system

Abstract

Provide a system. The method includes: receiving a regulatory request; means for parsing the received request to extract specific regulatory information; A means to retrieve up-to-date regulatory information from external databases or APIs; and A method for summarizing acquired legal and regulatory information using generative AI to concisely summarize it, and a means for selecting and generating appropriate countermeasures based on the summarized regulatory information; The system includes a means for providing the generated remedy to a user.

Description

[Technical Field]

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

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

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

[0004] Understanding legal changes aimed at small and medium-sized enterprises and proposing appropriate countermeasures is a difficult task for many agencies. It's especially difficult to quickly and accurately grasp and understand this information when legal changes occur frequently and vary widely in content. This prevents agencies from providing appropriate solutions to their clients, hindering business efficiency and the improvement of trust. Therefore, there is a need to automate the provision of legal change information to small and medium-sized enterprises and the presentation of appropriate countermeasures based on that information, thereby improving the operational efficiency of agencies. [Means for solving the problem]

[0005] The present invention is a system that includes a means for receiving requests related to legal regulations, a means for analyzing the received requests and extracting information related to specific legal regulations, a means for obtaining the latest information on legal regulations from an external database or external API, a means for summarizing the obtained legal regulatory information using a generative AI that concisely summarizes the information, a means for selecting and generating appropriate countermeasures based on the summarized legal regulatory information, and a means for providing the generated countermeasures to users. This system allows agents to easily understand information about legal changes and quickly provide appropriate solutions for small and medium-sized enterprises. As a result, the agent's business efficiency can be improved and the service provided to clients can be improved.

[0006] A "user terminal" is a device such as a computer or smartphone that a user uses to input and send regulatory requests to a server.

[0007] The "means for receiving a request" refers to a function that enables the server to receive request data regarding legal regulations sent from a user terminal.

[0008] The "means for analyzing the request" is a process for analyzing the content of the received request data and identifying the specific legal and regulatory information that the user is seeking.

[0009] "External Database or External API" refers to an external data source or interface that the Server accesses to obtain up-to-date regulatory information.

[0010] "Generative AI" is an artificial intelligence system that uses natural language processing technology to concisely summarize acquired information.

[0011] "Means of summarization" refers to the process of using generative AI to summarize regulatory information in a concise and easy-to-understand format.

[0012] "Means for selecting and generating countermeasures" refers to the functionality for identifying appropriate countermeasures based on summarized regulatory information and generating that information.

[0013] The "means for providing to the user" refers to a process for transmitting the generated countermeasures and summary information to the user terminal and displaying them to the user.

[0014] "Legal and Regulatory Information" is data about the latest changes and new requirements regarding laws and regulations. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] This invention is a system that receives requests related to legal regulations, analyzes the requests to obtain information about specific legal regulations, summarizes the information concisely using generative AI, selects appropriate countermeasures, and provides them to the user. Here, we will explain the specific program processing in natural language.

[0037] Overall system overview

[0038] 1. Accepting user requests

[0039] A user sends a request for information about a specific law or regulation from a terminal. For example, suppose the user types and submits "I want to know about amendments to the Labor Standards Act." The terminal then sends this request to the server.

[0040] 2. Receiving and parsing the request

[0041] The server receives the request from the device and analyzes the content to identify information about the specific law and regulation. This analysis determines the specific legal reform topic the user is seeking (in this case, amendments to the Labor Standards Act).

[0042] 3. Obtaining information on legal reforms

[0043] The server accesses a database of legal amendments or an external API to retrieve the latest information about the laws and regulations specified in the request, for example, the latest provisions and explanations regarding amendments to the Labor Standards Act.

[0044] 4. Simplifying information with AI

[0045] The acquired legal amendment information is passed to a generative AI, which then summarises the information concisely. For example, it might generate a summary such as "The upper limit on working hours has been changed to 45 hours per month."

[0046] 5. Solution Proposal

[0047] The server selects countermeasures for small and medium-sized enterprises based on the summarized information on legal amendments, for example, generating recommendations for the introduction of a working time management system.

[0048] 6. Providing Results to Users

[0049] The server sends the generated summary information and countermeasures to the user's terminal, where the user can view the information.

[0050] Specific examples

[0051] Responding to the revision of the Labor Standards Act

[0052] The user sends a request from their device saying, "I want to know about amendments to the Labor Standards Act." The server receives this request and retrieves the latest information on amendments to the Labor Standards Act from an external API. The generative AI summarizes the information it retrieves as "The upper limit on working hours has been changed to 45 hours per month," and based on this, the server proposes "the introduction of a working hours management system" as a solution. Finally, the server sends this summary information and solution to the user's device, and the user can confirm the details on their device.

[0053] Through the above process, users (agency staff) can easily understand the details of legal amendments and immediately provide appropriate countermeasures to their clients (small and medium-sized enterprises), thereby improving business efficiency and service.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] The user inputs a request for information on legal regulations into the terminal and presses the send button, which then sends the request to the server.

[0057] Step 2:

[0058] The server receives the request sent from the terminal, and the received request data is a specific regulatory topic for which the user wishes to confirm.

[0059] Step 3:

[0060] The server parses the request data to identify the specific regulatory topic being sought, specifically extracting regulatory categories and specific amendments from the request data.

[0061] Step 4:

[0062] The server accesses an external database or external API to retrieve the latest information on the identified legal and regulatory topic. For example, an external API is called to retrieve revision data for the Labor Standards Act.

[0063] Step 5:

[0064] The server passes the acquired legal information to a generative AI, which then summarizes the information concisely. The AI ​​extracts the key points of the legal regulations and provides a short summary.

[0065] Step 6:

[0066] The server selects countermeasures for small and medium-sized enterprises based on the summarized legal and regulatory information. For example, if there is a change in the upper limit of working hours, the server will suggest the introduction of a working hour management system.

[0067] Step 7:

[0068] The server generates information about the selected countermeasure, including details of the countermeasure, and formats the information in a format that can be provided to the user.

[0069] Step 8:

[0070] The server sends the generated summary information and countermeasures to the user terminal, which receives the information and displays it to the user.

[0071] Step 9:

[0072] The user checks the information provided on the device and understands the specific countermeasures. Based on this information, the user provides appropriate advice to the client.

[0073] Example 1

[0074] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0075] Currently, many companies and individuals need to respond quickly to changes in laws and regulations, but it is difficult to understand the necessary content from the vast amount of information and determine appropriate countermeasures. Since it is particularly difficult for small and medium-sized enterprises and individuals to collect and apply information about changes in laws and regulations, they are required to obtain information efficiently and accurately and provide appropriate countermeasures based on that information.

[0076] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0077] In this invention, the server includes means for receiving requests related to laws and regulations, means for analyzing the received requests and extracting information related to specific laws and regulations, means for obtaining the latest information related to laws and regulations from an external database or external API, means for summarizing the obtained legal and regulatory information using a generative AI that concisely summarizes the obtained legal and regulatory information, means for selecting and generating appropriate countermeasures based on the summarized legal and regulatory information, means for providing the generated countermeasures to a user, means for analyzing data obtained from external information sources using natural language processing technology, and means for instructing the generative AI model to summarize the legal and regulatory information using prompt sentences. This enables users to quickly and efficiently respond to changes in laws and regulations and obtain accurate information and appropriate countermeasures.

[0078] "Legal regulations" refer to rules and laws enacted by government agencies such as countries and regions, which companies and individuals must comply with.

[0079] A "request" is a request that a user sends to a system for particular information or service.

[0080] "Receiving means" refers to the function or device that allows the server to receive requests sent from the user.

[0081] "Analysis means" refers to the processing method or software used to understand the content of the received request and extract the necessary information.

[0082] An "external database" is an external information aggregation system that stores information related to laws and regulations.

[0083] An "external API" is an interface for data exchange with other systems and services.

[0084] "Generative AI" refers to an artificial intelligence model that automatically generates sentences based on given data and questions.

[0085] "Summarization means" refers to a processing method or software for concisely summarizing acquired information.

[0086] A "solution" is a specific measure or action that should be taken in response to a particular problem or situation.

[0087] "Natural language processing technology" refers to technology that allows computers to understand, interpret, and generate human language.

[0088] A "prompt" is an instruction given to a generative AI when it is put into operation, and serves as a guideline for the AI ​​to generate the necessary information.

[0089] The present invention is a system that receives requests related to laws and regulations, analyzes the requests to obtain information about specific laws and regulations, summarizes the information concisely using generative AI, selects appropriate countermeasures, and provides them to users. The following describes how to specifically implement the present invention.

[0090] Overall overview

[0091] 1. Accepting user requests

[0092] A user uses their device to input and send a request for information about a specific law or regulation. For example, the user might write, "I want to know about amendments to the Labor Standards Act," and click the send button. The device then sends this request to the server.

[0093] 2. Receiving and parsing the request

[0094] The server receives a request sent from the user's device. The server analyzes the request and identifies the specific legal and regulatory topic the user is looking for. This analysis is performed using natural language processing (NLP) technology. As a result of the analysis, for example, "Amendments to the Labor Standards Act" may be identified as a topic.

[0095] 3. Obtaining legal and regulatory information

[0096] The server accesses external databases and APIs to obtain the latest information on specified laws and regulations. Specifically, it uses external information sources such as the "Government Gazette API" and "Legal Data Provision Service." The server sends an API request, analyzes the received data, and obtains the latest provisions and explanations.

[0097] 4. AI-powered information summarization

[0098] The server passes the acquired legal and regulatory information to a generative AI model (e.g., "OpenAI (registered trademark) GPT-4 (registered trademark)"). The generative AI model concisely summarizes the information provided. This summarization process uses a pre-set prompt (e.g., "Please briefly summarize the latest amendments to the Labor Standards Act").

[0099] 5. Solution Proposal

[0100] The server selects countermeasures based on specific conditions (for example, for small and medium-sized enterprises) based on the information summarized by the generative AI model. In doing so, the server refers to a list of proposal candidates from an internal database and recommends, for example, the introduction of a working hours management system.

[0101] 6. Providing results to users

[0102] The server sends the final summary information and countermeasures to the user's device. The user can check this information on their own device. For example, a message such as "The upper limit on working hours has been changed to 45 hours per month. As a countermeasure, we recommend introducing a working hours management system" will be displayed on the user's device screen.

[0103] Specific examples

[0104] The user sends a request from their device saying, "I would like to know about amendments to the Labor Standards Act." The server receives this request and uses NLP technology to identify the topic "Amendments to the Labor Standards Act." The server accesses the "Government Gazette API" to obtain data on this year's amendments to the Labor Standards Act. The obtained data is passed to a generative AI model (OpenAI GPT-4) and the prompt is used: "Please briefly summarize the latest amendments to the Labor Standards Act." The generative AI model generates a summary: "The upper limit on working hours has been changed to 45 hours per month." The server selects a solution from its internal database that recommends "introducing a working hour management system," and sends the final summary information and solution to the user's device. The user checks the results on their device.

[0105] In this way, the system of the present invention allows users to quickly and efficiently respond to changes in laws and regulations and obtain accurate information and appropriate countermeasures.

[0106] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0107] Step 1:

[0108] A user uses their device to input and send a request for information about a specific law or regulation. Specifically, the user writes "I would like to know about amendments to the Labor Standards Act" and clicks the send button. This request is sent from the device to the server as an HTTP POST request.

[0109] Input: The regulatory request entered by the user into the device

[0110] Output: HTTP POST request sent to the server

[0111] Step 2:

[0112] The server receives the request sent from the user terminal. The server analyzes the content of the received request using natural language processing (NLP) technology to identify specific legal and regulatory topics. For example, this analysis extracts the topic "Amendments to the Labor Standards Act."

[0113] Input: HTTP POST request received by the server

[0114] Output: Parsed topic (e.g. "Amendment to the Labor Standards Act")

[0115] Step 3:

[0116] The server accesses external databases and APIs to obtain the latest information on specified laws and regulations. Specifically, the server sends API requests to external information sources such as "Government Gazette API" and "Legal Data Service" to obtain relevant data. This obtained data includes the latest provisions and explanations.

[0117] Input: Parsed topic, external database or API endpoint

[0118] Output: The latest legal information (e.g., latest provisions and explanations)

[0119] Step 4:

[0120] The server passes the acquired legal and regulatory information to a generative AI model (e.g., OpenAI GPT-4). Here, the server sets a prompt and instructs the generative AI model to provide a concise summary. The prompt is set to something like, "Please briefly summarize the latest amendments to the Labor Standards Act." The generative AI model generates a summary based on this prompt.

[0121] Input: Obtained legal information, prompt text

[0122] Output: The generated summary (e.g. "The working hour limit has been changed to 45 hours per month")

[0123] Step 5:

[0124] Based on the information summarized by the generative AI model, the server selects countermeasures according to specific conditions (for example, for small and medium-sized enterprises). The server refers to a list of proposal candidates from an internal database and selects the optimal countermeasure (for example, "introduction of a working hour management system").

[0125] Input: Generated summary, list of suggestion candidates from internal database

[0126] Output: Selected solution (e.g., "Introduce a working time management system")

[0127] Step 6:

[0128] The server sends the final summary information and countermeasures to the user's terminal. The user can check this information on their own terminal. For example, a message such as "The upper limit of working hours has been changed to 45 hours per month. As a countermeasure, we recommend introducing a working hours management system" will be displayed on the terminal screen.

[0129] Input: Final summary information, action plan

[0130] Output: Message sent to user terminal (summary information and action plan)

[0131] (Application example 1)

[0132] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0133] The food delivery industry needs to respond quickly to changes in food safety standards and hygiene control laws, but the current system tends to be slow in obtaining regulatory information and proposing countermeasures. As a result, it is difficult for businesses to comply with the latest regulations, leading to problems such as reduced business efficiency and compliance.

