regulatory content" in text fields. The input process is designed to be intuitive for the user.

[0935] Once the user enters the necessary information, the terminal packages it in JSON or XML format and sends it to the server using an HTTP POST request. For example, if the following input is given:

[0936] Purpose of regulation: To enhance the protection of personal information.

[0937] Scope of application: Financial institutions

[0938] Topics covered: Management of personal data

[0939] Specific regulations: Explicit consent is required for the collection and use of personal data.

[0940] The server receives the transmitted data in JSON or XML format and parses it. It then queries the rules and standards database to find relevant existing laws and standards. The data obtained as a result of the search is combined with the information entered by the user.

[0941] Next, the server uses a natural language generation (NLG) algorithm to generate drafts of laws and standards based on the acquired data and user input data. This draft generation uses a template engine, dynamically filling in user input data into specific fields. For example, the following template is used:

[0942] Chapter 1 General Provisions

[0943] 1. This law aims to {{ purpose}}.

[0944] 2. The scope of application shall be {{ scope}}.

[0945] Chapter 2: Regulations

[0946] 1. Regarding {{subjects}}, {{regulation_content}}.

[0947] The draft generated based on this will look like this:

[0948] Chapter 1 General Provisions

[0949] 1. This law aims to strengthen the protection of personal information.

[0950] 2. The scope of application shall be financial institutions.

[0951] Chapter 2: Regulations

[0952] 1. Regarding the management of personal data, explicit consent is required for the collection and use of personal data.

[0953] The generated draft is resent from the server to the user's device. The user can review this draft within a web form or application and edit it as needed. Once editing is complete, the user presses the save button, and the edited draft is sent back to the server and saved to the database.

[0954] Through this procedure, this invention can quickly and accurately generate drafts of rules and standards, and provide an environment in which users can easily create and edit legal documents. This leads to increased efficiency and digitalization of legal work.

[0955] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0956] Step 1: User Input

[0957] Users access the system using a terminal. They enter the "purpose of the regulation," "scope of application," "subjects covered," and "specific regulatory details" in text fields via a dedicated web form or application. For example:

[0958] Purpose of regulation: To enhance the protection of personal information.

[0959] Scope of application: Financial institutions

[0960] Topics covered: Management of personal data

[0961] Specific regulations: Explicit consent is required for the collection and use of personal data.

[0962] Once the user has finished entering information into all the fields, they click the "Submit" button in the form.

[0963] Step 2: Submit the input data

[0964] The terminal converts the data entered by the user into JSON or XML format and sends it to the server using an HTTP POST request. For example, the following JSON data is generated:

[0965] {

[0966] "Essential": "Strengthening of personal information protection",

[0967] "scope": "Financial Institutions",

[0968] "subjects": "Management of personal data",

[0969] "regulation_content": "Explicit consent is required for the collection and use of personal data."

[0970] }

[0971] The device performs the specific action of sending this JSON data to the server.

[0972] Step 3: Analyze the input data

[0973] The server receives the transmitted JSON data and performs parsing. Here, it reads each field and converts it into a data structure. For example, the data corresponding to the "purpose" field is interpreted as "Enhancing personal information protection." Based on this parsing result, the server constructs its internal data structure.

[0974] Step 4: Execute database query

[0975] The server queries a database of rules and standards based on the analyzed data. For example, it generates a query to search for existing regulations on "personal data protection" and sends it to the database. The database returns data containing the relevant existing regulations. Specifically, this involves generating and executing SQL queries.

[0976] Step 5: Draft Generation

[0977] The server combines existing legal data retrieved from the database with user input data and generates a draft using a natural language generation (NLG) algorithm. A template engine is used to dynamically populate specific fields with user data. This process utilizes an AI model to generate the appropriate document structure. For example, the following template is used:

[0978] Chapter 1 General Provisions

[0979] 1. This law aims to {{ purpose}}.

[0980] 2. The scope of application shall be {{ scope}}.

[0981] Chapter 2: Regulations

[0982] 1. Regarding {{subjects}}, {{regulation_content}}.

[0983] The draft generated based on this would look like this:

[0984] Chapter 1 General Provisions

[0985] 1. This law aims to strengthen the protection of personal information.

[0986] 2. The scope of application shall be financial institutions.

[0987] Chapter 2: Regulations

[0988] 1. Regarding the management of personal data, explicit consent is required for the collection and use of personal data.

[0989] Step 6: Submit the generated draft

[0990] The generated draft is sent again from the server to the user's terminal. The server packages this draft as an HTTP response and sends it to the terminal. This allows the user to review the generated draft.

[0991] Step 7: Editing the draft

[0992] Users review the generated draft on their device and edit it as needed. This editing process takes place within a web form or application, allowing users to modify the draft's content. After making changes, the user clicks the "Save" button.

[0993] Step 8: Save the edited draft

[0994] When the user clicks the save button, the device resends the edited draft to the server. The server receives the submitted edited draft and saves it to the database again. In this specific operation, the JSON data is parsed and the appropriate database entry is updated.

[0995] This processing flow allows the system to efficiently and accurately generate draft rules and standards, providing an environment where users can easily edit them.

[0996] (Application Example 1)

[0997] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0998] In today's digital society, users need to create and edit legal documents and product pages quickly and accurately. However, creating these documents requires specialized knowledge, and understanding and editing them is time-consuming. An efficient system is needed to solve this problem.

[0999] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1000] In this invention, the server includes means for the user to input input data including the purpose, scope, subject matter, and specific content of the regulation; means for transmitting the input data to the server; means for the server to analyze the input data and search a database for relevant existing laws and regulations; means for the server to generate a draft of the law or regulation based on the search results; means for displaying the generated draft on the user's terminal; means for the user to input product name, price, description, and ingredient information; means for transmitting the input product data to the server; means for the server to search a database for relevant product information based on the product data; and means for the server to display the generated product page draft on the user's terminal. This makes it possible to easily generate not only drafts of laws and regulations but also product pages.

[1001] A "user" is an end-user who accesses and operates a system or application.

[1002] The "purpose of regulation" refers to the specific goals and directions that the established regulation should achieve.

[1003] "Scope of application" refers to the specific region, organization, or situation to which the regulation or law applies.

[1004] "Subject matter" refers to specific matters, activities, or areas that are subject to regulation.

[1005] "Specific regulations" refer to actions, conditions, or prohibitions specifically defined in regulations or laws.

[1006] "Input data" refers to data that includes information and content provided by the user to the system.

[1007] "Means" refers to the methods, tools, and processes used to achieve a specific purpose or function.

[1008] A "server" refers to a central system that receives requests from users and processes and provides data.

[1009] "Natural language generation" refers to the technology that allows computers to generate documents and texts in human language.

[1010] "Product name" refers to the name or label used to identify a specific product.

[1011] "Price" refers to the amount or cost set for a product or service.

[1012] A "description" refers to text that describes detailed information and features about a product or service.

[1013] "Ingredient information" refers to a list of materials and ingredients contained in a particular product, especially food and cosmetics.

[1014] A "database" refers to a computer system for efficiently storing, searching, and managing structured information.

[1015] A "product page draft" refers to the initial version or first draft of a page that allows users to view information about a product.

[1016] The system for implementing this invention is one in which a user inputs data using their own terminal, and a server analyzes and processes that data to generate and display drafts of laws and regulations and draft product pages.

[1017] Specifically, users use their devices to input data such as "purpose of regulation," "scope of application," "subjects covered," and "specific regulatory content" via web forms or applications. Similarly, users input data including "product name," "price," "description," and "ingredient information" to generate product pages. This input data is entered through a text-field interface designed for intuitive user interaction.

[1018] The input data is structured in JSON or XML format and sent to the server using an HTTP POST request. The server parses the received data and searches the legal database for relevant laws and regulations. Similarly, for product data, the server searches the database for relevant product information after receiving the data.

[1019] The server generates drafts using natural language generation (NLG) algorithms based on search results. Leveraging a template engine, it dynamically embeds user input data into specific fields, ensuring the generated drafts meet the format and structure requirements of legal or product pages. Not only are legal and regulatory drafts generated, but product page drafts are also templated and provided in a format easily understandable to users.

[1020] The generated draft is sent back to the user's device, where they can review and edit it within a web form or application. Once editing is complete, pressing the save button again saves the edited draft to the server. This system provides an environment for the rapid and accurate creation and editing of legal / regulation drafts and product page drafts.

[1021] Hardware and software to use

[1022] Hardware: User terminals (e.g., personal computers, smartphones), servers

[1023] Software: Flask (web framework), Jinja2 (template engine), requests (HTTP request library)

[1024] Specific example

[1025] User input:

[1026] Purpose of regulation: To enhance the protection of personal information.

[1027] Scope of application: Financial institutions

[1028] Topics covered: Management of personal data

[1029] Specific regulations: Explicit consent is required for the collection and use of personal data.