[0134] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0135] In this invention, the server includes means for receiving requests related to laws and regulations, means for analyzing the received requests and extracting information related to specific laws and regulations, means for obtaining the latest information related to laws and regulations from an external database or external API, means for summarizing the obtained information on laws and regulations using a generative AI that concisely summarizes the information, means for selecting and generating appropriate countermeasures based on the summarized information on laws and regulations, means for transmitting the summarized information on laws and regulations and the countermeasures to a user terminal, means for proposing countermeasures for food delivery companies in accordance with new laws and regulations, means for reflecting the proposed countermeasures in the business system of the food delivery company, and means for providing an appropriate prompt sentence to the generative AI model. This enables food delivery companies to quickly obtain the latest information on laws and regulations and reflect the countermeasures based on the information in their business systems.

[0136] "Legal regulations" is a general term for laws, regulations, guidelines, etc. established by governments and regulatory bodies, and are rules that industries and companies must follow.

[0137] A "request" refers to an instruction or inquiry that a user sends to a server seeking some information or service.

[0138] "Analysis" refers to the process of analyzing the content of a received request and identifying necessary information.

[0139] "Information" refers to data and knowledge about legal regulations, the latest provisions and amendments, etc.

[0140] An "external database" is a large-scale data repository located outside the server, and is an information source accessible via the Internet.

[0141] An "external API" is an application program interface that provides a means to connect to external services and databases.

[0142] "Generative AI" is a type of artificial intelligence trained to perform specific tasks, specifically models capable of generating new information from data.

[0143] "Summarization" refers to the compilation of acquired regulatory information into a more concise format.

[0144] "Countermeasures" indicate specific actions or measures that businesses and users should take based on the summarized legal and regulatory information.

[0145] "User terminal" refers to a device used by a user, such as a smartphone, PC, or tablet, and is a medium for communicating with the server.

[0146] A "prompt" is an input sentence that instructs a generative AI to perform a specific task, and serves as an instruction for the AI ​​to generate an appropriate result.

[0147] This invention provides a system for the food delivery industry to quickly respond to new food safety standards and amendments to the Sanitation Control Act. This system performs all processes from obtaining legal information to proposing countermeasures, and is implemented by combining a server, user terminals, and generative AI models.

[0148] Overall system configuration

[0149] The system includes the following major components:

[0150] 1. User Device

[0151] A device through which a user can request new regulatory information, such as a smartphone, tablet, or computer.

[0152] 2. Server

[0153] Its role is to receive requests, analyze them, obtain information from external databases and APIs, summarize using generative AI, select and propose countermeasures, and send the results to the user's device.

[0154] 3. Generative AI Models

[0155] For example, language models such as OpenAI's GPT-4 are used to summarize legal information and generate prompts.

[0156] Program processing

[0157] The server performs the following steps:

[0158] 1. Receiving and parsing the request

[0159] A request such as "I would like to obtain the latest food safety standards" is received from the user terminal. The received request is analyzed on the server side to identify the required information.

[0160] 2. Obtaining legal and regulatory information

[0161] The server accesses an external database or API (e.g., a government regulatory database) to obtain the latest regulatory information.

[0162] 3. Summary of information

[0163] The acquired legal and regulatory information is passed to a generative AI model (e.g., GPT-4) to generate a concise summary. An example prompt is, "How can I comply with the latest food safety standards?"

[0164] 4. Selection and proposal of countermeasures

[0165] Based on the summarized information, the server selects specific countermeasures for food delivery companies, such as "Since all fresh vegetables are required to be washed before shipping, we recommend introducing fresh vegetable washing machines."

[0166] 5. Sending the results to the user device

[0167] The final summary information and countermeasures are sent to the user terminal so that the user can check them.

[0168] Specific examples

[0169] For example, if a food delivery company wants to know about new food safety standards, they might enter the following prompt:

[0170] "How can I comply with the latest food safety standards?"

[0171] The server analyzes this request and retrieves the latest information from an external database. Using a generative AI model, it generates a summary such as, "New food safety standards require all raw vegetables to be washed before shipping," and suggests a countermeasure such as, "We recommend installing a raw vegetable washing machine." This allows users to quickly and concisely obtain the latest regulatory information and take the necessary countermeasures.

[0172] As a result, the present invention enables the food delivery industry to quickly and efficiently comply with new regulations.

[0173] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0174] Step 1:

[0175] A user terminal generates and transmits a regulatory request.

[0176] Input: The user inputs a request into the terminal saying, "I would like to obtain the latest food safety standards."

[0177] Output: This request is sent from the user terminal to the server.

[0178] Specific operation: A user fills out a request form on a device such as a smartphone or tablet and presses the send button. This request is sent to the server as an HTTP request.

[0179] Step 2:

[0180] The server analyzes the received request and extracts specific regulatory information.

[0181] Input: A request sent from the user's device to "get the latest food safety standards."

[0182] Output: Extract the keyword "food safety standards" as the parsed regulatory information request.

[0183] What it does: The server uses a natural language processing (NLP) engine to analyze the request and extract key elements of the request (in this case, "food safety standards").

[0184] Step 3:

[0185] The server accesses an external database or API to retrieve the latest regulatory information.

[0186] Input: Request for "Food Safety Standards."

[0187] Output: The latest information on the acquired "Food Safety Standards."

[0188] How it works: The server sends an HTTP request to an external API (for example, a government regulatory database API) to obtain the latest data on "food safety standards." The obtained data is returned to the server in JSON format.

[0189] Step 4:

[0190] The server passes the acquired legal and regulatory information to a generative AI model for summarization.

[0191] Input: Latest information on "Food Safety Standards" (JSON data).

[0192] Output: A summary result from the generative AI model (e.g., "New food safety standards require all raw vegetables to be washed before shipping.").

[0193] How it works: The server converts the acquired information into text format and inputs it to a generative AI model (e.g., GPT-4) using a prompt sentence, such as "How can I comply with the latest food safety standards?". The AI ​​model generates a concise summary based on this prompt.

[0194] Step 5:

[0195] The server selects and generates appropriate countermeasures based on the summarized information.

[0196] Input: Summary result (e.g., "New food safety standards require all fresh vegetables to be washed before shipping.").

[0197] Output: The selected countermeasure (e.g., "We recommend installing a fresh vegetable washer").

[0198] Specific operation: The server checks the summary results and runs an algorithm to automatically select specific countermeasures for the contractor. It selects the optimal solution based on past countermeasures in the database and the contractor's request information.

[0199] Step 6:

[0200] The server transmits the generated summary information and countermeasures to the user terminal.

[0201] Input: Summary information and action plan (e.g., "New food safety standards require all fresh vegetables to be washed before shipping" and "We recommend installing fresh vegetable washers").

[0202] Output: The final result displayed on the user's terminal.

[0203] Specific operation: The server returns the generated summary information and countermeasures to the user device as an HTTP response. The user device receives this information and displays it on the screen. The user can then take the necessary measures based on this information.

[0204] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0205] This invention is a system that receives requests related to legal regulations, analyzes the requests to obtain information about specific legal regulations, summarizes them succinctly using generative AI, selects appropriate countermeasures, and provides them to the user, and further combines this with an emotion engine that recognizes the user's emotions. Here, we will explain the specific program processing in natural language.

[0206] Overall system overview

[0207] 1. Accepting user requests

[0208] A user inputs a request for information about a specific law and regulation from a terminal and presses the send button. For example, suppose a user inputs and sends "I want to know about amendments to the Labor Standards Act." The terminal then sends this request to the server.

[0209] 2. Receiving and parsing the request

[0210] The server receives a request sent from the terminal. The received request data is a specific legal topic for which the user is seeking confirmation. The server analyzes the request data and identifies the specific legal topic being sought. For example, the server identifies information about amendments to the Labor Standards Act from the user's request.

[0211] 3. Emotion analysis using an emotion engine

[0212] When the server receives a request, it uses an emotion engine to analyze the user's emotional state. For example, it reads emotions from the user's request text or voice input and determines whether the user is anxious or excited.

[0213] 4. Obtaining information on legal reforms

[0214] The server accesses a database of legal amendments or an external API to retrieve the latest information on the laws and regulations specified in the request. For example, it calls an external API to retrieve data on amendments to the Labor Standards Act.

[0215] 5. Simplifying information with AI

[0216] The acquired legal amendment information is passed to a generative AI, which then processes the information into a concise summary. The AI ​​extracts the key points of the legal amendment and creates a short summary. For example, a summary such as "The upper limit on working hours has been changed to 45 hours per month" is generated.

[0217] 6. Propose solutions based on emotions

[0218] The server selects countermeasures for small and medium-sized enterprises based on the summarized legal amendment information and the user's emotional state recognized by the emotion engine. For example, if it recognizes that the user is feeling anxious, it selects content that strongly recommends a work hour management system.

[0219] 7. Individual customization

[0220] The server then generates a customized version of the selected solution based on the user's emotional state. It then generates information containing details of the solution and formats it for delivery to the user. For example, if the user's emotion is "anxiety," it also presents contact information for the support desk.

[0221] 8. Providing Results to Users

[0222] The server sends the generated summary information and countermeasures to the user's terminal, which receives this information and displays it to the user. Based on this information, the user can understand the specific countermeasures and proceed with the necessary procedures.

[0223] Specific examples

[0224] Responding to the revision of the Labor Standards Act

[0225] The user sends a request from their device saying, "I'd like to know about the revisions to the Labor Standards Act." The server receives this request, and its emotion engine recognizes that the user's emotion is "anxiety." The server retrieves the latest information about the revisions to the Labor Standards Act from an external API, and the generative AI summarizes it as "The upper limit on working hours has been changed to 45 hours per month." Based on this summary, the server takes into account the user's emotional state and makes a proposal strongly recommending the "introduction of a working hours management system," along with contact information for a support desk. Finally, the server sends this summary and a customized solution to the user's device, where the user can confirm the content on their device.

[0226] Through the above process, users (agency staff) can understand the details of legal changes and the appropriate countermeasures to them in a way that takes into account the user's emotional state, and can immediately provide appropriate advice to clients (small and medium-sized enterprises), thereby improving work efficiency and service.

[0227] The processing flow will be explained below.

[0228] Step 1:

[0229] A user inputs a request for information about a specific law or regulation into a terminal and presses the send button. For example, the user inputs a request such as "I want to know about amendments to the Labor Standards Act." The terminal then sends this request to the server.

[0230] Step 2:

[0231] The server receives a request sent from the terminal, the request data including the specific regulatory topic of interest to the user.

[0232] Step 3:

[0233] The server analyzes the received request data and extracts information about specific laws and regulations. In this case, it extracts information about amendments to the Labor Standards Act.

[0234] Step 4:

[0235] The server uses an emotion engine to analyze the user's emotional state, for example, by recognizing whether the user is feeling anxious based on the request or input text.

[0236] Step 5:

[0237] The server accesses an external database or external API to obtain the latest information on the identified laws and regulations. The server obtains amendment data for the Labor Standards Act from the external API.

[0238] Step 6:

[0239] The acquired legal and regulatory information is passed to a generative AI, which then summarises the information concisely. For example, it might generate a summary such as, "The upper limit on working hours has been changed to 45 hours per month."

[0240] Step 7:

[0241] The server selects countermeasures for small and medium-sized enterprises based on the summarized legal and regulatory information and the user's emotional state recognized by the emotion engine. For example, if the user feels anxious, it will strongly recommend the introduction of a working time management system.

[0242] Step 8:

[0243] The server then generates a customized version of the selected solution, including, for example, a detailed explanation of how to implement the time management system and contact information for support.

[0244] Step 9:

[0245] The server sends the generated summary information and the customized countermeasures to the user terminal, and the terminal displays the received information to the user.

[0246] Step 10:

[0247] The user can check the information provided on the device and understand specific countermeasures. For example, they can check how to implement a working time management system and contact support. Based on this information, the user can provide appropriate advice to the client.

[0248] Example 2

[0249] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0250] Conventional legal information provision systems have difficulty in quickly and accurately providing specific legal information that users desire. They also lack the ability to propose appropriate countermeasures based on the user's emotions and circumstances. Therefore, there is a need for efficient information provision and countermeasure proposals that take the user's emotions into consideration.

[0251] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0252] In this invention, the server includes means for receiving requests related to laws and regulations, means for analyzing the received requests and extracting information related to specific laws and regulations, means for analyzing user sentiment, means for acquiring the latest information related to laws and regulations from an external database or external API, means for summarizing the acquired legal and regulatory information using a generative AI that concisely summarizes the acquired legal and regulatory information, means for selecting and generating appropriate countermeasures based on the summarized legal and regulatory information and the user's sentiment, and means for providing the generated countermeasures to the user. This makes it possible to respond to user requests quickly and accurately and to propose appropriate countermeasures that take the user's sentiment into consideration.

[0253] A "regulatory request" is a request made by a user to the system for information about a specific regulation.

[0254] "Analysis" is a processing means for analyzing received data and understanding its contents.

[0255] "Emotion analysis" is a means of identifying and identifying a user's emotional state from their input and behavior.

[0256] An "external database" is a database that exists outside the system and stores data related to laws and regulations.

[0257] An "external API" is an application program interface for communicating with external services and obtaining data.

[0258] "Generative AI" is an AI that learns from large amounts of data and performs natural language processing. It is an AI module that is used, for example, to concisely summarize legal and regulatory information.

[0259] "Summarization" is the process of summarizing detailed information into a concise and easy-to-understand form.

[0260] "Appropriate responses" are specific guidelines or recommendations that the system provides based on the user's request or emotional state.

[0261] "Providing" means giving information or services to a user.

[0262] This system receives requests related to legal regulations, analyzes the requests to obtain information about specific legal regulations, summarizes them succinctly using generative AI, selects appropriate countermeasures, and provides them to the user. It also incorporates an emotion engine that recognizes the user's emotions.

[0263] Overall system overview

[0264] 1. Accepting user requests

[0265] The user inputs a request for information about a specific law and regulation into the terminal and presses the send button. For example, suppose the user inputs "I want to know about amendments to the Labor Standards Act" and sends it. The terminal then sends this request to the server.

[0266] 2. Receiving and parsing the request

[0267] The server receives a request sent from a terminal. The received request data includes a specific legal and regulatory topic. The server analyzes the request data and identifies the specific legal and regulatory topic. For example, it identifies a request regarding "amendments to the Labor Standards Act."

[0268] 3. Emotion analysis using an emotion engine

[0269] When the server receives a request, it uses an emotion engine to analyze the user's emotional state. For example, it reads emotions from the user's request text or voice input and determines whether the user is anxious or excited.

[0270] 4. Obtaining information on legal reforms

[0271] The server accesses a database of legal amendments or an external API to obtain the latest information on the laws and regulations specified in the request. For example, it calls an external API to obtain "Labor Standards Act amendment data."

[0272] 5. Simplifying information with AI

[0273] The server then passes the acquired legal change information to a generative AI, which then summarizes the information concisely. This generative AI uses an advanced natural language processing model such as GPT-4. For example, it generates a summary such as, "The upper limit on working hours has been changed to 45 hours per month."

[0274] 6. Propose solutions based on emotions

[0275] The server selects appropriate countermeasures based on the summarized legal amendment information and the user's emotional state recognized by the emotion engine. For example, if the user is feeling anxious, it will select a solution that strongly recommends a work time management system.

[0276] 7. Individual customization

[0277] The server then generates a customized response based on the user's emotional state. For example, it can add support contact information to the response to reassure the user.

[0278] 8. Providing Results to Users

[0279] The server sends the generated summary information and countermeasures to the user's terminal, which receives this information and displays it to the user. Based on this information, the user can understand the specific countermeasures and proceed with the necessary procedures.

[0280] Specific examples

[0281] The user sends a request from their device saying, "I'd like to know about the revisions to the Labor Standards Act." The server receives this request, and its emotion engine recognizes the user's emotion as "anxiety." The server retrieves the latest information on legal revisions from an external API, and the generative AI summarizes it as "The upper limit on working hours has been changed to 45 hours per month." Based on this summary, the server takes into account the user's emotional state and makes a proposal strongly recommending the "introduction of a working hours management system," along with contact information for a support desk. Finally, the server sends this summary and a customized solution to the user's device, where the user can view the content on their device.

[0282] In this way, users can understand the details of the legal changes and the appropriate measures to take in response, and receive support in taking concrete action.

[0283] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0284] Step 1:

[0285] The user inputs a request for information about a specific law and regulation into the terminal and presses the send button.

[0286] Input: The user enters the text "I want to know about the revision of the Labor Standards Act."

[0287] Action: The user enters text into the input field and clicks the "Submit" button.

[0288] Output: The terminal sends the input request text to the server.

[0289] Step 2:

[0290] The server receives the request sent from the terminal.

[0291] Input: Request data in the HTTP request sent from the terminal.

[0292] What it does: The server extracts text data from the body of the HTTP request.

[0293] Output: Extracted text data (e.g., "I would like to know about the revision of the Labor Standards Act").

[0294] Step 3:

[0295] The server analyzes the received request data to identify specific regulatory topics.

[0296] Input: Text data (e.g., "I would like to know about the revision of the Labor Standards Act").

[0297] How it works: The server uses natural language processing libraries to parse the request and extract specific regulatory topics.

[0298] Output: Extracted topics (e.g., "Amendments to the Labor Standards Act").

[0299] Step 4:

[0300] When the server receives a request, it uses an emotion engine to analyze the user's emotional state.

[0301] Input: Request text (e.g., "I would like to know about the revision of the Labor Standards Act").

[0302] Operation: The server calls the emotion engine API to perform emotion analysis.

[0303] Output: User's emotional state (e.g., anxiety).

[0304] Step 5:

[0305] The server accesses a legal change database or external API to retrieve the latest information about the laws and regulations identified in the request.

[0306] Input: Identified legal and regulatory topic (e.g., "Amendments to the Labor Standards Act").

[0307] How it works: The server sends a request to an external API to get the latest regulatory information.

[0308] Output: Retrieved legal change information data.

[0309] Step 6:

[0310] The server passes the legal amendment information it has acquired to a generative AI, which then summarizes it concisely.

[0311] Input: Obtained legal reform information data.

[0312] How it works: The server inputs data into a generative AI model and generates a summary result.

[0313] Output: Summarized legal change information (e.g., "The maximum working hours has been changed to 45 hours per month").

[0314] Step 7:

[0315] The server selects appropriate countermeasures based on the summarized legal amendment information and the user's emotional state recognized by the emotion engine.

[0316] Input: Abstracted legal change information and the user's emotional state.

[0317] Action: The server chooses a course of action based on the conditions.

[0318] Output: Selected solution (e.g., "Implementation of a working time management system").

[0319] Step 8:

[0320] The server then customizes the selected response individually based on the user's emotional state.

[0321] Input: The selected response and the user's emotional state.

[0322] Operation: The server customizes the solution by adding contact information for the support desk, etc.

[0323] Output: Customized action information (e.g. "Implementing a working time management system. Contact support here.").

[0324] Step 9:

[0325] The server transmits the generated summary information and countermeasures to the user terminal.

[0326] Input: Customized workaround information.

[0327] Operation: The server formats the countermeasure information in JSON format as an HTTP response and sends it to the device.

[0328] Output: Summary information and remedial actions sent to the user terminal.

[0329] Step 10:

[0330] The terminal displays the information received from the server to the user.

[0331] Input: Summary information and remedial actions received from the server.

[0332] Behavior: The user device parses the received JSON data and formats it as text for display in the user interface.

[0333] Output: On-screen summary information and suggested actions.

[0334] (Application example 2)

[0335] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0336] In situations where local legal regulations must be addressed, it is difficult for security staff to quickly obtain appropriate information. Furthermore, appropriate responses that take into account the local situation and the user's emotional state may not be provided, potentially resulting in a decline in the quality of responses. This can lead to issues such as delayed legal responses and an increased risk of incorrect actions.

[0337] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving a request related to laws and regulations; means for analyzing the received request and extracting information related to the specific laws and regulations; means for acquiring the latest information related to laws and regulations from an external database or an external API; means for summarizing the acquired laws and regulations using a generative AI that concisely summarizes the acquired laws and regulations; means for selecting and generating appropriate countermeasures based on the summarized laws and regulations information; emotion analysis engine means for analyzing the user's emotional state; means for customizing the summary information and countermeasures based on the emotional state; and means for providing the user with appropriate countermeasures and customized information. This enables security staff to quickly obtain appropriate laws and regulations information and countermeasures on site and take appropriate action according to the situation.

[0338] A "regulatory request" is a request sent by a user to a server for information about a particular regulation.

[0339] "Means for receiving" refers to a device or method for receiving a request sent from a user terminal.

[0340] "Means for parsing and extracting information relating to specific regulations" refers to a device or method that analyzes the received request and extracts information relating to specific regulations therefrom.

[0341] "External Database or External API" refers to an external database or application programming interface that provides regulatory information.

[0342] "Means for obtaining the latest information" refers to a device or method for collecting the latest information on laws and regulations from an external database or external API.

[0343] "Generative AI" refers to an artificial intelligence system that generates new information based on input information.

[0344] A "means for concisely summarizing" refers to a device or method for concisely summarizing acquired information to the main points.

[0345] "Means of summarizing" refers to a method of using generative AI to extract and summarize complex information in a concise form.

[0346] "Means for selecting and generating appropriate countermeasures" refers to a device or method for selecting and generating the most appropriate countermeasures based on the summarized regulatory information.

[0347] An "emotion analysis engine" refers to a system that analyzes a user's emotional state and outputs the results.

[0348] "Means for customizing summary information and responses based on emotional state" refers to a device or method that individually tailors summary information and selected responses based on the analysis results of an emotion analysis engine.

[0349] "Means for providing to the user" refers to a device or method for delivering the generated information and countermeasures to the user.

[0350] This invention is a system that receives requests related to laws and regulations, analyzes the requests to obtain information about specific laws and regulations, summarizes the information concisely using generative AI, selects appropriate countermeasures, and provides them to the user. This system is composed of a sentiment analysis engine that recognizes the user's emotions.

[0351] Overall system overview

[0352] 1. Accepting user requests

[0353] First, a user inputs a request for information about a specific law or regulation from the device. For example, the user might say through the smart glasses, "I want to know about the legal procedures when a suspicious person is found at the scene." This voice request is sent by the device to the server.

[0354] 2. Receiving and parsing the request

[0355] The server receives the request sent from the terminal. The received request data is the specific legal topic for which the user is seeking confirmation. The server analyzes the request data and identifies the specific legal topic being sought. In this case, it is identified as "Legal Procedures Regarding Suspicious Persons."

[0356] 3. Emotion analysis using an emotion engine

[0357] When the server receives a request, it uses an emotion analysis engine to analyze the user's emotional state. For example, it can read emotions from the user's request or voice input and determine whether the user is nervous.

[0358] 4. Obtaining legal and regulatory information

[0359] The server then accesses a database of laws and regulations or an external API to retrieve the latest information on the laws and regulations specified in the request. For example, it may call an external API to retrieve the latest data on the Peace Preservation Act or legal procedures for dealing with suspicious persons.

[0360] 5. Simplifying information with AI

[0361] The acquired legal and regulatory information is passed to a generative AI, which then processes it into a concise summary. For example, it might generate a summary such as, "When arresting a suspicious person, immediately contact the police and ensure safety at the scene."

[0362] 6. Propose solutions based on emotions

[0363] The server selects appropriate countermeasures based on the summarized legal information and the user's emotional state as determined by an emotion analysis engine. For example, if the server determines that the user is nervous, it selects content that emphasizes how to quickly contact the police or praises the first responders.

[0364] 7. Individual customization

[0365] The server generates a customized response based on the selected emotional state, and generates information including details of the response in a format that can be provided to the user.

[0366] 8. Providing Results to Users

[0367] The server sends the generated summary information and countermeasures to the user device (smart glasses). The user device receives this information and displays it to the user. Based on this information, the user can understand the specific countermeasures and proceed with the necessary procedures.

[0368] Hardware and software used

[0369] Hardware

[0370] Smart glasses: Wearable devices with voice recognition and display functions (e.g., Google® Glass®, Microsoft® HoloLens®)

[0371] software

[0372] Sentiment analysis engine: Python Sentiment Analysis library (e.g. textblob)

[0373] Generative AI: OpenAI's GPT-3 (registered trademark) or GPT-4 API

[0374] Data acquisition: Realized using RESTful API (e.g., regulatory information API)

[0375] Prompt Sentence Examples

[0376] “Based on the summarized information about security regulations and considering that the user is anxious feeling, provide a suitable solution to handle unidentified persons in restricted areas.”

[0377] This system allows security staff to quickly obtain appropriate legal and regulatory information and countermeasures on-site, and to take the most appropriate action depending on the situation through sentiment analysis.

[0378] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0379] Step 1:

[0380] The user inputs a request for information about specific laws and regulations from the device. The request is generated by the user speaking into the smart glasses, such as "I would like to know about the legal procedures when a suspicious person is discovered at the scene." The input voice data is converted into text format, and the device sends the request to the server.

[0381] Step 2:

[0382] The server receives a request sent from the terminal. The received request data is text data containing a specific legal topic for which the user is seeking confirmation. The server analyzes this text data and identifies the specific legal topic being sought. In this case, "Legal procedures regarding suspicious persons" is extracted.

[0383] Step 3:

[0384] The server passes the received request to an emotion analysis engine, which analyzes the user's emotional state. Using the text data extracted from the voice request as input, the emotion analysis engine identifies the user's emotional state (e.g., nervousness, anxiety). The analysis result is that the user is "nervous."

[0385] Step 4:

[0386] The server accesses a regulatory database or external API to retrieve the latest information related to the identified regulatory topic. For example, to retrieve data on the Peace Preservation Act or legal procedures for dealing with suspicious persons, the server sends a request to the API endpoint and retrieves the required data.

[0387] Step 5:

[0388] The acquired information is passed to a generative AI, which then summarises it concisely. Using legal and regulatory data as input, the generative AI extracts key points and outputs a concise summary. For example, a generated summary might read, "When arresting a suspicious individual, contact the police immediately and ensure safety at the scene."

[0389] Step 6:

[0390] The server selects appropriate countermeasures based on the generated summary information and the user's emotional state recognized by the emotion analysis engine. Countermeasures are generated using the analysis results and summary data as input, such as "If the user is nervous, emphasize how to quickly contact the police or praise the first responders."

[0391] Step 7:

[0392] The server then generates and customizes the selected countermeasures based on the user's emotional state. Using the countermeasures and emotional data as input, the server customizes the countermeasures based on the user's emotional state, and generates specific details of the countermeasures. For example, the server adds details to the countermeasures, such as "procedures for contacting the public security department" and "the importance of initial response."

[0393] Step 8:

[0394] The server sends the customized summary information and countermeasures to the user's device. The device receives the generated data as input and displays the summary of legal proceedings and countermeasures on the smart glasses display. The user can review this information and take appropriate action based on it.

[0395] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0396] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0397] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0398] [Second embodiment]

[0399] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0400] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0401] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0402] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0403] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0404] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0405] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0406] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0407] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0408] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0409] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0410] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0411] This invention is a system that receives requests related to legal regulations, analyzes the requests to obtain information about specific legal regulations, summarizes the information concisely using generative AI, selects appropriate countermeasures, and provides them to the user. Here, we will explain the specific program processing in natural language.

[0412] Overall system overview

[0413] 1. Accepting user requests

[0414] A user sends a request for information about a specific law or regulation from a terminal. For example, suppose the user types and submits "I want to know about amendments to the Labor Standards Act." The terminal then sends this request to the server.

[0415] 2. Receiving and parsing the request

[0416] The server receives the request from the device and analyzes the content to identify information about the specific law and regulation. This analysis determines the specific legal reform topic the user is seeking (in this case, amendments to the Labor Standards Act).