[1030] Product Name: Premium Black Tea

[1031] Price: 1200

[1032] Description: This is a fragrant black tea made with high-quality tea leaves.

[1033] Ingredient information: black tea leaves, fragrance

[1034] Example of a prompt

[1035] Input prompt: "Please enter the purpose, scope, subject matter, and specific details of the regulation. Also, please enter the product name, price, description, and ingredient information."

[1036] Prompt message for generated draft: "The following draft laws / regulations and product page drafts have been generated. You can edit the content."

[1037] This system allows users to easily create legal documents and product pages without requiring specialized knowledge.

[1038] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1039] Step 1:

[1040] Users input the purpose, scope, and subject matter of the regulation, as well as specific regulatory details, or product name, price, description, and ingredient information through their user terminal. The input data is structured in text field format and is entered via an intuitive web form or application.

[1041] Step 2:

[1042] The user terminal packages the entered data in JSON or XML format and sends it to the server using an HTTP POST request. In this case, the user's input data is sent to the server as the body of the HTTP request.

[1043] Step 3:

[1044] The server analyzes the received data. Specifically, it extracts data about regulations and products entered by the user and stores it as structured information. For example, input data about regulations is analyzed as follows:

[1045] {

[1046] "Essential": "Strengthening of personal information protection",

[1047] "scope": "Financial Institutions",

[1048] "subjects": "Management of personal data",

[1049] "regulation_content": "Explicit consent is required for the collection and use of personal data."

[1050] }

[1051] Product data is analyzed in the same manner.

[1052] {

[1053] "product_name": "Premium Black Tea",

[1054] "price": 1200,

[1055] "Description": "A fragrant black tea made with high-quality tea leaves."

[1056] "ingredients": "Tea leaves, fragrances"

[1057] }

[1058] Step 4:

[1059] Based on the analyzed data, the server searches the database for relevant legal data or product information. For example, it searches and queries existing laws and regulations related to "personal information protection" or existing product information related to "tea." The relevant legal data is retrieved as follows:

[1060] [

[1061] {

[1062] "law": "Personal Information Protection Law",

[1063] "clause": "Article 12",

[1064] "Description": "Explicit consent is required for the collection of personal data."

[1065] }

[1066] ]

[1067] Product information is retrieved in the same manner.

[1068] [

[1069] {

[1070] "name": "Tea A",

[1071] "price": 1000,

[1072] "description": "Fragrant Black Tea A"

[1073] }

[1074] ]

[1075] Step 5:

[1076] The server uses a natural language generation (NLG) algorithm based on the acquired data to generate drafts of laws, regulations, or product pages. The generated drafts utilize a template engine (e.g., Jinja2). User input data is dynamically embedded into the template and formatted as a draft. The generated draft will look like this:

[1077] Chapter 1 General Provisions

[1078] 1. This law aims to strengthen the protection of personal information.

[1079] 2. The scope of application shall be financial institutions.

[1080] Chapter 2: Regulations

[1081] 1. Regarding the management of personal data, explicit consent is required for the collection and use of personal data.

[1082] Product Name: Premium Black Tea

[1083] Price: ¥1200

[1084] Description: This is a fragrant black tea made with high-quality tea leaves.

[1085] Ingredient information: black tea leaves, fragrance

[1086] Related products:

[1087] Black Tea A - ¥1000: Aromatic Black Tea A

[1088] Step 6:

[1089] The generated draft is sent back to the user's terminal in JSON or HTML format. The user's terminal displays the received draft in the user interface, allowing the user to review and edit it. The user edits the draft and sends it back to the server by pressing the save button.

[1090] Step 7:

[1091] The draft edited by the user is sent to the server and ultimately stored in the database. This allows users to efficiently generate and edit high-quality drafts of laws and regulations, as well as product pages.

[1092] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1093] User input and emotion recognition

[1094] Users access the system using a device (e.g., a personal computer or smartphone). Through a dedicated web form or application, users input necessary information such as the "purpose of the regulation," "scope of application," "subjects covered," and "specific regulatory content." Simultaneously, an emotion engine analyzes the user's emotions (e.g., joy, anger, sadness) in real time. The emotion engine identifies emotions based on the user's facial recognition and text analysis.

[1095] Sending input data and emotion data

[1096] Once input is complete, the device sends the entered data, along with sentiment data, to the server. The data is packaged in JSON or XML format and sent using an HTTP POST request.

[1097] Specific example:

[1098] When a user enters data for the purpose of "protecting personal information in financial institutions," that data is sent to the server in the following format.

[1099] {

[1100] "Essential": "Strengthening of personal information protection",

[1101] "scope": "Financial Institutions",

[1102] "subjects": "Management of personal data",

[1103] "regulation_content": "Explicit consent is required for the collection and use of personal data",

[1104] "emotion": "frustration" / / Example of emotion

[1105] }

[1106] Interaction with the database and consideration of emotions

[1107] Upon receiving the transmitted data, the server analyzes it and extracts each field (purpose, scope, subject matter, specific regulations, and sentiment information). Based on the analyzed data, the server queries a database of laws and regulations, seeking relevant existing laws and ordinances.

[1108] Specific example:

[1109] Search existing laws and regulations concerning "personal information protection" and retrieve relevant articles and regulations.

[1110] Draft generation and emotional response

[1111] The server embeds acquired data and user input data into a template and generates drafts of laws and regulations using a natural language generation (NLG) algorithm. During this process, the wording and expression of the draft are adjusted based on sentiment data from the sentiment engine. For example, if a user expresses dissatisfaction, the draft's expression is made more specific and clear to best reflect the user's intent.

[1112] Specific draft generation process:

[1113] Template:

[1114] Chapter 1 General Provisions

[1115] 1. This law aims to {{ purpose}}.

[1116] 2. The scope of application shall be {{ scope}}.

[1117] Chapter 2: Regulations

[1118] 1. Regarding {{subjects}}, {{regulation_content}}.

[1119] Generated draft:

[1120] Chapter 1 General Provisions

[1121] 1. This law aims to strengthen the protection of personal information.

[1122] 2. The scope of application shall be financial institutions.

[1123] Chapter 2: Regulations

[1124] 1. Regarding the management of personal data, explicit consent is required for the collection and use of personal data. In particular, the user's wishes must not be disregarded.

[1125] Viewing and editing results

[1126] The generated draft is sent back to the user's device. The user can review this draft within the web form or application and edit it as needed. Once editing is complete, pressing the save button again will save the edited draft to the server.

[1127] In this way, the system of the present invention provides an environment in which drafts of laws and regulations can be generated quickly and accurately, and which can create and edit legal documents with higher precision by taking into account the user's feelings. This enables the digitalization and efficiency of legal infrastructure.

[1128] The following describes the processing flow.

[1129] Step 1:

[1130] The user enters the purpose, scope, target items, and specific details of the regulation into a dedicated web form or application text field. Simultaneously, an emotion engine monitors the user's facial expressions and typing speed, analyzing their emotions in real time.

[1131] Step 2:

[1132] The terminal packages the emotion data obtained from the emotion engine, along with the entered restriction information, in JSON or XML format and sends it to the server using an HTTP POST request.

[1133] Step 3:

[1134] The server analyzes the received regulatory data and sentiment data, and extracts each field (purpose, scope of application, subject matter, specific regulatory content, and sentiment information).

[1135] Step 4:

[1136] Based on the data analyzed by the server, queries are executed against a database of laws and regulations to search for relevant existing laws and regulations. For example, past laws and regulations related to "personal information protection" may be extracted.

[1137] Step 5:

[1138] The server retrieves relevant laws and regulations from the database and compares them with a template, then generates a draft that also takes sentiment information into account. By using the sentiment engine, if a user expresses "dissatisfaction," more specific language and additional explanations will be included.

[1139] Step 6:

[1140] The server sends the generated draft to the user's device. The draft incorporates adjustments based on sentiment data.

[1141] Specific example:

[1142] Template:

[1143] Chapter 1 General Provisions

[1144] 1. This law aims to {{ purpose}}.

[1145] 2. The scope of application shall be {{ scope}}.

[1146] Chapter 2: Regulations

[1147] 1. Regarding {{subjects}}, {{regulation_content}}.

[1148] Adjustments during draft generation:

[1149] Chapter 1 General Provisions

[1150] 1. This law aims to strengthen the protection of personal information.

[1151] 2. The scope of application shall be financial institutions.

[1152] Chapter 2: Regulations

[1153] 1. Regarding the management of personal data, explicit consent is required for the collection and use of personal data. In particular, the user's wishes must not be disregarded.

[1154] Step 7:

[1155] The terminal displays the generated draft. Users can review the draft within the web form or application and edit it as needed.

[1156] Step 8:

[1157] The user finishes editing the draft and presses the "Save" button to resubmit the edited draft to the server.