[0417] 3. Obtaining information on legal reforms

[0418] The server accesses a database of legal amendments or an external API to retrieve the latest information about the laws and regulations specified in the request, for example, the latest provisions and explanations regarding amendments to the Labor Standards Act.

[0419] 4. Simplifying information with AI

[0420] The acquired legal amendment information is passed to a generative AI, which then summarises the information concisely. For example, it might generate a summary such as "The upper limit on working hours has been changed to 45 hours per month."

[0421] 5. Solution Proposal

[0422] The server selects countermeasures for small and medium-sized enterprises based on the summarized information on legal amendments, for example, generating recommendations for the introduction of a working time management system.

[0423] 6. Providing Results to Users

[0424] The server sends the generated summary information and countermeasures to the user's terminal, where the user can view the information.

[0425] Specific examples

[0426] Responding to the revision of the Labor Standards Act

[0427] The user sends a request from their device saying, "I want to know about amendments to the Labor Standards Act." The server receives this request and retrieves the latest information on amendments to the Labor Standards Act from an external API. The generative AI summarizes the information it retrieves as "The upper limit on working hours has been changed to 45 hours per month," and based on this, the server proposes "the introduction of a working hours management system" as a solution. Finally, the server sends this summary information and solution to the user's device, and the user can confirm the details on their device.

[0428] Through the above process, users (agency staff) can easily understand the details of legal amendments and immediately provide appropriate countermeasures to their clients (small and medium-sized enterprises), thereby improving business efficiency and service.

[0429] The processing flow will be explained below.

[0430] Step 1:

[0431] The user inputs a request for information on legal regulations into the terminal and presses the send button, which then sends the request to the server.

[0432] Step 2:

[0433] The server receives the request sent from the terminal, and the received request data is a specific regulatory topic for which the user wishes to confirm.

[0434] Step 3:

[0435] The server parses the request data to identify the specific regulatory topic being sought, specifically extracting regulatory categories and specific amendments from the request data.

[0436] Step 4:

[0437] The server accesses an external database or external API to retrieve the latest information on the identified legal and regulatory topic. For example, an external API is called to retrieve revision data for the Labor Standards Act.

[0438] Step 5:

[0439] The server passes the acquired legal information to a generative AI, which then summarizes the information concisely. The AI ​​extracts the key points of the legal regulations and provides a short summary.

[0440] Step 6:

[0441] The server selects countermeasures for small and medium-sized enterprises based on the summarized legal and regulatory information. For example, if there is a change in the upper limit of working hours, the server will suggest the introduction of a working hour management system.

[0442] Step 7:

[0443] The server generates information about the selected countermeasure, including details of the countermeasure, and formats the information in a format that can be provided to the user.

[0444] Step 8:

[0445] The server sends the generated summary information and countermeasures to the user terminal, which receives the information and displays it to the user.

[0446] Step 9:

[0447] The user checks the information provided on the device and understands the specific countermeasures. Based on this information, the user provides appropriate advice to the client.

[0448] Example 1

[0449] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0450] Currently, many companies and individuals need to respond quickly to changes in laws and regulations, but it is difficult to understand the necessary content from the vast amount of information and determine appropriate countermeasures. Since it is particularly difficult for small and medium-sized enterprises and individuals to collect and apply information about changes in laws and regulations, they are required to obtain information efficiently and accurately and provide appropriate countermeasures based on that information.

[0451] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0452] In this invention, the server includes means for receiving requests related to laws and regulations, means for analyzing the received requests and extracting information related to specific laws and regulations, means for obtaining the latest information related to laws and regulations from an external database or external API, means for summarizing the obtained legal and regulatory information using a generative AI that concisely summarizes the obtained legal and regulatory information, means for selecting and generating appropriate countermeasures based on the summarized legal and regulatory information, means for providing the generated countermeasures to a user, means for analyzing data obtained from external information sources using natural language processing technology, and means for instructing the generative AI model to summarize the legal and regulatory information using prompt sentences. This enables users to quickly and efficiently respond to changes in laws and regulations and obtain accurate information and appropriate countermeasures.

[0453] "Legal regulations" refer to rules and laws enacted by government agencies such as countries and regions, which companies and individuals must comply with.

[0454] A "request" is a request that a user sends to a system for particular information or service.

[0455] "Receiving means" refers to the function or device that allows the server to receive requests sent from the user.

[0456] "Analysis means" refers to the processing method or software used to understand the content of the received request and extract the necessary information.

[0457] An "external database" is an external information aggregation system that stores information related to laws and regulations.

[0458] An "external API" is an interface for data exchange with other systems and services.

[0459] "Generative AI" refers to an artificial intelligence model that automatically generates sentences based on given data and questions.

[0460] "Summarization means" refers to a processing method or software for concisely summarizing acquired information.

[0461] A "solution" is a specific measure or action that should be taken in response to a particular problem or situation.

[0462] "Natural language processing technology" refers to technology that allows computers to understand, interpret, and generate human language.

[0463] A "prompt" is an instruction given to a generative AI when it is put into operation, and serves as a guideline for the AI ​​to generate the necessary information.

[0464] The present invention is a system that receives requests related to laws and regulations, analyzes the requests to obtain information about specific laws and regulations, summarizes the information concisely using generative AI, selects appropriate countermeasures, and provides them to users. The following describes how to specifically implement the present invention.

[0465] Overall overview

[0466] 1. Accepting user requests

[0467] A user uses their device to input and send a request for information about a specific law or regulation. For example, the user might write, "I want to know about amendments to the Labor Standards Act," and click the send button. The device then sends this request to the server.

[0468] 2. Receiving and parsing the request

[0469] The server receives a request sent from the user's device. The server analyzes the request and identifies the specific legal and regulatory topic the user is looking for. This analysis is performed using natural language processing (NLP) technology. As a result of the analysis, for example, "Amendments to the Labor Standards Act" may be identified as a topic.

[0470] 3. Obtaining legal and regulatory information

[0471] The server accesses external databases and APIs to obtain the latest information on specified laws and regulations. Specifically, it uses external information sources such as the "Government Gazette API" and "Legal Data Provision Service." The server sends an API request, analyzes the received data, and obtains the latest provisions and explanations.

[0472] 4. AI-powered information summarization

[0473] The server passes the acquired legal and regulatory information to a generative AI model (e.g., "OpenAI GPT-4"). The generative AI model then succinctly summarizes the information provided. This summarization process uses a pre-set prompt (e.g., "Please briefly summarize the latest amendments to the Labor Standards Act").

[0474] 5. Solution Proposal

[0475] The server selects countermeasures based on specific conditions (for example, for small and medium-sized enterprises) based on the information summarized by the generative AI model. In doing so, the server refers to a list of proposal candidates from an internal database and recommends, for example, the introduction of a working hours management system.

[0476] 6. Providing results to users

[0477] The server sends the final summary information and countermeasures to the user's device. The user can check this information on their own device. For example, a message such as "The upper limit on working hours has been changed to 45 hours per month. As a countermeasure, we recommend introducing a working hours management system" will be displayed on the user's device screen.

[0478] Specific examples

[0479] The user sends a request from their device saying, "I would like to know about amendments to the Labor Standards Act." The server receives this request and uses NLP technology to identify the topic "Amendments to the Labor Standards Act." The server accesses the "Government Gazette API" to obtain data on this year's amendments to the Labor Standards Act. The obtained data is passed to a generative AI model (OpenAI GPT-4) and the prompt is used: "Please briefly summarize the latest amendments to the Labor Standards Act." The generative AI model generates a summary: "The upper limit on working hours has been changed to 45 hours per month." The server selects a solution from its internal database that recommends "introducing a working hour management system," and sends the final summary information and solution to the user's device. The user checks the results on their device.

[0480] In this way, the system of the present invention allows users to quickly and efficiently respond to changes in laws and regulations and obtain accurate information and appropriate countermeasures.

[0481] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0482] Step 1:

[0483] A user uses their device to input and send a request for information about a specific law or regulation. Specifically, the user writes "I would like to know about amendments to the Labor Standards Act" and clicks the send button. This request is sent from the device to the server as an HTTP POST request.

[0484] Input: The regulatory request entered by the user into the device

[0485] Output: HTTP POST request sent to the server

[0486] Step 2:

[0487] The server receives the request sent from the user terminal. The server analyzes the content of the received request using natural language processing (NLP) technology to identify specific legal and regulatory topics. For example, this analysis extracts the topic "Amendments to the Labor Standards Act."

[0488] Input: HTTP POST request received by the server

[0489] Output: Parsed topic (e.g. "Amendment to the Labor Standards Act")

[0490] Step 3:

[0491] The server accesses external databases and APIs to obtain the latest information on specified laws and regulations. Specifically, the server sends API requests to external information sources such as "Government Gazette API" and "Legal Data Service" to obtain relevant data. This obtained data includes the latest provisions and explanations.

[0492] Input: Parsed topic, external database or API endpoint

[0493] Output: The latest legal information (e.g., latest provisions and explanations)

[0494] Step 4:

[0495] The server passes the acquired legal and regulatory information to a generative AI model (e.g., OpenAI GPT-4). Here, the server sets a prompt and instructs the generative AI model to provide a concise summary. The prompt is set to something like, "Please briefly summarize the latest amendments to the Labor Standards Act." The generative AI model generates a summary based on this prompt.

[0496] Input: Obtained legal information, prompt text

[0497] Output: The generated summary (e.g. "The working hour limit has been changed to 45 hours per month")

[0498] Step 5:

[0499] Based on the information summarized by the generative AI model, the server selects countermeasures according to specific conditions (for example, for small and medium-sized enterprises). The server refers to a list of proposal candidates from an internal database and selects the optimal countermeasure (for example, "introduction of a working hour management system").

[0500] Input: Generated summary, list of suggestion candidates from internal database

[0501] Output: Selected solution (e.g., "Introduce a working time management system")

[0502] Step 6:

[0503] The server sends the final summary information and countermeasures to the user's terminal. The user can check this information on their own terminal. For example, a message such as "The upper limit of working hours has been changed to 45 hours per month. As a countermeasure, we recommend introducing a working hours management system" will be displayed on the terminal screen.

[0504] Input: Final summary information, action plan

[0505] Output: Message sent to user terminal (summary information and action plan)

[0506] (Application example 1)

[0507] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0508] The food delivery industry needs to respond quickly to changes in food safety standards and hygiene control laws, but the current system tends to be slow in obtaining regulatory information and proposing countermeasures. As a result, it is difficult for businesses to comply with the latest regulations, leading to problems such as reduced business efficiency and compliance.

[0509] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0510] In this invention, the server includes means for receiving requests related to laws and regulations, means for analyzing the received requests and extracting information related to specific laws and regulations, means for obtaining the latest information related to laws and regulations from an external database or external API, means for summarizing the obtained information on laws and regulations using a generative AI that concisely summarizes the information, means for selecting and generating appropriate countermeasures based on the summarized information on laws and regulations, means for transmitting the summarized information on laws and regulations and the countermeasures to a user terminal, means for proposing countermeasures for food delivery companies in accordance with new laws and regulations, means for reflecting the proposed countermeasures in the business system of the food delivery company, and means for providing an appropriate prompt sentence to the generative AI model. This enables food delivery companies to quickly obtain the latest information on laws and regulations and reflect the countermeasures based on the information in their business systems.

[0511] "Legal regulations" is a general term for laws, regulations, guidelines, etc. established by governments and regulatory bodies, and are rules that industries and companies must follow.

[0512] A "request" refers to an instruction or inquiry that a user sends to a server seeking some information or service.

[0513] "Analysis" refers to the process of analyzing the content of a received request and identifying necessary information.

[0514] "Information" refers to data and knowledge about legal regulations, the latest provisions and amendments, etc.

[0515] An "external database" is a large-scale data repository located outside the server, and is an information source accessible via the Internet.

[0516] An "external API" is an application program interface that provides a means to connect to external services and databases.

[0517] "Generative AI" is a type of artificial intelligence trained to perform specific tasks, specifically models capable of generating new information from data.

[0518] "Summarization" refers to the compilation of acquired regulatory information into a more concise format.

[0519] "Countermeasures" indicate specific actions or measures that businesses and users should take based on the summarized legal and regulatory information.

[0520] "User terminal" refers to a device used by a user, such as a smartphone, PC, or tablet, and is a medium for communicating with the server.

[0521] A "prompt" is an input sentence that instructs a generative AI to perform a specific task, and serves as an instruction for the AI ​​to generate an appropriate result.

[0522] This invention provides a system for the food delivery industry to quickly respond to new food safety standards and amendments to the Sanitation Control Act. This system performs all processes from obtaining legal information to proposing countermeasures, and is implemented by combining a server, user terminals, and generative AI models.

[0523] Overall system configuration

[0524] The system includes the following major components:

[0525] 1. User Device

[0526] A device through which a user can request new regulatory information, such as a smartphone, tablet, or computer.

[0527] 2. Server

[0528] Its role is to receive requests, analyze them, obtain information from external databases and APIs, summarize using generative AI, select and propose countermeasures, and send the results to the user's device.

[0529] 3. Generative AI Models

[0530] For example, language models such as OpenAI's GPT-4 are used to summarize legal information and generate prompts.

[0531] Program processing

[0532] The server performs the following steps:

[0533] 1. Receiving and parsing the request

[0534] A request such as "I would like to obtain the latest food safety standards" is received from the user terminal. The received request is analyzed on the server side to identify the required information.

[0535] 2. Obtaining legal and regulatory information

[0536] The server accesses an external database or API (e.g., a government regulatory database) to obtain the latest regulatory information.

[0537] 3. Summary of information

[0538] The acquired legal and regulatory information is passed to a generative AI model (e.g., GPT-4) to generate a concise summary. An example prompt is, "How can I comply with the latest food safety standards?"

[0539] 4. Selection and proposal of countermeasures

[0540] Based on the summarized information, the server selects specific countermeasures for food delivery companies, such as "Since all fresh vegetables are required to be washed before shipping, we recommend introducing fresh vegetable washing machines."

[0541] 5. Sending the results to the user device

[0542] The final summary information and countermeasures are sent to the user terminal so that the user can check them.

[0543] Specific examples

[0544] For example, if a food delivery company wants to know about new food safety standards, they might enter the following prompt:

[0545] "How can I comply with the latest food safety standards?"

[0546] The server analyzes this request and retrieves the latest information from an external database. Using a generative AI model, it generates a summary such as, "New food safety standards require all raw vegetables to be washed before shipping," and suggests a countermeasure such as, "We recommend installing a raw vegetable washing machine." This allows users to quickly and concisely obtain the latest regulatory information and take the necessary countermeasures.

[0547] As a result, the present invention enables the food delivery industry to quickly and efficiently comply with new regulations.

[0548] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0549] Step 1:

[0550] A user terminal generates and transmits a regulatory request.

[0551] Input: The user inputs a request into the terminal saying, "I would like to obtain the latest food safety standards."

[0552] Output: This request is sent from the user terminal to the server.

[0553] Specific operation: A user fills out a request form on a device such as a smartphone or tablet and presses the send button. This request is sent to the server as an HTTP request.

[0554] Step 2:

[0555] The server analyzes the received request and extracts specific regulatory information.

[0556] Input: A request sent from the user's device to "get the latest food safety standards."

[0557] Output: Extract the keyword "food safety standards" as the parsed regulatory information request.

[0558] What it does: The server uses a natural language processing (NLP) engine to analyze the request and extract key elements of the request (in this case, "food safety standards").

[0559] Step 3:

[0560] The server accesses an external database or API to retrieve the latest regulatory information.

[0561] Input: Request for "Food Safety Standards."

[0562] Output: The latest information on the acquired "Food Safety Standards."

[0563] How it works: The server sends an HTTP request to an external API (for example, a government regulatory database API) to obtain the latest data on "food safety standards." The obtained data is returned to the server in JSON format.

[0564] Step 4:

[0565] The server passes the acquired legal and regulatory information to a generative AI model for summarization.

[0566] Input: Latest information on "Food Safety Standards" (JSON data).

[0567] Output: A summary result from the generative AI model (e.g., "New food safety standards require all raw vegetables to be washed before shipping.").

[0568] How it works: The server converts the acquired information into text format and inputs it to a generative AI model (e.g., GPT-4) using a prompt sentence, such as "How can I comply with the latest food safety standards?". The AI ​​model generates a concise summary based on this prompt.

[0569] Step 5:

[0570] The server selects and generates appropriate countermeasures based on the summarized information.

[0571] Input: Summary result (e.g., "New food safety standards require all fresh vegetables to be washed before shipping.").

[0572] Output: The selected countermeasure (e.g., "We recommend installing a fresh vegetable washer").

[0573] Specific operation: The server checks the summary results and runs an algorithm to automatically select specific countermeasures for the contractor. It selects the optimal solution based on past countermeasures in the database and the contractor's request information.

[0574] Step 6:

[0575] The server transmits the generated summary information and countermeasures to the user terminal.

[0576] Input: Summary information and action plan (e.g., "New food safety standards require all fresh vegetables to be washed before shipping" and "We recommend installing fresh vegetable washers").

[0577] Output: The final result displayed on the user's terminal.

[0578] Specific operation: The server returns the generated summary information and countermeasures to the user device as an HTTP response. The user device receives this information and displays it on the screen. The user can then take the necessary measures based on this information.

[0579] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0580] This invention is a system that receives requests related to legal regulations, analyzes the requests to obtain information about specific legal regulations, summarizes them succinctly using generative AI, selects appropriate countermeasures, and provides them to the user, and further combines this with an emotion engine that recognizes the user's emotions. Here, we will explain the specific program processing in natural language.

[0581] Overall system overview

[0582] 1. Accepting user requests

[0583] A user inputs a request for information about a specific law and regulation from a terminal and presses the send button. For example, suppose a user inputs and sends "I want to know about amendments to the Labor Standards Act." The terminal then sends this request to the server.

[0584] 2. Receiving and parsing the request

[0585] The server receives a request sent from the terminal. The received request data is a specific legal topic for which the user is seeking confirmation. The server analyzes the request data and identifies the specific legal topic being sought. For example, the server identifies information about amendments to the Labor Standards Act from the user's request.

[0586] 3. Emotion analysis using an emotion engine

[0587] When the server receives a request, it uses an emotion engine to analyze the user's emotional state. For example, it reads emotions from the user's request text or voice input and determines whether the user is anxious or excited.

[0588] 4. Obtaining information on legal reforms

[0589] The server accesses a database of legal amendments or an external API to retrieve the latest information on the laws and regulations specified in the request. For example, it calls an external API to retrieve data on amendments to the Labor Standards Act.

[0590] 5. Simplifying information with AI

[0591] The acquired legal amendment information is passed to a generative AI, which then processes the information into a concise summary. The AI ​​extracts the key points of the legal amendment and creates a short summary. For example, a summary such as "The upper limit on working hours has been changed to 45 hours per month" is generated.

[0592] 6. Propose solutions based on emotions

[0593] The server selects countermeasures for small and medium-sized enterprises based on the summarized legal amendment information and the user's emotional state recognized by the emotion engine. For example, if it recognizes that the user is feeling anxious, it selects content that strongly recommends a work hour management system.

[0594] 7. Individual customization

[0595] The server then generates a customized version of the selected solution based on the user's emotional state. It then generates information containing details of the solution and formats it for delivery to the user. For example, if the user's emotion is "anxiety," it also presents contact information for the support desk.

[0596] 8. Providing Results to Users

[0597] The server sends the generated summary information and countermeasures to the user's terminal, which receives this information and displays it to the user. Based on this information, the user can understand the specific countermeasures and proceed with the necessary procedures.

[0598] Specific examples

[0599] Responding to the revision of the Labor Standards Act

[0600] The user sends a request from their device saying, "I'd like to know about the revisions to the Labor Standards Act." The server receives this request, and its emotion engine recognizes that the user's emotion is "anxiety." The server retrieves the latest information about the revisions to the Labor Standards Act from an external API, and the generative AI summarizes it as "The upper limit on working hours has been changed to 45 hours per month." Based on this summary, the server takes into account the user's emotional state and makes a proposal strongly recommending the "introduction of a working hours management system," along with contact information for a support desk. Finally, the server sends this summary and a customized solution to the user's device, where the user can confirm the content on their device.

[0601] Through the above process, users (agency staff) can understand the details of legal changes and the appropriate countermeasures to them in a way that takes into account the user's emotional state, and can immediately provide appropriate advice to clients (small and medium-sized enterprises), thereby improving work efficiency and service.

[0602] The processing flow will be explained below.

[0603] Step 1:

[0604] A user inputs a request for information about a specific law or regulation into a terminal and presses the send button. For example, the user inputs a request such as "I want to know about amendments to the Labor Standards Act." The terminal then sends this request to the server.

[0605] Step 2:

[0606] The server receives a request sent from the terminal, the request data including the specific regulatory topic of interest to the user.

[0607] Step 3:

[0608] The server analyzes the received request data and extracts information about specific laws and regulations. In this case, it extracts information about amendments to the Labor Standards Act.

[0609] Step 4:

[0610] The server uses an emotion engine to analyze the user's emotional state, for example, by recognizing whether the user is feeling anxious based on the request or input text.

[0611] Step 5:

[0612] The server accesses an external database or external API to obtain the latest information on the identified laws and regulations. The server obtains amendment data for the Labor Standards Act from the external API.

[0613] Step 6:

[0614] The acquired legal and regulatory information is passed to a generative AI, which then summarises the information concisely. For example, it might generate a summary such as, "The upper limit on working hours has been changed to 45 hours per month."

[0615] Step 7:

[0616] The server selects countermeasures for small and medium-sized enterprises based on the summarized legal and regulatory information and the user's emotional state recognized by the emotion engine. For example, if the user feels anxious, it will strongly recommend the introduction of a working time management system.

[0617] Step 8:

[0618] The server then generates a customized version of the selected solution, including, for example, a detailed explanation of how to implement the time management system and contact information for support.

[0619] Step 9:

[0620] The server sends the generated summary information and the customized countermeasures to the user terminal, and the terminal displays the received information to the user.

[0621] Step 10:

[0622] The user can check the information provided on the device and understand specific countermeasures. For example, they can check how to implement a working time management system and contact support. Based on this information, the user can provide appropriate advice to the client.

[0623] Example 2

[0624] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0625] Conventional legal information provision systems have difficulty in quickly and accurately providing specific legal information that users desire. They also lack the ability to propose appropriate countermeasures based on the user's emotions and circumstances. Therefore, there is a need for efficient information provision and countermeasure proposals that take the user's emotions into consideration.

[0626] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0627] In this invention, the server includes means for receiving requests related to laws and regulations, means for analyzing the received requests and extracting information related to specific laws and regulations, means for analyzing user sentiment, means for acquiring the latest information related to laws and regulations from an external database or external API, means for summarizing the acquired legal and regulatory information using a generative AI that concisely summarizes the acquired legal and regulatory information, means for selecting and generating appropriate countermeasures based on the summarized legal and regulatory information and the user's sentiment, and means for providing the generated countermeasures to the user. This makes it possible to respond to user requests quickly and accurately and to propose appropriate countermeasures that take the user's sentiment into consideration.

[0628] A "regulatory request" is a request made by a user to the system for information about a specific regulation.

[0629] "Analysis" is a processing means for analyzing received data and understanding its contents.

[0630] "Emotion analysis" is a means of identifying and identifying a user's emotional state from their input and behavior.

[0631] An "external database" is a database that exists outside the system and stores data related to laws and regulations.

[0632] An "external API" is an application program interface for communicating with external services and obtaining data.

[0633] "Generative AI" is an AI that learns from large amounts of data and performs natural language processing. It is an AI module that is used, for example, to concisely summarize legal and regulatory information.

[0634] "Summarization" is the process of summarizing detailed information into a concise and easy-to-understand form.

[0635] "Appropriate responses" are specific guidelines or recommendations that the system provides based on the user's request or emotional state.

[0636] "Providing" means giving information or services to a user.

[0637] This system receives requests related to legal regulations, analyzes the requests to obtain information about specific legal regulations, summarizes them succinctly using generative AI, selects appropriate countermeasures, and provides them to the user. It also incorporates an emotion engine that recognizes the user's emotions.

[0638] Overall system overview

[0639] 1. Accepting user requests

[0640] The user inputs a request for information about a specific law and regulation into the terminal and presses the send button. For example, suppose the user inputs "I want to know about amendments to the Labor Standards Act" and sends it. The terminal then sends this request to the server.

[0641] 2. Receiving and parsing the request

[0642] The server receives a request sent from a terminal. The received request data includes a specific legal and regulatory topic. The server analyzes the request data and identifies the specific legal and regulatory topic. For example, it identifies a request regarding "amendments to the Labor Standards Act."

[0643] 3. Emotion analysis using an emotion engine

[0644] When the server receives a request, it uses an emotion engine to analyze the user's emotional state. For example, it reads emotions from the user's request text or voice input and determines whether the user is anxious or excited.

[0645] 4. Obtaining information on legal reforms

[0646] The server accesses a database of legal amendments or an external API to obtain the latest information on the laws and regulations specified in the request. For example, it calls an external API to obtain "Labor Standards Act amendment data."

[0647] 5. Simplifying information with AI

[0648] The server then passes the acquired legal change information to a generative AI, which then summarizes the information concisely. This generative AI uses an advanced natural language processing model such as GPT-4. For example, it generates a summary such as, "The upper limit on working hours has been changed to 45 hours per month."

[0649] 6. Propose solutions based on emotions

[0650] The server selects appropriate countermeasures based on the summarized legal amendment information and the user's emotional state recognized by the emotion engine. For example, if the user is feeling anxious, it will select a solution that strongly recommends a work time management system.

[0651] 7. Individual customization

[0652] The server then generates a customized response based on the user's emotional state. For example, it can add support contact information to the response to reassure the user.

[0653] 8. Providing Results to Users

[0654] The server sends the generated summary information and countermeasures to the user's terminal, which receives this information and displays it to the user. Based on this information, the user can understand the specific countermeasures and proceed with the necessary procedures.

[0655] Specific examples

[0656] The user sends a request from their device saying, "I'd like to know about the revisions to the Labor Standards Act." The server receives this request, and its emotion engine recognizes the user's emotion as "anxiety." The server retrieves the latest information on legal revisions from an external API, and the generative AI summarizes it as "The upper limit on working hours has been changed to 45 hours per month." Based on this summary, the server takes into account the user's emotional state and makes a proposal strongly recommending the "introduction of a working hours management system," along with contact information for a support desk. Finally, the server sends this summary and a customized solution to the user's device, where the user can view the content on their device.

[0657] In this way, users can understand the details of the legal changes and the appropriate measures to take in response, and receive support in taking concrete action.

[0658] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0659] Step 1:

[0660] The user inputs a request for information about a specific law and regulation into the terminal and presses the send button.

[0661] Input: The user enters the text "I want to know about the revision of the Labor Standards Act."

[0662] Action: The user enters text into the input field and clicks the "Submit" button.

[0663] Output: The terminal sends the input request text to the server.

[0664] Step 2:

[0665] The server receives the request sent from the terminal.

[0666] Input: Request data in the HTTP request sent from the terminal.

[0667] What it does: The server extracts text data from the body of the HTTP request.

[0668] Output: Extracted text data (e.g., "I would like to know about the revision of the Labor Standards Act").

[0669] Step 3:

[0670] The server analyzes the received request data to identify specific regulatory topics.

[0671] Input: Text data (e.g., "I would like to know about the revision of the Labor Standards Act").

[0672] How it works: The server uses natural language processing libraries to parse the request and extract specific regulatory topics.

[0673] Output: Extracted topics (e.g., "Amendments to the Labor Standards Act").