[1158] Step 9:

[1159] The server saves the final draft and performs any additional processing necessary for further legal refinement.

[1160] (Example 2)

[1161] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1162] Conventional systems for generating drafts of laws and regulations generate them based on user input data, but their accuracy and suitability are limited. Furthermore, because document generation does not take user emotions into consideration, it is difficult to create legal documents that fully reflect the user's intentions and feelings. As a result, users are often dissatisfied when generating drafts of laws and regulations, leading to a decrease in work efficiency.

[1163] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user to input input data including the purpose of regulation, scope of application, matters to be covered, and specific content of regulation; means for transmitting the input data and the user's sentiment data to the server; means for the server to analyze the input data and sentiment data and search for relevant existing laws and regulations data from a database; natural language generation means for the server to generate a draft of the law or regulation based on the search results and the user's sentiment data; and means for displaying the generated draft on the user's terminal. This makes it possible to generate an appropriate draft of the law or regulation that reflects the user's sentiment.

[1164] A "user" refers to an individual or legal entity that uses the system to generate drafts of laws and regulations.

[1165] "Input data" refers to data entered by the user that includes the purpose, scope, subject matter, and specific content of the regulations.

[1166] "Emotional data" refers to information about the user's emotions (e.g., joy, anger, sadness, etc.) detected during the user's input process.

[1167] A "server" refers to a computer system that receives and analyzes data sent by users and generates drafts of laws and regulations.

[1168] "Natural language generation means" refers to a function or algorithm that automatically generates drafts of laws and regulations using natural language based on input data.

[1169] A "template" refers to a framework containing standard phrases used to draft laws and regulations.

[1170] A "database" refers to an information storage system that stores and makes searchable information related to laws and regulations.

[1171] A "terminal" refers to an electronic device (such as a personal computer or smartphone) that a user uses to access a system, input data, or view and edit generated drafts.

[1172] A "draft" refers to a draft document of a law or ordinance that is automatically generated by a generative AI model.

[1173] "Means of input" refers to the interface or method by which a user supplies input data to the system.

[1174] "Means of transmission" refers to the means of communication used to send input data and emotional data from a terminal to a server.

[1175] "Means of analysis" refers to algorithms and processes used by a server to analyze received data and extract necessary information.

[1176] "Means of display" refers to the interface or method for displaying the generated draft on the user's device.

[1177] The system of this invention is for the rapid and accurate generation of drafts of laws and regulations, and includes various means for user input, sentiment recognition, data analysis, draft generation, display, and editing. Specific embodiments of the system are described below.

[1178] Users access the system using devices such as personal computers or smartphones. They input necessary information, including the "purpose of the regulation," "scope of application," "subjects covered," and "specific regulatory content," via a dedicated web form or application. During this input process, the emotion engine analyzes the user's emotions in real time. The emotion engine is a software module that identifies emotions through facial recognition and text analysis.

[1179] Once input is complete, the terminal sends the entered data and sentiment data to the server. The data is packaged in JSON or XML format and sent using an HTTP POST request. The server parses the received data and extracts each field (purpose, scope, subject matter, specific regulations, and sentiment information). Based on the parsed data, the server queries a database of laws and regulations to find relevant existing laws and regulations.

[1180] The server embeds acquired data and user input data into a template and uses a generative AI model (e.g., GPT-3) to generate drafts of laws and regulations. During this process, the wording and expression of the draft are adjusted based on sentiment data from the sentiment engine. For example, if a user expresses dissatisfaction, the draft's wording becomes more specific and detailed.

[1181] The generated draft is resent from the server to the user's device. Users can review the draft through a web form or application and edit it as needed. Once editing is complete, pressing the save button again will save the edited draft to the server.

[1182] The following are examples of specific prompt messages.

[1183] "(Input data): Purpose: Strengthening personal information protection; Scope of application: Financial institutions; Target: Management of personal data; Specific regulations: Clear consent is required for the collection and use of personal data; (Emotion): Dissatisfaction"

[1184] "(Input data): Purpose: Promotion of environmental protection; Scope of application: All companies; Target: Waste management; Specific regulations: Proper disposal of waste; (Emotion) Joy"

[1185] This system enables the generation of appropriate draft laws and regulations that reflect user sentiment, providing more accurate legal documents. Furthermore, it allows for smooth editing of documents to suit user intent, contributing to the digitalization and efficiency of legal procedures.

[1186] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1187] Step 1:

[1188] Users access the system using devices such as personal computers or smartphones. Through a dedicated web form or application, users input information such as the "purpose of the regulation," "scope of application," "subjects covered," and "specific regulatory content." During this input process, an emotion engine performs facial recognition of the user and identifies the user's emotions through text analysis. This process generates user input data and emotion data, which are then passed on to the next step.

[1189] Step 2:

[1190] The terminal packages the input data and sentiment data into JSON or XML format and sends it to the server using an HTTP POST request. Specifically, it sets values ​​for each field (purpose, scope, target items, specific regulations, and sentiment information) in a JSON object and includes it in the body of the HTTP request. As a result, the server receives this data.

[1191] Step 3:

[1192] The server analyzes the received data. The data analysis module parses the data in JSON format and extracts each field (purpose, scope, subject matter, specific regulations, and sentiment information). Based on this, the server prepares the data to send queries to databases related to laws and regulations in the next step.

[1193] Step 4:

[1194] Based on the analysis results, the server executes queries against a database of laws and regulations. Specifically, a query generation module executes the generated queries against the legal database to search for existing laws and regulations related to fields such as "personal information protection" and "financial institutions." As a result of this query execution, the relevant legal data is returned to the server.

[1195] Step 5:

[1196] The server embeds acquired data and user input data into a template and uses a generative AI model (e.g., GPT-3) to generate drafts of laws and regulations. This process adjusts the wording and phrasing of the draft based on sentiment data acquired from an emotion engine. The generative AI model references the template and forms the draft document based on user-provided data and sentiment data. As a result, a completed draft is generated.

[1197] Step 6:

[1198] The server sends the generated draft to the terminal in JSON or plain text format. The terminal displays the received draft within the application. Users can review the draft through web forms or applications and edit it as needed. The user's edits are sent back to the server for saving. This process ensures that the legal document best reflects the user's intent.

[1199] (Application Example 2)

[1200] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1201] Conventional regulatory draft generation systems often failed to adequately reflect user intentions because they did not consider user sentiment. Furthermore, the generated drafts were often formal and did not address user dissatisfaction in terms of specific content or expression. This resulted in low user satisfaction and a need for improved quality in the generated drafts.

[1202] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1203] In this invention, the server includes means for analyzing user input data and sentiment data, means for searching a database for relevant existing regulatory data, and means for generating a draft of regulations based on the search results and sentiment data. This makes it possible to generate an appropriate draft of regulations that takes user sentiment into consideration.

[1204] A "user" is an entity that utilizes the system, and is a person or group that inputs data for a specific purpose.

[1205] A "regulation" is a law, ordinance, or rule enacted to achieve a specific purpose.

[1206] "Input data" refers to information provided by the user to the system, including the purpose of the regulation, its scope, the matters covered, and the specific content of the regulation.

[1207] "Emotional data" refers to data that indicates the user's emotions at the time of input, and is extracted by analyzing emotional states such as joy, anger, and sadness.

[1208] A "server" is a computing system that receives data sent by users, analyzes it, and generates draft regulations.

[1209] A "database" is an information aggregation system that stores relevant existing regulatory data and allows servers to perform searches.

[1210] "Searching" is the act of a server retrieving information related to user input data and sentiment data from a database.

[1211] "Natural language generation" refers to algorithms or techniques for generating draft regulations based on analyzed data.

[1212] A "template" is a standard document format used in the generation of draft regulations.

[1213] The system for implementing this invention first provides a means for the user to input data including the purpose of the regulation, its scope, the matters covered, and the specific content of the regulation. The user accesses a dedicated web form or application using a terminal such as a smartphone or personal computer and inputs the data. At the same time, the user's sentiment data is also collected. The sentiment data is collected by the smartphone's camera and microphone and analyzed in real time. Software such as OpenCV or TensorFlow is used for this sentiment analysis.

[1214] Input data and sentiment data are packaged in JSON or XML format and sent to the server using the HTTPS protocol. The transmitted data is received and analyzed on the server. The server extracts the "purpose of the regulation," "scope of application," "targeted matters," "specific regulatory content," and "sentiment data" from the analyzed data.

[1215] Based on the extracted data, the server interacts with the database to search for relevant existing regulatory data. Based on this search, the server embeds the retrieved data into a template and generates a draft of the regulation using a natural language generation (NLG) algorithm. At this time, the wording of the draft is adjusted to align with the user's intentions based on sentiment data. For example, if the user has the emotion of "dissatisfaction," the wording of the draft is made more specific and clear to best reflect the user's intentions.

[1216] The generated draft is sent back to the user's device, where the user can review and edit it. The editing function is designed to allow the user to modify the draft content as needed and save it again to the server.