[0674] Step 4:

[0675] When the server receives a request, it uses an emotion engine to analyze the user's emotional state.

[0676] Input: Request text (e.g., "I would like to know about the revision of the Labor Standards Act").

[0677] Operation: The server calls the emotion engine API to perform emotion analysis.

[0678] Output: User's emotional state (e.g., anxiety).

[0679] Step 5:

[0680] The server accesses a legal change database or external API to retrieve the latest information about the laws and regulations identified in the request.

[0681] Input: Identified legal and regulatory topic (e.g., "Amendments to the Labor Standards Act").

[0682] How it works: The server sends a request to an external API to get the latest regulatory information.

[0683] Output: Retrieved legal change information data.

[0684] Step 6:

[0685] The server passes the legal amendment information it has acquired to a generative AI, which then summarizes it concisely.

[0686] Input: Obtained legal reform information data.

[0687] How it works: The server inputs data into a generative AI model and generates a summary result.

[0688] Output: Summarized legal change information (e.g., "The maximum working hours has been changed to 45 hours per month").

[0689] Step 7:

[0690] The server selects appropriate countermeasures based on the summarized legal amendment information and the user's emotional state recognized by the emotion engine.

[0691] Input: Abstracted legal change information and the user's emotional state.

[0692] Action: The server chooses a course of action based on the conditions.

[0693] Output: Selected solution (e.g., "Implementation of a working time management system").

[0694] Step 8:

[0695] The server then customizes the selected response individually based on the user's emotional state.

[0696] Input: The selected response and the user's emotional state.

[0697] Operation: The server customizes the solution by adding contact information for the support desk, etc.

[0698] Output: Customized action information (e.g. "Implementing a working time management system. Contact support here.").

[0699] Step 9:

[0700] The server transmits the generated summary information and countermeasures to the user terminal.

[0701] Input: Customized workaround information.

[0702] Operation: The server formats the countermeasure information in JSON format as an HTTP response and sends it to the device.

[0703] Output: Summary information and remedial actions sent to the user terminal.

[0704] Step 10:

[0705] The terminal displays the information received from the server to the user.

[0706] Input: Summary information and remedial actions received from the server.

[0707] Behavior: The user device parses the received JSON data and formats it as text for display in the user interface.

[0708] Output: On-screen summary information and suggested actions.

[0709] (Application example 2)

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

[0711] In situations where local legal regulations must be addressed, it is difficult for security staff to quickly obtain appropriate information. Furthermore, appropriate responses that take into account the local situation and the user's emotional state may not be provided, potentially resulting in a decline in the quality of responses. This can lead to issues such as delayed legal responses and an increased risk of incorrect actions.

[0712] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving a request related to laws and regulations; means for analyzing the received request and extracting information related to the specific laws and regulations; means for acquiring the latest information related to laws and regulations from an external database or an external API; means for summarizing the acquired laws and regulations using a generative AI that concisely summarizes the acquired laws and regulations; means for selecting and generating appropriate countermeasures based on the summarized laws and regulations information; emotion analysis engine means for analyzing the user's emotional state; means for customizing the summary information and countermeasures based on the emotional state; and means for providing the user with appropriate countermeasures and customized information. This enables security staff to quickly obtain appropriate laws and regulations information and countermeasures on site and take appropriate action according to the situation.

[0713] A "regulatory request" is a request sent by a user to a server for information about a particular regulation.

[0714] "Means for receiving" refers to a device or method for receiving a request sent from a user terminal.

[0715] "Means for parsing and extracting information relating to specific regulations" refers to a device or method that analyzes the received request and extracts information relating to specific regulations therefrom.

[0716] "External Database or External API" refers to an external database or application programming interface that provides regulatory information.

[0717] "Means for obtaining the latest information" refers to a device or method for collecting the latest information on laws and regulations from an external database or external API.

[0718] "Generative AI" refers to an artificial intelligence system that generates new information based on input information.

[0719] A "means for concisely summarizing" refers to a device or method for concisely summarizing acquired information to the main points.

[0720] "Means of summarizing" refers to a method of using generative AI to extract and summarize complex information in a concise form.

[0721] "Means for selecting and generating appropriate countermeasures" refers to a device or method for selecting and generating the most appropriate countermeasures based on the summarized regulatory information.

[0722] An "emotion analysis engine" refers to a system that analyzes a user's emotional state and outputs the results.

[0723] "Means for customizing summary information and responses based on emotional state" refers to a device or method that individually tailors summary information and selected responses based on the analysis results of an emotion analysis engine.

[0724] "Means for providing to the user" refers to a device or method for delivering the generated information and countermeasures to the user.

[0725] This invention is a system that receives requests related to laws and regulations, analyzes the requests to obtain information about specific laws and regulations, summarizes the information concisely using generative AI, selects appropriate countermeasures, and provides them to the user. This system is composed of a sentiment analysis engine that recognizes the user's emotions.

[0726] Overall system overview

[0727] 1. Accepting user requests

[0728] First, a user inputs a request for information about a specific law or regulation from the device. For example, the user might say through the smart glasses, "I want to know about the legal procedures when a suspicious person is found at the scene." This voice request is sent by the device to the server.

[0729] 2. Receiving and parsing the request

[0730] The server receives the request sent from the terminal. The received request data is the specific legal topic for which the user is seeking confirmation. The server analyzes the request data and identifies the specific legal topic being sought. In this case, it is identified as "Legal Procedures Regarding Suspicious Persons."

[0731] 3. Emotion analysis using an emotion engine

[0732] When the server receives a request, it uses an emotion analysis engine to analyze the user's emotional state. For example, it can read emotions from the user's request or voice input and determine whether the user is nervous.

[0733] 4. Obtaining legal and regulatory information

[0734] The server then accesses a database of laws and regulations or an external API to retrieve the latest information on the laws and regulations specified in the request. For example, it may call an external API to retrieve the latest data on the Peace Preservation Act or legal procedures for dealing with suspicious persons.

[0735] 5. Simplifying information with AI

[0736] The acquired legal and regulatory information is passed to a generative AI, which then processes it into a concise summary. For example, it might generate a summary such as, "When arresting a suspicious person, immediately contact the police and ensure safety at the scene."

[0737] 6. Propose solutions based on emotions

[0738] The server selects appropriate countermeasures based on the summarized legal information and the user's emotional state as determined by an emotion analysis engine. For example, if the server determines that the user is nervous, it selects content that emphasizes how to quickly contact the police or praises the first responders.

[0739] 7. Individual customization

[0740] The server generates a customized response based on the selected emotional state, and generates information including details of the response in a format that can be provided to the user.

[0741] 8. Providing Results to Users

[0742] The server sends the generated summary information and countermeasures to the user device (smart glasses). The user device receives this information and displays it to the user. Based on this information, the user can understand the specific countermeasures and proceed with the necessary procedures.

[0743] Hardware and software used

[0744] Hardware

[0745] Smart glasses: Wearable devices with voice recognition and display capabilities (e.g., Google Glass, Microsoft HoloLens)

[0746] software

[0747] Sentiment analysis engine: Python Sentiment Analysis library (e.g. textblob)

[0748] Generative AI: OpenAI's GPT-3 or GPT-4 API

[0749] Data acquisition: Realized using RESTful API (e.g., regulatory information API)

[0750] Prompt Sentence Examples

[0751] “Based on the summarized information about security regulations and considering that the user is anxious feeling, provide a suitable solution to handle unidentified persons in restricted areas.”

[0752] This system allows security staff to quickly obtain appropriate legal and regulatory information and countermeasures on-site, and to take the most appropriate action depending on the situation through sentiment analysis.

[0753] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0754] Step 1:

[0755] The user inputs a request for information about specific laws and regulations from the device. The request is generated by the user speaking into the smart glasses, such as "I would like to know about the legal procedures when a suspicious person is discovered at the scene." The input voice data is converted into text format, and the device sends the request to the server.

[0756] Step 2:

[0757] The server receives a request sent from the terminal. The received request data is text data containing a specific legal topic for which the user is seeking confirmation. The server analyzes this text data and identifies the specific legal topic being sought. In this case, "Legal procedures regarding suspicious persons" is extracted.

[0758] Step 3:

[0759] The server passes the received request to an emotion analysis engine, which analyzes the user's emotional state. Using the text data extracted from the voice request as input, the emotion analysis engine identifies the user's emotional state (e.g., nervousness, anxiety). The analysis result is that the user is "nervous."

[0760] Step 4:

[0761] The server accesses a regulatory database or external API to retrieve the latest information related to the identified regulatory topic. For example, to retrieve data on the Peace Preservation Act or legal procedures for dealing with suspicious persons, the server sends a request to the API endpoint and retrieves the required data.

[0762] Step 5:

[0763] The acquired information is passed to a generative AI, which then summarises it concisely. Using legal and regulatory data as input, the generative AI extracts key points and outputs a concise summary. For example, a generated summary might read, "When arresting a suspicious individual, contact the police immediately and ensure safety at the scene."

[0764] Step 6:

[0765] The server selects appropriate countermeasures based on the generated summary information and the user's emotional state recognized by the emotion analysis engine. Countermeasures are generated using the analysis results and summary data as input, such as "If the user is nervous, emphasize how to quickly contact the police or praise the first responders."

[0766] Step 7:

[0767] The server then generates and customizes the selected countermeasures based on the user's emotional state. Using the countermeasures and emotional data as input, the server customizes the countermeasures based on the user's emotional state, and generates specific details of the countermeasures. For example, the server adds details to the countermeasures, such as "procedures for contacting the public security department" and "the importance of initial response."

[0768] Step 8:

[0769] The server sends the customized summary information and countermeasures to the user's device. The device receives the generated data as input and displays the summary of legal proceedings and countermeasures on the smart glasses display. The user can review this information and take appropriate action based on it.

[0770] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0771] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0772] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0773] [Third embodiment]

[0774] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0775] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0776] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0777] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0778] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0779] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0780] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0781] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0782] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0783] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0784] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0785] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0786] This invention is a system that receives requests related to legal regulations, analyzes the requests to obtain information about specific legal regulations, summarizes the information concisely using generative AI, selects appropriate countermeasures, and provides them to the user. Here, we will explain the specific program processing in natural language.

[0787] Overall system overview

[0788] 1. Accepting user requests

[0789] A user sends a request for information about a specific law or regulation from a terminal. For example, suppose the user types and submits "I want to know about amendments to the Labor Standards Act." The terminal then sends this request to the server.

[0790] 2. Receiving and parsing the request

[0791] The server receives the request from the device and analyzes the content to identify information about the specific law and regulation. This analysis determines the specific legal reform topic the user is seeking (in this case, amendments to the Labor Standards Act).

[0792] 3. Obtaining information on legal reforms

[0793] The server accesses a database of legal amendments or an external API to retrieve the latest information about the laws and regulations specified in the request, for example, the latest provisions and explanations regarding amendments to the Labor Standards Act.

[0794] 4. Simplifying information with AI

[0795] The acquired legal amendment information is passed to a generative AI, which then summarises the information concisely. For example, it might generate a summary such as "The upper limit on working hours has been changed to 45 hours per month."

[0796] 5. Solution Proposal

[0797] The server selects countermeasures for small and medium-sized enterprises based on the summarized information on legal amendments, for example, generating recommendations for the introduction of a working time management system.

[0798] 6. Providing Results to Users

[0799] The server sends the generated summary information and countermeasures to the user's terminal, where the user can view the information.

[0800] Specific examples

[0801] Responding to the revision of the Labor Standards Act

[0802] The user sends a request from their device saying, "I want to know about amendments to the Labor Standards Act." The server receives this request and retrieves the latest information on amendments to the Labor Standards Act from an external API. The generative AI summarizes the information it retrieves as "The upper limit on working hours has been changed to 45 hours per month," and based on this, the server proposes "the introduction of a working hours management system" as a solution. Finally, the server sends this summary information and solution to the user's device, and the user can confirm the details on their device.

[0803] Through the above process, users (agency staff) can easily understand the details of legal amendments and immediately provide appropriate countermeasures to their clients (small and medium-sized enterprises), thereby improving business efficiency and service.

[0804] The processing flow will be explained below.

[0805] Step 1:

[0806] The user inputs a request for information on legal regulations into the terminal and presses the send button, which then sends the request to the server.

[0807] Step 2:

[0808] The server receives the request sent from the terminal, and the received request data is a specific regulatory topic for which the user wishes to confirm.

[0809] Step 3:

[0810] The server parses the request data to identify the specific regulatory topic being sought, specifically extracting regulatory categories and specific amendments from the request data.

[0811] Step 4:

[0812] The server accesses an external database or external API to retrieve the latest information on the identified legal and regulatory topic. For example, an external API is called to retrieve revision data for the Labor Standards Act.

[0813] Step 5:

[0814] The server passes the acquired legal information to a generative AI, which then summarizes the information concisely. The AI ​​extracts the key points of the legal regulations and provides a short summary.

[0815] Step 6:

[0816] The server selects countermeasures for small and medium-sized enterprises based on the summarized legal and regulatory information. For example, if there is a change in the upper limit of working hours, the server will suggest the introduction of a working hour management system.

[0817] Step 7:

[0818] The server generates information about the selected countermeasure, including details of the countermeasure, and formats the information in a format that can be provided to the user.

[0819] Step 8:

[0820] The server sends the generated summary information and countermeasures to the user terminal, which receives the information and displays it to the user.

[0821] Step 9:

[0822] The user checks the information provided on the device and understands the specific countermeasures. Based on this information, the user provides appropriate advice to the client.

[0823] Example 1

[0824] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0825] Currently, many companies and individuals need to respond quickly to changes in laws and regulations, but it is difficult to understand the necessary content from the vast amount of information and determine appropriate countermeasures. Since it is particularly difficult for small and medium-sized enterprises and individuals to collect and apply information about changes in laws and regulations, they are required to obtain information efficiently and accurately and provide appropriate countermeasures based on that information.