[1217] As a specific example, if a user enters information with the purpose of "protecting personal information in financial institutions," the following prompt message will be generated.

[1218] The user entered the data targeting financial institutions with the aim of "strengthening personal information protection." The sentiment data indicates "dissatisfaction."

[1219] Input data:

[1220] Objective: To strengthen the protection of personal information.

[1221] Scope of application: Financial institutions

[1222] Topics covered: Management of personal data

[1223] Regulations: Explicit consent is required for the collection and use of personal data.

[1224] User sentiment: Dissatisfaction

[1225] Prompt message:

[1226] "The user feels frustration. Draft an appropriate response for strengthening personal data protection within financial institutions."

[1227] This allows the system to quickly and accurately generate regulatory drafts that reflect user intentions and sentiments, providing users with more satisfying results.

[1228] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1229] Step 1:

[1230] Users access a dedicated web form or application and enter the purpose, scope, subject matter, and specific details of the regulation. The entered data is saved on the device in text format, and sentiment data is collected in real time using the camera and microphone. Sentiment data is extracted through image analysis and voice analysis.

[1231] Input: User input data (text format), user's facial image, and audio.

[1232] Output: Input data and sentiment data

[1233] Step 2:

[1234] The device packages input data and sentiment data in JSON format and sends it to the server using the HTTPS protocol. Encryption is applied to enhance security during transmission.

[1235] Input: Input data, sentiment data

[1236] Output: Data package in JSON format

[1237] Step 3:

[1238] The server receives the transmitted data in JSON format and performs analysis. The analysis extracts the "purpose of the regulation," "scope of application," "targeted matters," "specific regulatory details," and "sentiment data" from the input data. This analysis is performed to facilitate database searching.

[1239] Input: Data package in JSON format

[1240] Output: Extracted input data and sentiment data

[1241] Step 4:

[1242] Based on the data extracted by the server, relevant existing regulatory data is searched for in the database. SQL queries are used for the search to identify database entries related to the regulations.

[1243] Input: Extracted input data and sentiment data

[1244] Output: Search results (existing regulatory data)

[1245] Step 5:

[1246] The server uses search results and sentiment data to apply a natural language generation (NLG) algorithm to generate a draft of the regulations. During this process, the draft text is refined based on sentiment data, selecting expressions that align with user preferences.

[1247] Input: Search results (existing regulatory data), sentiment data

[1248] Output: Generated regulatory draft

[1249] Step 6:

[1250] The server packages the generated draft back into JSON format and sends it to the user's terminal. An editing function for the draft is also provided at this time.

[1251] Input: Generated regulatory draft

[1252] Output: Draft data in JSON format

[1253] Step 7:

[1254] The user's device displays the received draft and makes it editable. The user reviews the draft and makes corrections to complete the final regulations. The edits are then saved back to the server.

[1255] Input: Draft data in JSON format

[1256] Output: Edited regulatory draft

[1257] Step 8:

[1258] Once the user finishes editing and presses the save button, the device sends the edited data back to the server. The server then saves it to the database as the final regulatory draft.

[1259] Input: Edited regulatory draft

[1260] Output: Saved final regulatory draft

[1261] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1262] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1263] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1264] [Fourth Embodiment]

[1265] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1266] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1267] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1268] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1269] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1270] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1271] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1272] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1273] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1274] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1275] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1276] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1277] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1278] User input

[1279] Users access the system using their own devices (e.g., personal computers or smartphones). Through a dedicated web form or application, users input necessary information such as the "purpose of the regulation," "scope of application," "subjects covered," and "specific regulatory details." The input is in text field format, allowing users to operate it intuitively.

[1280] Sending input data

[1281] Once input is complete, the terminal sends the entered data to the server. The data is packaged in JSON or XML format and sent using an HTTP POST request.

[1282] Specific example:

[1283] If a user enters data for the purpose of "protecting personal information in financial institutions," that data will be sent to the server.

[1284] {

[1285] "Essential": "Strengthening of personal information protection",

[1286] "scope": "Financial Institutions",

[1287] "subjects": "Management of personal data",

[1288] "regulation_content": "Explicit consent is required for the collection and use of personal data."

[1289] }

[1290] Interaction with the database

[1291] Upon receiving the transmitted data, the server analyzes it and queries existing laws and regulations that relate to the regulations. The server queries the database of legal information and retrieves the relevant data.

[1292] Specific example:

[1293] Search existing laws and regulations concerning "personal information protection" and retrieve relevant articles and regulations.

[1294] Draft generation

[1295] The server generates drafts of laws and regulations using natural language generation (NLG) algorithms based on acquired data and user input data. This is done by dynamically embedding user input data into specific fields using a template engine. The generated drafts meet legally required formats and structures and are provided in a user-friendly format.

[1296] Specific draft generation process:

[1297] Template:

[1298] Chapter 1 General Provisions

[1299] 1. This law aims to {{ purpose}}.

[1300] 2. The scope of application shall be {{ scope}}.

[1301] Chapter 2: Regulations

[1302] 1. Regarding {{subjects}}, {{regulation_content}}.

[1303] Generated draft:

[1304] Chapter 1 General Provisions

[1305] 1. This law aims to strengthen the protection of personal information.

[1306] 2. The scope of application shall be financial institutions.

[1307] Chapter 2: Regulations

[1308] 1. Regarding the management of personal data, explicit consent is required for the collection and use of personal data.

[1309] Viewing and editing results

[1310] The generated draft is sent back to the user's device. The user can review this draft within the web form or application and edit it as needed. Once editing is complete, pressing the save button again will save the edited draft to the server.

[1311] In this way, the system of the present invention quickly and accurately generates drafts of laws and regulations, and provides an environment in which users can easily create and edit legal documents. This enables the digitalization and efficiency of legal infrastructure.

[1312] The following describes the processing flow.

[1313] Step 1:

[1314] Users enter input data, including the purpose, scope, subject matter, and specific content of the regulations, into a dedicated web form or application text field.

[1315] Step 2:

[1316] The terminal packages the input data in JSON or XML format and sends it to the server using an HTTP POST request.

[1317] Step 3:

[1318] The server analyzes the received data and extracts each field (purpose, scope of application, subject matter, and specific regulatory content).

[1319] Step 4:

[1320] Based on the data analyzed by the server, queries are executed against a database of laws and regulations to search for relevant existing laws and ordinances.

[1321] Step 5:

[1322] The server retrieves relevant laws and regulations from the database and matches that data against the appropriate template.

[1323] Step 6:

[1324] The server embeds acquired data and user input data into a template, and uses a natural language generation (NLG) algorithm to generate drafts of laws and regulations.

[1325] Step 7:

[1326] The server sends the generated draft to the user's terminal.

[1327] Step 8:

[1328] The terminal displays the generated draft. Users can review the draft within the web form or application and edit it as needed.

[1329] Step 9:

[1330] Once the user finishes editing and presses the "Save" button, the edited draft is sent back to the server.

[1331] Step 10:

[1332] The server saves the final draft and performs additional processing as needed to prepare it as a legal document.

[1333] (Example 1)

[1334] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1335] Traditionally, drafting rules and standards was a complex, time-consuming, and specialized process. Furthermore, ensuring consistency with existing laws and regulations required significant resources, and manual methods were prone to errors. Additionally, there were limited means to effectively utilize user-generated data and quickly generate legal documents. Consequently, meeting the high efficiency and accuracy demands of modern times proved difficult.

[1336] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1337] In this invention, the server includes means for the user to input input data including the purpose of the regulation, scope of application, matters covered, and specific content of the regulation; means for transmitting the input data to the server; means for the server to analyze the input data and search a database for relevant existing rules and standards data; means for the server to generate a draft of the rules and standards based on the search results; means for displaying the generated draft on the user's terminal; means for the user to review the generated draft and edit it within a web form or application; and means for transmitting the edited draft back to the server and saving it in the database. This enables the rapid and accurate generation of draft rules and standards based on data intuitively entered by the user, and allows for the creation of high-quality legal documents through repeated editing.

[1338] A "user" refers to a person who accesses the system and enters the purpose of the regulation, its scope, the matters covered, and the specific content of the regulation.

[1339] "Input data" refers to information that users input into the system, such as the purpose, scope, subject matter, and specific content of the regulations.

[1340] A "terminal" is a device used by a user to access a system, and includes personal computers, smartphones, and other similar devices.

[1341] A "server" refers to a computer system that receives input data sent from a user's terminal, analyzes it, and generates draft rules and standards in conjunction with a database.

[1342] A "database" is a collection of information that stores existing data on rules and standards, making it searchable by a server using queries.

[1343] "Natural language generation means" refers to algorithms and software for automatically generating draft rules and standards based on input data and information obtained from databases.

[1344] A "draft" refers to an initial version of a legal document containing generated rules or standards, provided in a format that allows users to review and edit it.

[1345] "Display means" refers to the functions and interfaces for displaying the generated draft on the user's terminal.

[1346] "Editing means" refers to the functions and interfaces that allow users to review the generated draft and make corrections and edits as needed.

[1347] "Storage method" refers to the function that sends the user-edited draft back to the server and saves it in the database.

[1348] For this invention to be implemented, it is crucial that the user, terminal, and server each fulfill their respective roles. The following describes how this system will be implemented in detail.

[1349] First, the user accesses a dedicated web form or application using a device (e.g., a PC or smartphone). Using this interface, the user enters necessary information such as the "purpose of the regulation," "scope of application," "matters covered," and "specific regulatory content" in text fields. The input process is designed to be intuitive for the user.

[1350] Once the user enters the necessary information, the terminal packages it in JSON or XML format and sends it to the server using an HTTP POST request. For example, if the following input is given:

[1351] Purpose of regulation: To enhance the protection of personal information.

[1352] Scope of application: Financial institutions

[1353] Topics covered: Management of personal data

[1354] Specific regulations: Explicit consent is required for the collection and use of personal data.

[1355] The server receives the transmitted data in JSON or XML format and parses it. It then queries the rules and standards database to find relevant existing laws and standards. The data obtained as a result of the search is combined with the information entered by the user.

[1356] Next, the server uses a natural language generation (NLG) algorithm to generate drafts of laws and standards based on the acquired data and user input data. This draft generation uses a template engine, dynamically filling in user input data into specific fields. For example, the following template is used:

[1357] Chapter 1 General Provisions

[1358] 1. This law aims to {{ purpose}}.

[1359] 2. The scope of application shall be {{ scope}}.

[1360] Chapter 2: Regulations

[1361] 1. Regarding {{subjects}}, {{regulation_content}}.

[1362] The draft generated based on this will look like this:

[1363] Chapter 1 General Provisions

[1364] 1. This law aims to strengthen the protection of personal information.

[1365] 2. The scope of application shall be financial institutions.

[1366] Chapter 2: Regulations

[1367] 1. Regarding the management of personal data, explicit consent is required for the collection and use of personal data.

[1368] The generated draft is resent from the server to the user's device. The user can review this draft within a web form or application and edit it as needed. Once editing is complete, the user presses the save button, and the edited draft is sent back to the server and saved to the database.

[1369] Through this procedure, this invention can quickly and accurately generate drafts of rules and standards, and provide an environment in which users can easily create and edit legal documents. This leads to increased efficiency and digitalization of legal work.

[1370] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1371] Step 1: User Input

[1372] Users access the system using a terminal. They enter the "purpose of the regulation," "scope of application," "subjects covered," and "specific regulatory details" in text fields via a dedicated web form or application. For example:

[1373] Purpose of regulation: To enhance the protection of personal information.

[1374] Scope of application: Financial institutions

[1375] Topics covered: Management of personal data

[1376] Specific regulations: Explicit consent is required for the collection and use of personal data.

[1377] Once the user has finished entering information into all the fields, they click the "Submit" button in the form.

[1378] Step 2: Submit the input data

[1379] The terminal converts the data entered by the user into JSON or XML format and sends it to the server using an HTTP POST request. For example, the following JSON data is generated:

[1380] {

[1381] "Essential": "Strengthening of personal information protection",

[1382] "scope": "Financial Institutions",

[1383] "subjects": "Management of personal data",

[1384] "regulation_content": "Explicit consent is required for the collection and use of personal data."

[1385] }

[1386] The device performs the specific action of sending this JSON data to the server.

[1387] Step 3: Analyze the input data

[1388] The server receives the transmitted JSON data and performs parsing. Here, it reads each field and converts it into a data structure. For example, the data corresponding to the "purpose" field is interpreted as "Enhancing personal information protection." Based on this parsing result, the server constructs its internal data structure.

[1389] Step 4: Execute database query

[1390] The server queries a database of rules and standards based on the analyzed data. For example, it generates a query to search for existing regulations on "personal data protection" and sends it to the database. The database returns data containing the relevant existing regulations. Specifically, this involves generating and executing SQL queries.

[1391] Step 5: Draft Generation

[1392] The server combines existing legal data retrieved from the database with user input data and generates a draft using a natural language generation (NLG) algorithm. A template engine is used to dynamically populate specific fields with user data. This process utilizes an AI model to generate the appropriate document structure. For example, the following template is used:

[1393] Chapter 1 General Provisions

[1394] 1. This law aims to {{ purpose}}.

[1395] 2. The scope of application shall be {{ scope}}.

[1396] Chapter 2: Regulations

[1397] 1. Regarding {{subjects}}, {{regulation_content}}.

[1398] The draft generated based on this would look like this:

[1399] Chapter 1 General Provisions

[1400] 1. This law aims to strengthen the protection of personal information.

[1401] 2. The scope of application shall be financial institutions.

[1402] Chapter 2: Regulations

[1403] 1. Regarding the management of personal data, explicit consent is required for the collection and use of personal data.

[1404] Step 6: Submit the generated draft

[1405] The generated draft is sent again from the server to the user's terminal. The server packages this draft as an HTTP response and sends it to the terminal. This allows the user to review the generated draft.

[1406] Step 7: Editing the draft

[1407] Users review the generated draft on their device and edit it as needed. This editing process takes place within a web form or application, allowing users to modify the draft's content. After making changes, the user clicks the "Save" button.

[1408] Step 8: Save the edited draft

[1409] When the user clicks the save button, the device resends the edited draft to the server. The server receives the submitted edited draft and saves it to the database again. In this specific operation, the JSON data is parsed and the appropriate database entry is updated.

[1410] This processing flow allows the system to efficiently and accurately generate draft rules and standards, providing an environment where users can easily edit them.

[1411] (Application Example 1)

[1412] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1413] In today's digital society, users need to create and edit legal documents and product pages quickly and accurately. However, creating these documents requires specialized knowledge, and understanding and editing them is time-consuming. An efficient system is needed to solve this problem.

[1414] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1415] In this invention, the server includes means for the user to input input data including the purpose, scope, subject matter, and specific content of the regulation; means for transmitting the input data to the server; means for the server to analyze the input data and search a database for relevant existing laws and regulations; means for the server to generate a draft of the law or regulation based on the search results; means for displaying the generated draft on the user's terminal; means for the user to input product name, price, description, and ingredient information; means for transmitting the input product data to the server; means for the server to search a database for relevant product information based on the product data; and means for the server to display the generated product page draft on the user's terminal. This makes it possible to easily generate not only drafts of laws and regulations but also product pages.

[1416] A "user" is an end-user who accesses and operates a system or application.

[1417] The "purpose of regulation" refers to the specific goals and directions that the established regulation should achieve.

[1418] "Scope of application" refers to the specific region, organization, or situation to which the regulation or law applies.

[1419] "Subject matter" refers to specific matters, activities, or areas that are subject to regulation.

[1420] "Specific regulations" refer to actions, conditions, or prohibitions specifically defined in regulations or laws.

[1421] "Input data" refers to data that includes information and content provided by the user to the system.

[1422] "Means" refers to the methods, tools, and processes used to achieve a specific purpose or function.

[1423] A "server" refers to a central system that receives requests from users and processes and provides data.

[1424] "Natural language generation" refers to the technology that allows computers to generate documents and texts in human language.

[1425] "Product name" refers to the name or label used to identify a specific product.

[1426] "Price" refers to the amount or cost set for a product or service.

[1427] A "description" refers to text that describes detailed information and features about a product or service.

[1428] "Ingredient information" refers to a list of materials and ingredients contained in a particular product, especially food and cosmetics.

[1429] A "database" refers to a computer system for efficiently storing, searching, and managing structured information.

[1430] A "product page draft" refers to the initial version or first draft of a page that allows users to view information about a product.

[1431] The system for implementing this invention is one in which a user inputs data using their own terminal, and a server analyzes and processes that data to generate and display drafts of laws and regulations and draft product pages.

[1432] Specifically, users use their devices to input data such as "purpose of regulation," "scope of application," "subjects covered," and "specific regulatory content" via web forms or applications. Similarly, users input data including "product name," "price," "description," and "ingredient information" to generate product pages. This input data is entered through a text-field interface designed for intuitive user interaction.

[1433] The input data is structured in JSON or XML format and sent to the server using an HTTP POST request. The server parses the received data and searches the legal database for relevant laws and regulations. Similarly, for product data, the server searches the database for relevant product information after receiving the data.

[1434] The server generates drafts using natural language generation (NLG) algorithms based on search results. Leveraging a template engine, it dynamically embeds user input data into specific fields, ensuring the generated drafts meet the format and structure requirements of legal or product pages. Not only are legal and regulatory drafts generated, but product page drafts are also templated and provided in a format easily understandable to users.