[0826] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0827] In this invention, the server includes means for receiving requests related to laws and regulations, means for analyzing the received requests and extracting information related to specific laws and regulations, means for obtaining the latest information related to laws and regulations from an external database or external API, means for summarizing the obtained legal and regulatory information using a generative AI that concisely summarizes the obtained legal and regulatory information, means for selecting and generating appropriate countermeasures based on the summarized legal and regulatory information, means for providing the generated countermeasures to a user, means for analyzing data obtained from external information sources using natural language processing technology, and means for instructing the generative AI model to summarize the legal and regulatory information using prompt sentences. This enables users to quickly and efficiently respond to changes in laws and regulations and obtain accurate information and appropriate countermeasures.

[0828] "Legal regulations" refer to rules and laws enacted by government agencies such as countries and regions, which companies and individuals must comply with.

[0829] A "request" is a request that a user sends to a system for particular information or service.

[0830] "Receiving means" refers to the function or device that allows the server to receive requests sent from the user.

[0831] "Analysis means" refers to the processing method or software used to understand the content of the received request and extract the necessary information.

[0832] An "external database" is an external information aggregation system that stores information related to laws and regulations.

[0833] An "external API" is an interface for data exchange with other systems and services.

[0834] "Generative AI" refers to an artificial intelligence model that automatically generates sentences based on given data and questions.

[0835] "Summarization means" refers to a processing method or software for concisely summarizing acquired information.

[0836] A "solution" is a specific measure or action that should be taken in response to a particular problem or situation.

[0837] "Natural language processing technology" refers to technology that allows computers to understand, interpret, and generate human language.

[0838] A "prompt" is an instruction given to a generative AI when it is put into operation, and serves as a guideline for the AI ​​to generate the necessary information.

[0839] The present invention is a system that receives requests related to laws and regulations, analyzes the requests to obtain information about specific laws and regulations, summarizes the information concisely using generative AI, selects appropriate countermeasures, and provides them to users. The following describes how to specifically implement the present invention.

[0840] Overall overview

[0841] 1. Accepting user requests

[0842] A user uses their device to input and send a request for information about a specific law or regulation. For example, the user might write, "I want to know about amendments to the Labor Standards Act," and click the send button. The device then sends this request to the server.

[0843] 2. Receiving and parsing the request

[0844] The server receives a request sent from the user's device. The server analyzes the request and identifies the specific legal and regulatory topic the user is looking for. This analysis is performed using natural language processing (NLP) technology. As a result of the analysis, for example, "Amendments to the Labor Standards Act" may be identified as a topic.

[0845] 3. Obtaining legal and regulatory information

[0846] The server accesses external databases and APIs to obtain the latest information on specified laws and regulations. Specifically, it uses external information sources such as the "Government Gazette API" and "Legal Data Provision Service." The server sends an API request, analyzes the received data, and obtains the latest provisions and explanations.

[0847] 4. AI-powered information summarization

[0848] The server passes the acquired legal and regulatory information to a generative AI model (e.g., "OpenAI GPT-4"). The generative AI model then succinctly summarizes the information provided. This summarization process uses a pre-set prompt (e.g., "Please briefly summarize the latest amendments to the Labor Standards Act").

[0849] 5. Solution Proposal

[0850] The server selects countermeasures based on specific conditions (for example, for small and medium-sized enterprises) based on the information summarized by the generative AI model. In doing so, the server refers to a list of proposal candidates from an internal database and recommends, for example, the introduction of a working hours management system.

[0851] 6. Providing results to users

[0852] The server sends the final summary information and countermeasures to the user's device. The user can check this information on their own device. For example, a message such as "The upper limit on working hours has been changed to 45 hours per month. As a countermeasure, we recommend introducing a working hours management system" will be displayed on the user's device screen.

[0853] Specific examples

[0854] The user sends a request from their device saying, "I would like to know about amendments to the Labor Standards Act." The server receives this request and uses NLP technology to identify the topic "Amendments to the Labor Standards Act." The server accesses the "Government Gazette API" to obtain data on this year's amendments to the Labor Standards Act. The obtained data is passed to a generative AI model (OpenAI GPT-4) and the prompt is used: "Please briefly summarize the latest amendments to the Labor Standards Act." The generative AI model generates a summary: "The upper limit on working hours has been changed to 45 hours per month." The server selects a solution from its internal database that recommends "introducing a working hour management system," and sends the final summary information and solution to the user's device. The user checks the results on their device.

[0855] In this way, the system of the present invention allows users to quickly and efficiently respond to changes in laws and regulations and obtain accurate information and appropriate countermeasures.

[0856] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0857] Step 1:

[0858] A user uses their device to input and send a request for information about a specific law or regulation. Specifically, the user writes "I would like to know about amendments to the Labor Standards Act" and clicks the send button. This request is sent from the device to the server as an HTTP POST request.

[0859] Input: The regulatory request entered by the user into the device

[0860] Output: HTTP POST request sent to the server

[0861] Step 2:

[0862] The server receives the request sent from the user terminal. The server analyzes the content of the received request using natural language processing (NLP) technology to identify specific legal and regulatory topics. For example, this analysis extracts the topic "Amendments to the Labor Standards Act."

[0863] Input: HTTP POST request received by the server

[0864] Output: Parsed topic (e.g. "Amendment to the Labor Standards Act")

[0865] Step 3:

[0866] The server accesses external databases and APIs to obtain the latest information on specified laws and regulations. Specifically, the server sends API requests to external information sources such as "Government Gazette API" and "Legal Data Service" to obtain relevant data. This obtained data includes the latest provisions and explanations.

[0867] Input: Parsed topic, external database or API endpoint

[0868] Output: The latest legal information (e.g., latest provisions and explanations)

[0869] Step 4:

[0870] The server passes the acquired legal and regulatory information to a generative AI model (e.g., OpenAI GPT-4). Here, the server sets a prompt and instructs the generative AI model to provide a concise summary. The prompt is set to something like, "Please briefly summarize the latest amendments to the Labor Standards Act." The generative AI model generates a summary based on this prompt.

[0871] Input: Obtained legal information, prompt text

[0872] Output: The generated summary (e.g. "The working hour limit has been changed to 45 hours per month")

[0873] Step 5:

[0874] Based on the information summarized by the generative AI model, the server selects countermeasures according to specific conditions (for example, for small and medium-sized enterprises). The server refers to a list of proposal candidates from an internal database and selects the optimal countermeasure (for example, "introduction of a working hour management system").

[0875] Input: Generated summary, list of suggestion candidates from internal database

[0876] Output: Selected solution (e.g., "Introduce a working time management system")

[0877] Step 6:

[0878] The server sends the final summary information and countermeasures to the user's terminal. The user can check this information on their own terminal. For example, a message such as "The upper limit of working hours has been changed to 45 hours per month. As a countermeasure, we recommend introducing a working hours management system" will be displayed on the terminal screen.

[0879] Input: Final summary information, action plan

[0880] Output: Message sent to user terminal (summary information and action plan)

[0881] (Application example 1)

[0882] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0883] The food delivery industry needs to respond quickly to changes in food safety standards and hygiene control laws, but the current system tends to be slow in obtaining regulatory information and proposing countermeasures. As a result, it is difficult for businesses to comply with the latest regulations, leading to problems such as reduced business efficiency and compliance.

[0884] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0885] In this invention, the server includes means for receiving requests related to laws and regulations, means for analyzing the received requests and extracting information related to specific laws and regulations, means for obtaining the latest information related to laws and regulations from an external database or external API, means for summarizing the obtained information on laws and regulations using a generative AI that concisely summarizes the information, means for selecting and generating appropriate countermeasures based on the summarized information on laws and regulations, means for transmitting the summarized information on laws and regulations and the countermeasures to a user terminal, means for proposing countermeasures for food delivery companies in accordance with new laws and regulations, means for reflecting the proposed countermeasures in the business system of the food delivery company, and means for providing an appropriate prompt sentence to the generative AI model. This enables food delivery companies to quickly obtain the latest information on laws and regulations and reflect the countermeasures based on the information in their business systems.

[0886] "Legal regulations" is a general term for laws, regulations, guidelines, etc. established by governments and regulatory bodies, and are rules that industries and companies must follow.

[0887] A "request" refers to an instruction or inquiry that a user sends to a server seeking some information or service.

[0888] "Analysis" refers to the process of analyzing the content of a received request and identifying necessary information.

[0889] "Information" refers to data and knowledge about legal regulations, the latest provisions and amendments, etc.

[0890] An "external database" is a large-scale data repository located outside the server, and is an information source accessible via the Internet.

[0891] An "external API" is an application program interface that provides a means to connect to external services and databases.

[0892] "Generative AI" is a type of artificial intelligence trained to perform specific tasks, specifically models capable of generating new information from data.

[0893] "Summarization" refers to the compilation of acquired regulatory information into a more concise format.

[0894] "Countermeasures" indicate specific actions or measures that businesses and users should take based on the summarized legal and regulatory information.

[0895] "User terminal" refers to a device used by a user, such as a smartphone, PC, or tablet, and is a medium for communicating with the server.

[0896] A "prompt" is an input sentence that instructs a generative AI to perform a specific task, and serves as an instruction for the AI ​​to generate an appropriate result.

[0897] This invention provides a system for the food delivery industry to quickly respond to new food safety standards and amendments to the Sanitation Control Act. This system performs all processes from obtaining legal information to proposing countermeasures, and is implemented by combining a server, user terminals, and generative AI models.

[0898] Overall system configuration

[0899] The system includes the following major components:

[0900] 1. User Device

[0901] A device through which a user can request new regulatory information, such as a smartphone, tablet, or computer.

[0902] 2. Server

[0903] Its role is to receive requests, analyze them, obtain information from external databases and APIs, summarize using generative AI, select and propose countermeasures, and send the results to the user's device.

[0904] 3. Generative AI Models

[0905] For example, language models such as OpenAI's GPT-4 are used to summarize legal information and generate prompts.

[0906] Program processing

[0907] The server performs the following steps:

[0908] 1. Receiving and parsing the request

[0909] A request such as "I would like to obtain the latest food safety standards" is received from the user terminal. The received request is analyzed on the server side to identify the required information.

[0910] 2. Obtaining legal and regulatory information

[0911] The server accesses an external database or API (e.g., a government regulatory database) to obtain the latest regulatory information.

[0912] 3. Summary of information

[0913] The acquired legal and regulatory information is passed to a generative AI model (e.g., GPT-4) to generate a concise summary. An example prompt is, "How can I comply with the latest food safety standards?"

[0914] 4. Selection and proposal of countermeasures

[0915] Based on the summarized information, the server selects specific countermeasures for food delivery companies, such as "Since all fresh vegetables are required to be washed before shipping, we recommend introducing fresh vegetable washing machines."

[0916] 5. Sending the results to the user device

[0917] The final summary information and countermeasures are sent to the user terminal so that the user can check them.

[0918] Specific examples

[0919] For example, if a food delivery company wants to know about new food safety standards, they might enter the following prompt:

[0920] "How can I comply with the latest food safety standards?"

[0921] The server analyzes this request and retrieves the latest information from an external database. Using a generative AI model, it generates a summary such as, "New food safety standards require all raw vegetables to be washed before shipping," and suggests a countermeasure such as, "We recommend installing a raw vegetable washing machine." This allows users to quickly and concisely obtain the latest regulatory information and take the necessary countermeasures.

[0922] As a result, the present invention enables the food delivery industry to quickly and efficiently comply with new regulations.

[0923] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0924] Step 1:

[0925] A user terminal generates and transmits a regulatory request.

[0926] Input: The user inputs a request into the terminal saying, "I would like to obtain the latest food safety standards."

[0927] Output: This request is sent from the user terminal to the server.

[0928] Specific operation: A user fills out a request form on a device such as a smartphone or tablet and presses the send button. This request is sent to the server as an HTTP request.

[0929] Step 2:

[0930] The server analyzes the received request and extracts specific regulatory information.

[0931] Input: A request sent from the user's device to "get the latest food safety standards."

[0932] Output: Extract the keyword "food safety standards" as the parsed regulatory information request.

[0933] What it does: The server uses a natural language processing (NLP) engine to analyze the request and extract key elements of the request (in this case, "food safety standards").

[0934] Step 3:

[0935] The server accesses an external database or API to retrieve the latest regulatory information.

[0936] Input: Request for "Food Safety Standards."

[0937] Output: The latest information on the acquired "Food Safety Standards."

[0938] How it works: The server sends an HTTP request to an external API (for example, a government regulatory database API) to obtain the latest data on "food safety standards." The obtained data is returned to the server in JSON format.

[0939] Step 4:

[0940] The server passes the acquired legal and regulatory information to a generative AI model for summarization.

[0941] Input: Latest information on "Food Safety Standards" (JSON data).

[0942] Output: A summary result from the generative AI model (e.g., "New food safety standards require all raw vegetables to be washed before shipping.").

[0943] How it works: The server converts the acquired information into text format and inputs it to a generative AI model (e.g., GPT-4) using a prompt sentence, such as "How can I comply with the latest food safety standards?". The AI ​​model generates a concise summary based on this prompt.

[0944] Step 5:

[0945] The server selects and generates appropriate countermeasures based on the summarized information.

[0946] Input: Summary result (e.g., "New food safety standards require all fresh vegetables to be washed before shipping.").

[0947] Output: The selected countermeasure (e.g., "We recommend installing a fresh vegetable washer").

[0948] Specific operation: The server checks the summary results and runs an algorithm to automatically select specific countermeasures for the contractor. It selects the optimal solution based on past countermeasures in the database and the contractor's request information.

[0949] Step 6:

[0950] The server transmits the generated summary information and countermeasures to the user terminal.

[0951] Input: Summary information and action plan (e.g., "New food safety standards require all fresh vegetables to be washed before shipping" and "We recommend installing fresh vegetable washers").

[0952] Output: The final result displayed on the user's terminal.

[0953] Specific operation: The server returns the generated summary information and countermeasures to the user device as an HTTP response. The user device receives this information and displays it on the screen. The user can then take the necessary measures based on this information.