[1435] The generated draft is sent back to the user's device, where they can review and edit it within a web form or application. Once editing is complete, pressing the save button again saves the edited draft to the server. This system provides an environment for the rapid and accurate creation and editing of legal / regulation drafts and product page drafts.

[1436] Hardware and software to use

[1437] Hardware: User terminals (e.g., personal computers, smartphones), servers

[1438] Software: Flask (web framework), Jinja2 (template engine), requests (HTTP request library)

[1439] Specific example

[1440] User input:

[1441] Purpose of regulation: To enhance the protection of personal information.

[1442] Scope of application: Financial institutions

[1443] Topics covered: Management of personal data

[1444] Specific regulations: Explicit consent is required for the collection and use of personal data.

[1445] Product Name: Premium Black Tea

[1446] Price: 1200

[1447] Description: This is a fragrant black tea made with high-quality tea leaves.

[1448] Ingredient information: black tea leaves, fragrance

[1449] Example of a prompt

[1450] Input prompt: "Please enter the purpose, scope, subject matter, and specific details of the regulation. Also, please enter the product name, price, description, and ingredient information."

[1451] Prompt message for generated draft: "The following draft laws / regulations and product page drafts have been generated. You can edit the content."

[1452] This system allows users to easily create legal documents and product pages without requiring specialized knowledge.

[1453] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1454] Step 1:

[1455] Users input the purpose, scope, and subject matter of the regulation, as well as specific regulatory details, or product name, price, description, and ingredient information through their user terminal. The input data is structured in text field format and is entered via an intuitive web form or application.

[1456] Step 2:

[1457] The user terminal packages the entered data in JSON or XML format and sends it to the server using an HTTP POST request. In this case, the user's input data is sent to the server as the body of the HTTP request.

[1458] Step 3:

[1459] The server analyzes the received data. Specifically, it extracts data about regulations and products entered by the user and stores it as structured information. For example, input data about regulations is analyzed as follows:

[1460] {

[1461] "Essential": "Strengthening of personal information protection",

[1462] "scope": "Financial Institutions",

[1463] "subjects": "Management of personal data",

[1464] "regulation_content": "Explicit consent is required for the collection and use of personal data."

[1465] }

[1466] Product data is analyzed in the same manner.

[1467] {

[1468] "product_name": "Premium Black Tea",

[1469] "price": 1200,

[1470] "Description": "A fragrant black tea made with high-quality tea leaves."

[1471] "ingredients": "Tea leaves, fragrances"

[1472] }

[1473] Step 4:

[1474] Based on the analyzed data, the server searches the database for relevant legal data or product information. For example, it searches and queries existing laws and regulations related to "personal information protection" or existing product information related to "tea." The relevant legal data is retrieved as follows:

[1475] [

[1476] {

[1477] "law": "Personal Information Protection Law",

[1478] "clause": "Article 12",

[1479] "Description": "Explicit consent is required for the collection of personal data."

[1480] }

[1481] ]

[1482] Product information is retrieved in the same manner.

[1483] [

[1484] {

[1485] "name": "Tea A",

[1486] "price": 1000,

[1487] "description": "Fragrant Black Tea A"

[1488] }

[1489] ]

[1490] Step 5:

[1491] The server uses a natural language generation (NLG) algorithm based on the acquired data to generate drafts of laws, regulations, or product pages. The generated drafts utilize a template engine (e.g., Jinja2). User input data is dynamically embedded into the template and formatted as a draft. The generated draft will look like this:

[1492] Chapter 1 General Provisions

[1493] 1. This law aims to strengthen the protection of personal information.

[1494] 2. The scope of application shall be financial institutions.

[1495] Chapter 2: Regulations

[1496] 1. Regarding the management of personal data, explicit consent is required for the collection and use of personal data.

[1497] Product Name: Premium Black Tea

[1498] Price: ¥1200

[1499] Description: This is a fragrant black tea made with high-quality tea leaves.

[1500] Ingredient information: black tea leaves, fragrance

[1501] Related products:

[1502] Black Tea A - ¥1000: Aromatic Black Tea A

[1503] Step 6:

[1504] The generated draft is sent back to the user's terminal in JSON or HTML format. The user's terminal displays the received draft in the user interface, allowing the user to review and edit it. The user edits the draft and sends it back to the server by pressing the save button.

[1505] Step 7:

[1506] The draft edited by the user is sent to the server and ultimately stored in the database. This allows users to efficiently generate and edit high-quality drafts of laws and regulations, as well as product pages.

[1507] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1508] User input and emotion recognition

[1509] Users access the system using a device (e.g., a personal computer or smartphone). Through a dedicated web form or application, users input necessary information such as the "purpose of the regulation," "scope of application," "subjects covered," and "specific regulatory content." Simultaneously, an emotion engine analyzes the user's emotions (e.g., joy, anger, sadness) in real time. The emotion engine identifies emotions based on the user's facial recognition and text analysis.

[1510] Sending input data and emotion data

[1511] Once input is complete, the device sends the entered data, along with sentiment data, to the server. The data is packaged in JSON or XML format and sent using an HTTP POST request.

[1512] Specific example:

[1513] When a user enters data for the purpose of "protecting personal information in financial institutions," that data is sent to the server in the following format.

[1514] {

[1515] "Essential": "Strengthening of personal information protection",

[1516] "scope": "Financial Institutions",

[1517] "subjects": "Management of personal data",

[1518] "regulation_content": "Explicit consent is required for the collection and use of personal data",

[1519] "emotion": "frustration" / / Example of emotion

[1520] }

[1521] Interaction with the database and consideration of emotions

[1522] Upon receiving the transmitted data, the server analyzes it and extracts each field (purpose, scope, subject matter, specific regulations, and sentiment information). Based on the analyzed data, the server queries a database of laws and regulations, seeking relevant existing laws and ordinances.

[1523] Specific example:

[1524] Search existing laws and regulations concerning "personal information protection" and retrieve relevant articles and regulations.

[1525] Draft generation and emotional response

[1526] The server embeds acquired data and user input data into a template and generates drafts of laws and regulations using a natural language generation (NLG) algorithm. During this process, the wording and expression of the draft are adjusted based on sentiment data from the sentiment engine. For example, if a user expresses dissatisfaction, the draft's expression is made more specific and clear to best reflect the user's intent.

[1527] Specific draft generation process:

[1528] Template:

[1529] Chapter 1 General Provisions

[1530] 1. This law aims to {{ purpose}}.

[1531] 2. The scope of application shall be {{ scope}}.

[1532] Chapter 2: Regulations

[1533] 1. Regarding {{subjects}}, {{regulation_content}}.

[1534] Generated draft:

[1535] Chapter 1 General Provisions

[1536] 1. This law aims to strengthen the protection of personal information.

[1537] 2. The scope of application shall be financial institutions.

[1538] Chapter 2: Regulations

[1539] 1. Regarding the management of personal data, explicit consent is required for the collection and use of personal data. In particular, the user's wishes must not be disregarded.

[1540] Viewing and editing results

[1541] The generated draft is sent back to the user's device. The user can review this draft within the web form or application and edit it as needed. Once editing is complete, pressing the save button again will save the edited draft to the server.

[1542] In this way, the system of the present invention provides an environment in which drafts of laws and regulations can be generated quickly and accurately, and which can create and edit legal documents with higher precision by taking into account the user's feelings. This enables the digitalization and efficiency of legal infrastructure.

[1543] The following describes the processing flow.

[1544] Step 1:

[1545] The user enters the purpose, scope, target items, and specific details of the regulation into a dedicated web form or application text field. Simultaneously, an emotion engine monitors the user's facial expressions and typing speed, analyzing their emotions in real time.

[1546] Step 2:

[1547] The terminal packages the emotion data obtained from the emotion engine, along with the entered restriction information, in JSON or XML format and sends it to the server using an HTTP POST request.

[1548] Step 3:

[1549] The server analyzes the received regulatory data and sentiment data, and extracts each field (purpose, scope of application, subject matter, specific regulatory content, and sentiment information).

[1550] Step 4:

[1551] Based on the data analyzed by the server, queries are executed against a database of laws and regulations to search for relevant existing laws and regulations. For example, past laws and regulations related to "personal information protection" may be extracted.

[1552] Step 5:

[1553] The server retrieves relevant laws and regulations from the database and compares them with a template, then generates a draft that also takes sentiment information into account. By using the sentiment engine, if a user expresses "dissatisfaction," more specific language and additional explanations will be included.

[1554] Step 6:

[1555] The server sends the generated draft to the user's device. The draft incorporates adjustments based on sentiment data.

[1556] Specific example:

[1557] Template:

[1558] Chapter 1 General Provisions

[1559] 1. This law aims to {{ purpose}}.

[1560] 2. The scope of application shall be {{ scope}}.

[1561] Chapter 2: Regulations

[1562] 1. Regarding {{subjects}}, {{regulation_content}}.

[1563] Adjustments during draft generation:

[1564] Chapter 1 General Provisions

[1565] 1. This law aims to strengthen the protection of personal information.

[1566] 2. The scope of application shall be financial institutions.

[1567] Chapter 2: Regulations

[1568] 1. Regarding the management of personal data, explicit consent is required for the collection and use of personal data. In particular, the user's wishes must not be disregarded.

[1569] Step 7:

[1570] The terminal displays the generated draft. Users can review the draft within the web form or application and edit it as needed.

[1571] Step 8:

[1572] The user finishes editing the draft and presses the "Save" button to resubmit the edited draft to the server.

[1573] Step 9:

[1574] The server saves the final draft and performs any additional processing necessary for further legal refinement.

[1575] (Example 2)

[1576] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1577] Conventional systems for generating drafts of laws and regulations generate them based on user input data, but their accuracy and suitability are limited. Furthermore, because document generation does not take user emotions into consideration, it is difficult to create legal documents that fully reflect the user's intentions and feelings. As a result, users are often dissatisfied when generating drafts of laws and regulations, leading to a decrease in work efficiency.

[1578] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user to input input data including the purpose of regulation, scope of application, matters to be covered, and specific content of regulation; means for transmitting the input data and the user's sentiment data to the server; means for the server to analyze the input data and sentiment data and search for relevant existing laws and regulations data from a database; natural language generation means for the server to generate a draft of the law or regulation based on the search results and the user's sentiment data; and means for displaying the generated draft on the user's terminal. This makes it possible to generate an appropriate draft of the law or regulation that reflects the user's sentiment.

[1579] A "user" refers to an individual or legal entity that uses the system to generate drafts of laws and regulations.

[1580] "Input data" refers to data entered by the user that includes the purpose, scope, subject matter, and specific content of the regulations.

[1581] "Emotional data" refers to information about the user's emotions (e.g., joy, anger, sadness, etc.) detected during the user's input process.

[1582] A "server" refers to a computer system that receives and analyzes data sent by users and generates drafts of laws and regulations.

[1583] "Natural language generation means" refers to a function or algorithm that automatically generates drafts of laws and regulations using natural language based on input data.

[1584] A "template" refers to a framework containing standard phrases used to draft laws and regulations.

[1585] A "database" refers to an information storage system that stores and makes searchable information related to laws and regulations.

[1586] A "terminal" refers to an electronic device (such as a personal computer or smartphone) that a user uses to access a system, input data, or view and edit generated drafts.

[1587] A "draft" refers to a draft document of a law or ordinance that is automatically generated by a generative AI model.

[1588] "Means of input" refers to the interface or method by which a user supplies input data to the system.

[1589] "Means of transmission" refers to the means of communication used to send input data and emotional data from a terminal to a server.

[1590] "Means of analysis" refers to algorithms and processes used by a server to analyze received data and extract necessary information.

[1591] "Means of display" refers to the interface or method for displaying the generated draft on the user's device.

[1592] The system of this invention is for the rapid and accurate generation of drafts of laws and regulations, and includes various means for user input, sentiment recognition, data analysis, draft generation, display, and editing. Specific embodiments of the system are described below.

[1593] Users access the system using devices such as personal computers or smartphones. They input necessary information, including the "purpose of the regulation," "scope of application," "subjects covered," and "specific regulatory content," via a dedicated web form or application. During this input process, the emotion engine analyzes the user's emotions in real time. The emotion engine is a software module that identifies emotions through facial recognition and text analysis.

[1594] Once input is complete, the terminal sends the entered data and sentiment data to the server. The data is packaged in JSON or XML format and sent using an HTTP POST request. The server parses the received data and extracts each field (purpose, scope, subject matter, specific regulations, and sentiment information). Based on the parsed data, the server queries a database of laws and regulations to find relevant existing laws and regulations.

[1595] The server embeds acquired data and user input data into a template and uses a generative AI model (e.g., GPT-3) to generate drafts of laws and regulations. During this process, the wording and expression of the draft are adjusted based on sentiment data from the sentiment engine. For example, if a user expresses dissatisfaction, the draft's wording becomes more specific and detailed.

[1596] The generated draft is resent from the server to the user's device. Users can review the draft through a web form or application and edit it as needed. Once editing is complete, pressing the save button again will save the edited draft to the server.

[1597] The following are examples of specific prompt messages.

[1598] "(Input data): Purpose: Strengthening personal information protection; Scope of application: Financial institutions; Target: Management of personal data; Specific regulations: Clear consent is required for the collection and use of personal data; (Emotion): Dissatisfaction"

[1599] "(Input data): Purpose: Promotion of environmental protection; Scope of application: All companies; Target: Waste management; Specific regulations: Proper disposal of waste; (Emotion) Joy"

[1600] This system enables the generation of appropriate draft laws and regulations that reflect user sentiment, providing more accurate legal documents. Furthermore, it allows for smooth editing of documents to suit user intent, contributing to the digitalization and efficiency of legal procedures.

[1601] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1602] Step 1:

[1603] Users access the system using devices such as personal computers or smartphones. Through a dedicated web form or application, users input information such as the "purpose of the regulation," "scope of application," "subjects covered," and "specific regulatory content." During this input process, an emotion engine performs facial recognition of the user and identifies the user's emotions through text analysis. This process generates user input data and emotion data, which are then passed on to the next step.

[1604] Step 2:

[1605] The terminal packages the input data and sentiment data into JSON or XML format and sends it to the server using an HTTP POST request. Specifically, it sets values ​​for each field (purpose, scope, target items, specific regulations, and sentiment information) in a JSON object and includes it in the body of the HTTP request. As a result, the server receives this data.

[1606] Step 3:

[1607] The server analyzes the received data. The data analysis module parses the data in JSON format and extracts each field (purpose, scope, subject matter, specific regulations, and sentiment information). Based on this, the server prepares the data to send queries to databases related to laws and regulations in the next step.

[1608] Step 4:

[1609] Based on the analysis results, the server executes queries against a database of laws and regulations. Specifically, a query generation module executes the generated queries against the legal database to search for existing laws and regulations related to fields such as "personal information protection" and "financial institutions." As a result of this query execution, the relevant legal data is returned to the server.

[1610] Step 5:

[1611] The server embeds acquired data and user input data into a template and uses a generative AI model (e.g., GPT-3) to generate drafts of laws and regulations. This process adjusts the wording and phrasing of the draft based on sentiment data acquired from an emotion engine. The generative AI model references the template and forms the draft document based on user-provided data and sentiment data. As a result, a completed draft is generated.

[1612] Step 6:

[1613] The server sends the generated draft to the terminal in JSON or plain text format. The terminal displays the received draft within the application. Users can review the draft through web forms or applications and edit it as needed. The user's edits are sent back to the server for saving. This process ensures that the legal document best reflects the user's intent.

[1614] (Application Example 2)

[1615] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1616] Conventional regulatory draft generation systems often failed to adequately reflect user intentions because they did not consider user sentiment. Furthermore, the generated drafts were often formal and did not address user dissatisfaction in terms of specific content or expression. This resulted in low user satisfaction and a need for improved quality in the generated drafts.

[1617] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1618] In this invention, the server includes means for analyzing user input data and sentiment data, means for searching a database for relevant existing regulatory data, and means for generating a draft of regulations based on the search results and sentiment data. This makes it possible to generate an appropriate draft of regulations that takes user sentiment into consideration.

[1619] A "user" is an entity that utilizes the system, and is a person or group that inputs data for a specific purpose.

[1620] A "regulation" is a law, ordinance, or rule enacted to achieve a specific purpose.

[1621] "Input data" refers to information provided by the user to the system, including the purpose of the regulation, its scope, the matters covered, and the specific content of the regulation.

[1622] "Emotional data" refers to data that indicates the user's emotions at the time of input, and is extracted by analyzing emotional states such as joy, anger, and sadness.

[1623] A "server" is a computing system that receives data sent by users, analyzes it, and generates draft regulations.

[1624] A "database" is an information aggregation system that stores relevant existing regulatory data and allows servers to perform searches.

[1625] "Searching" is the act of a server retrieving information related to user input data and sentiment data from a database.

[1626] "Natural language generation" refers to algorithms or techniques for generating draft regulations based on analyzed data.

[1627] A "template" is a standard document format used in the generation of draft regulations.

[1628] The system for implementing this invention first provides a means for the user to input data including the purpose of the regulation, its scope, the matters covered, and the specific content of the regulation. The user accesses a dedicated web form or application using a terminal such as a smartphone or personal computer and inputs the data. At the same time, the user's sentiment data is also collected. The sentiment data is collected by the smartphone's camera and microphone and analyzed in real time. Software such as OpenCV or TensorFlow is used for this sentiment analysis.

[1629] Input data and sentiment data are packaged in JSON or XML format and sent to the server using the HTTPS protocol. The transmitted data is received and analyzed on the server. The server extracts the "purpose of the regulation," "scope of application," "targeted matters," "specific regulatory content," and "sentiment data" from the analyzed data.

[1630] Based on the extracted data, the server interacts with the database to search for relevant existing regulatory data. Based on this search, the server embeds the retrieved data into a template and generates a draft of the regulation using a natural language generation (NLG) algorithm. At this time, the wording of the draft is adjusted to align with the user's intentions based on sentiment data. For example, if the user has the emotion of "dissatisfaction," the wording of the draft is made more specific and clear to best reflect the user's intentions.

[1631] The generated draft is sent back to the user's device, where the user can review and edit it. The editing function is designed to allow the user to modify the draft content as needed and save it again to the server.

[1632] As a specific example, if a user enters information with the purpose of "protecting personal information in financial institutions," the following prompt message will be generated.

[1633] The user entered the data targeting financial institutions with the aim of "strengthening personal information protection." The sentiment data indicates "dissatisfaction."

[1634] Input data:

[1635] Objective: To strengthen the protection of personal information.

[1636] Scope of application: Financial institutions

[1637] Topics covered: Management of personal data

[1638] Regulations: Explicit consent is required for the collection and use of personal data.

[1639] User sentiment: Dissatisfaction

[1640] Prompt message:

[1641] "The user feels frustration. Draft an appropriate response for strengthening personal data protection within financial institutions."

[1642] This allows the system to quickly and accurately generate regulatory drafts that reflect user intentions and sentiments, providing users with more satisfying results.

[1643] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1644] Step 1:

[1645] Users access a dedicated web form or application and enter the purpose, scope, subject matter, and specific details of the regulation. The entered data is saved on the device in text format, and sentiment data is collected in real time using the camera and microphone. Sentiment data is extracted through image analysis and voice analysis.

[1646] Input: User input data (text format), user's facial image, and audio.

[1647] Output: Input data and sentiment data

[1648] Step 2:

[1649] The device packages input data and sentiment data in JSON format and sends it to the server using the HTTPS protocol. Encryption is applied to enhance security during transmission.

[1650] Input: Input data, sentiment data

[1651] Output: Data package in JSON format

[1652] Step 3:

[1653] The server receives the transmitted data in JSON format and performs analysis. The analysis extracts the "purpose of the regulation," "scope of application," "targeted matters," "specific regulatory details," and "sentiment data" from the input data. This analysis is performed to facilitate database searching.

[1654] Input: Data package in JSON format

[1655] Output: Extracted input data and sentiment data

[1656] Step 4:

[1657] Based on the data extracted by the server, relevant existing regulatory data is searched for in the database. SQL queries are used for the search to identify database entries related to the regulations.

[1658] Input: Extracted input data and sentiment data

[1659] Output: Search results (existing regulatory data)

[1660] Step 5:

[1661] The server uses search results and sentiment data to apply a natural language generation (NLG) algorithm to generate a draft of the regulations. During this process, the draft text is refined based on sentiment data, selecting expressions that align with user preferences.

[1662] Input: Search results (existing regulatory data), sentiment data

[1663] Output: Generated regulatory draft

[1664] Step 6:

[1665] The server packages the generated draft back into JSON format and sends it to the user's terminal. An editing function for the draft is also provided at this time.

[1666] Input: Generated regulatory draft

[1667] Output: Draft data in JSON format

[1668] Step 7:

[1669] The user's device displays the received draft and makes it editable. The user reviews the draft and makes corrections to complete the final regulations. The edits are then saved back to the server.

[1670] Input: Draft data in JSON format

[1671] Output: Edited regulatory draft

[1672] Step 8:

[1673] Once the user finishes editing and presses the save button, the device sends the edited data back to the server. The server then saves it to the database as the final regulatory draft.

[1674] Input: Edited regulatory draft

[1675] Output: Saved final regulatory draft

[1676] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1677] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1678] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1679] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1680] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1681] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1682] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1683] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1684] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1685] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1686] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1687] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1688] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1689] 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.

[1690] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1691] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1692] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1693] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1694] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1695] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1696] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[1697] The following is further disclosed regarding the embodiments described above.

[1698] (Claim 1)

[1699] A means by which the user inputs data including the purpose of the regulation, its scope, the matters covered, and the specific content of the regulation,

[1700] A means of sending the input data to the server,

[1701] The server analyzes the input data and retrieves relevant existing laws and regulations from the database.

[1702] A natural language generation method in which a server generates drafts of laws and regulations based on search results,

[1703] A means of displaying the generated draft on the user's terminal,

[1704] A system that includes this.

[1705] (Claim 2)

[1706] The system according to claim 1, wherein drafts of laws and regulations are editable by users.

[1707] (Claim 3)

[1708] The system according to claim 1, which is linked to a database of laws and regulations and generates drafts using appropriate templates.

[1709] "Example 1"

[1710] (Claim 1)

[1711] A means by which the user inputs data including the purpose of the regulation, its scope, the matters covered, and the specific content of the regulation,

[1712] A means of sending the input data to the server,

[1713] The server analyzes the input data and retrieves relevant existing rules and standard data from the database.

[1714] A natural language generation method in which a server generates draft rules and standards based on search results,

[1715] A means of displaying the generated draft on the user's terminal,

[1716] A means for users to review the generated draft and edit it within a web form or application,

[1717] A means of sending the edited draft back to the server and saving it to the database,

[1718] A system that includes this.

[1719] (Claim 2)

[1720] The system according to claim 1, wherein draft rules and standards are editable by the user and can be saved after editing.

[1721] (Claim 3)

[1722] The system according to claim 1, which generates drafts by linking with a database of rules and standards and utilizing appropriate templates.

[1723] "Application Example 1"

[1724] (Claim 1)

[1725] A means by which the user inputs data including the purpose of the regulation, its scope, the matters covered, and the specific content of the regulation,

[1726] A means of sending the input data to the server,

[1727] The server analyzes the input data and retrieves relevant existing laws and regulations from the database.

[1728] A natural language generation method in which a server generates drafts of laws and regulations based on search results,

[1729] A means of displaying the generated draft on the user's terminal,

[1730] A means by which the user inputs product name, price, description, and ingredient information,

[1731] A means of sending the entered product data to the server,

[1732] A means for the server to retrieve relevant product information from the database based on product data,

[1733] A means by which the server generates a product page draft and displays it on the user's device,

[1734] A system that includes this.

[1735] (Claim 2)

[1736] The system according to claim 1, wherein drafts of laws and regulations are editable by users.

[1737] (Claim 3)

[1738] The system according to claim 1, which is linked to a database of laws and regulations and generates drafts using appropriate templates.

[1739] "Example 2 of combining an emotion engine"

[1740] (Claim 1)

[1741] A means by which the user inputs data including the purpose of the regulation, its scope, the matters covered, and the specific content of the regulation,

[1742] A means for sending input data and user sentiment data to a server,

[1743] The server analyzes input data and sentiment data, and retrieves relevant existing laws and regulations from a database.

[1744] A natural language generation method in which a server generates drafts of laws and regulations based on search results and user sentiment data,

[1745] A means of displaying the generated draft on the user's terminal,

[1746] A system that includes this.

[1747] (Claim 2)

[1748] The system according to claim 1, wherein drafts of laws and regulations are editable by users, and the edited drafts are stored on a server.

[1749] (Claim 3)

[1750] The system according to claim 1, which is linked to a database of laws and regulations and generates drafts based on user sentiment data by utilizing appropriate templates.

[1751] "Application example 2 when combining with an emotional engine"

[1752] (Claim 1)

[1753] A means by which the user inputs data including the purpose of the regulation, its scope, the matters covered, and the specific content of the regulation,

[1754] Means for transmitting input data and emotion data to a server,

[1755] The server analyzes input data and sentiment data, and retrieves relevant existing regulatory data from a database.

[1756] A natural language generation means that generates a draft regulation based on search results and sentiment data on a server,

[1757] A means of displaying the generated draft on the user's terminal,

[1758] A system that includes this.

[1759] (Claim 2)

[1760] The system according to claim 1, wherein the generated draft of the regulation is editable by the user.

[1761] (Claim 3)

[1762] The system according to claim 1, which works in conjunction with a regulatory database and generates drafts using appropriate templates. [Explanation of Symbols]

[1763] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means by which the user inputs data including the purpose of the regulation, its scope, the matters covered, and the specific content of the regulation, A means of sending the input data to the server, The server analyzes the input data and retrieves relevant existing laws and regulations from the database. A natural language generation method in which a server generates drafts of laws and regulations based on search results, A means of displaying the generated draft on the user's terminal, A system that includes this.

2. The system according to claim 1, wherein drafts of laws and regulations are editable by users.

3. The system according to claim 1, which is linked to a database of laws and regulations and generates a draft using an appropriate template.

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