[0954] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0955] This invention is a system that receives requests related to legal regulations, analyzes the requests to obtain information about specific legal regulations, summarizes them succinctly using generative AI, selects appropriate countermeasures, and provides them to the user, and further combines this with an emotion engine that recognizes the user's emotions. Here, we will explain the specific program processing in natural language.

[0956] Overall system overview

[0957] 1. Accepting user requests

[0958] A user inputs a request for information about a specific law and regulation from a terminal and presses the send button. For example, suppose a user inputs and sends "I want to know about amendments to the Labor Standards Act." The terminal then sends this request to the server.

[0959] 2. Receiving and parsing the request

[0960] The server receives a request sent from the terminal. The received request data is a specific legal topic for which the user is seeking confirmation. The server analyzes the request data and identifies the specific legal topic being sought. For example, the server identifies information about amendments to the Labor Standards Act from the user's request.

[0961] 3. Emotion analysis using an emotion engine

[0962] When the server receives a request, it uses an emotion engine to analyze the user's emotional state. For example, it reads emotions from the user's request text or voice input and determines whether the user is anxious or excited.

[0963] 4. Obtaining information on legal reforms

[0964] The server accesses a database of legal amendments or an external API to retrieve the latest information on the laws and regulations specified in the request. For example, it calls an external API to retrieve data on amendments to the Labor Standards Act.

[0965] 5. Simplifying information with AI

[0966] The acquired legal amendment information is passed to a generative AI, which then processes the information into a concise summary. The AI ​​extracts the key points of the legal amendment and creates a short summary. For example, a summary such as "The upper limit on working hours has been changed to 45 hours per month" is generated.

[0967] 6. Propose solutions based on emotions

[0968] The server selects countermeasures for small and medium-sized enterprises based on the summarized legal amendment information and the user's emotional state recognized by the emotion engine. For example, if it recognizes that the user is feeling anxious, it selects content that strongly recommends a work hour management system.

[0969] 7. Individual customization

[0970] The server then generates a customized version of the selected solution based on the user's emotional state. It then generates information containing details of the solution and formats it for delivery to the user. For example, if the user's emotion is "anxiety," it also presents contact information for the support desk.

[0971] 8. Providing Results to Users

[0972] The server sends the generated summary information and countermeasures to the user's terminal, which receives this information and displays it to the user. Based on this information, the user can understand the specific countermeasures and proceed with the necessary procedures.

[0973] Specific examples

[0974] Responding to the revision of the Labor Standards Act

[0975] The user sends a request from their device saying, "I'd like to know about the revisions to the Labor Standards Act." The server receives this request, and its emotion engine recognizes that the user's emotion is "anxiety." The server retrieves the latest information about the revisions to the Labor Standards Act from an external API, and the generative AI summarizes it as "The upper limit on working hours has been changed to 45 hours per month." Based on this summary, the server takes into account the user's emotional state and makes a proposal strongly recommending the "introduction of a working hours management system," along with contact information for a support desk. Finally, the server sends this summary and a customized solution to the user's device, where the user can confirm the content on their device.

[0976] Through the above process, users (agency staff) can understand the details of legal changes and the appropriate countermeasures to them in a way that takes into account the user's emotional state, and can immediately provide appropriate advice to clients (small and medium-sized enterprises), thereby improving work efficiency and service.

[0977] The processing flow will be explained below.

[0978] Step 1:

[0979] A user inputs a request for information about a specific law or regulation into a terminal and presses the send button. For example, the user inputs a request such as "I want to know about amendments to the Labor Standards Act." The terminal then sends this request to the server.

[0980] Step 2:

[0981] The server receives a request sent from the terminal, the request data including the specific regulatory topic of interest to the user.

[0982] Step 3:

[0983] The server analyzes the received request data and extracts information about specific laws and regulations. In this case, it extracts information about amendments to the Labor Standards Act.

[0984] Step 4:

[0985] The server uses an emotion engine to analyze the user's emotional state, for example, by recognizing whether the user is feeling anxious based on the request or input text.

[0986] Step 5:

[0987] The server accesses an external database or external API to obtain the latest information on the identified laws and regulations. The server obtains amendment data for the Labor Standards Act from the external API.

[0988] Step 6:

[0989] The acquired legal and regulatory information is passed to a generative AI, which then summarises the information concisely. For example, it might generate a summary such as, "The upper limit on working hours has been changed to 45 hours per month."

[0990] Step 7:

[0991] The server selects countermeasures for small and medium-sized enterprises based on the summarized legal and regulatory information and the user's emotional state recognized by the emotion engine. For example, if the user feels anxious, it will strongly recommend the introduction of a working time management system.

[0992] Step 8:

[0993] The server then generates a customized version of the selected solution, including, for example, a detailed explanation of how to implement the time management system and contact information for support.

[0994] Step 9:

[0995] The server sends the generated summary information and the customized countermeasures to the user terminal, and the terminal displays the received information to the user.

[0996] Step 10:

[0997] The user can check the information provided on the device and understand specific countermeasures. For example, they can check how to implement a working time management system and contact support. Based on this information, the user can provide appropriate advice to the client.

[0998] Example 2

[0999] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1000] Conventional legal information provision systems have difficulty in quickly and accurately providing specific legal information that users desire. They also lack the ability to propose appropriate countermeasures based on the user's emotions and circumstances. Therefore, there is a need for efficient information provision and countermeasure proposals that take the user's emotions into consideration.

[1001] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1002] In this invention, the server includes means for receiving requests related to laws and regulations, means for analyzing the received requests and extracting information related to specific laws and regulations, means for analyzing user sentiment, means for acquiring the latest information related to laws and regulations from an external database or external API, means for summarizing the acquired legal and regulatory information using a generative AI that concisely summarizes the acquired legal and regulatory information, means for selecting and generating appropriate countermeasures based on the summarized legal and regulatory information and the user's sentiment, and means for providing the generated countermeasures to the user. This makes it possible to respond to user requests quickly and accurately and to propose appropriate countermeasures that take the user's sentiment into consideration.

[1003] A "regulatory request" is a request made by a user to the system for information about a specific regulation.

[1004] "Analysis" is a processing means for analyzing received data and understanding its contents.

[1005] "Emotion analysis" is a means of identifying and identifying a user's emotional state from their input and behavior.

[1006] An "external database" is a database that exists outside the system and stores data related to laws and regulations.

[1007] An "external API" is an application program interface for communicating with external services and obtaining data.

[1008] "Generative AI" is an AI that learns from large amounts of data and performs natural language processing. It is an AI module that is used, for example, to concisely summarize legal and regulatory information.

[1009] "Summarization" is the process of summarizing detailed information into a concise and easy-to-understand form.

[1010] "Appropriate responses" are specific guidelines or recommendations that the system provides based on the user's request or emotional state.

[1011] "Providing" means giving information or services to a user.

[1012] This system receives requests related to legal regulations, analyzes the requests to obtain information about specific legal regulations, summarizes them succinctly using generative AI, selects appropriate countermeasures, and provides them to the user. It also incorporates an emotion engine that recognizes the user's emotions.

[1013] Overall system overview

[1014] 1. Accepting user requests

[1015] The user inputs a request for information about a specific law and regulation into the terminal and presses the send button. For example, suppose the user inputs "I want to know about amendments to the Labor Standards Act" and sends it. The terminal then sends this request to the server.

[1016] 2. Receiving and parsing the request

[1017] The server receives a request sent from a terminal. The received request data includes a specific legal and regulatory topic. The server analyzes the request data and identifies the specific legal and regulatory topic. For example, it identifies a request regarding "amendments to the Labor Standards Act."

[1018] 3. Emotion analysis using an emotion engine

[1019] When the server receives a request, it uses an emotion engine to analyze the user's emotional state. For example, it reads emotions from the user's request text or voice input and determines whether the user is anxious or excited.

[1020] 4. Obtaining information on legal reforms

[1021] The server accesses a database of legal amendments or an external API to obtain the latest information on the laws and regulations specified in the request. For example, it calls an external API to obtain "Labor Standards Act amendment data."

[1022] 5. Simplifying information with AI

[1023] The server then passes the acquired legal change information to a generative AI, which then summarizes the information concisely. This generative AI uses an advanced natural language processing model such as GPT-4. For example, it generates a summary such as, "The upper limit on working hours has been changed to 45 hours per month."

[1024] 6. Propose solutions based on emotions

[1025] The server selects appropriate countermeasures based on the summarized legal amendment information and the user's emotional state recognized by the emotion engine. For example, if the user is feeling anxious, it will select a solution that strongly recommends a work time management system.

[1026] 7. Individual customization

[1027] The server then generates a customized response based on the user's emotional state. For example, it can add support contact information to the response to reassure the user.

[1028] 8. Providing Results to Users

[1029] The server sends the generated summary information and countermeasures to the user's terminal, which receives this information and displays it to the user. Based on this information, the user can understand the specific countermeasures and proceed with the necessary procedures.

[1030] Specific examples

[1031] The user sends a request from their device saying, "I'd like to know about the revisions to the Labor Standards Act." The server receives this request, and its emotion engine recognizes the user's emotion as "anxiety." The server retrieves the latest information on legal revisions from an external API, and the generative AI summarizes it as "The upper limit on working hours has been changed to 45 hours per month." Based on this summary, the server takes into account the user's emotional state and makes a proposal strongly recommending the "introduction of a working hours management system," along with contact information for a support desk. Finally, the server sends this summary and a customized solution to the user's device, where the user can view the content on their device.

[1032] In this way, users can understand the details of the legal changes and the appropriate measures to take in response, and receive support in taking concrete action.

[1033] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1034] Step 1:

[1035] The user inputs a request for information about a specific law and regulation into the terminal and presses the send button.

[1036] Input: The user enters the text "I want to know about the revision of the Labor Standards Act."

[1037] Action: The user enters text into the input field and clicks the "Submit" button.

[1038] Output: The terminal sends the input request text to the server.

[1039] Step 2:

[1040] The server receives the request sent from the terminal.

[1041] Input: Request data in the HTTP request sent from the terminal.

[1042] What it does: The server extracts text data from the body of the HTTP request.

[1043] Output: Extracted text data (e.g., "I would like to know about the revision of the Labor Standards Act").

[1044] Step 3:

[1045] The server analyzes the received request data to identify specific regulatory topics.

[1046] Input: Text data (e.g., "I would like to know about the revision of the Labor Standards Act").

[1047] How it works: The server uses natural language processing libraries to parse the request and extract specific regulatory topics.

[1048] Output: Extracted topics (e.g., "Amendments to the Labor Standards Act").

[1049] Step 4:

[1050] When the server receives a request, it uses an emotion engine to analyze the user's emotional state.

[1051] Input: Request text (e.g., "I would like to know about the revision of the Labor Standards Act").

[1052] Operation: The server calls the emotion engine API to perform emotion analysis.

[1053] Output: User's emotional state (e.g., anxiety).

[1054] Step 5:

[1055] The server accesses a legal change database or external API to retrieve the latest information about the laws and regulations identified in the request.

[1056] Input: Identified legal and regulatory topic (e.g., "Amendments to the Labor Standards Act").

[1057] How it works: The server sends a request to an external API to get the latest regulatory information.

[1058] Output: Retrieved legal change information data.

[1059] Step 6:

[1060] The server passes the legal amendment information it has acquired to a generative AI, which then summarizes it concisely.

[1061] Input: Obtained legal reform information data.

[1062] How it works: The server inputs data into a generative AI model and generates a summary result.

[1063] Output: Summarized legal change information (e.g., "The maximum working hours has been changed to 45 hours per month").

[1064] Step 7:

[1065] The server selects appropriate countermeasures based on the summarized legal amendment information and the user's emotional state recognized by the emotion engine.

[1066] Input: Abstracted legal change information and the user's emotional state.

[1067] Action: The server chooses a course of action based on the conditions.

[1068] Output: Selected solution (e.g., "Implementation of a working time management system").

[1069] Step 8:

[1070] The server then customizes the selected response individually based on the user's emotional state.

[1071] Input: The selected response and the user's emotional state.

[1072] Operation: The server customizes the solution by adding contact information for the support desk, etc.

[1073] Output: Customized action information (e.g. "Implementing a working time management system. Contact support here.").

[1074] Step 9:

[1075] The server transmits the generated summary information and countermeasures to the user terminal.

[1076] Input: Customized workaround information.

[1077] Operation: The server formats the countermeasure information in JSON format as an HTTP response and sends it to the device.

[1078] Output: Summary information and remedial actions sent to the user terminal.

[1079] Step 10:

[1080] The terminal displays the information received from the server to the user.

[1081] Input: Summary information and remedial actions received from the server.

[1082] Behavior: The user device parses the received JSON data and formats it as text for display in the user interface.

[1083] Output: On-screen summary information and suggested actions.

[1084] (Application example 2)

[1085] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1086] In situations where local legal regulations must be addressed, it is difficult for security staff to quickly obtain appropriate information. Furthermore, appropriate responses that take into account the local situation and the user's emotional state may not be provided, potentially resulting in a decline in the quality of responses. This can lead to issues such as delayed legal responses and an increased risk of incorrect actions.

[1087] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving a request related to laws and regulations; means for analyzing the received request and extracting information related to the specific laws and regulations; means for acquiring the latest information related to laws and regulations from an external database or an external API; means for summarizing the acquired laws and regulations using a generative AI that concisely summarizes the acquired laws and regulations; means for selecting and generating appropriate countermeasures based on the summarized laws and regulations information; emotion analysis engine means for analyzing the user's emotional state; means for customizing the summary information and countermeasures based on the emotional state; and means for providing the user with appropriate countermeasures and customized information. This enables security staff to quickly obtain appropriate laws and regulations information and countermeasures on site and take appropriate action according to the situation.

[1088] A "regulatory request" is a request sent by a user to a server for information about a particular regulation.

[1089] "Means for receiving" refers to a device or method for receiving a request sent from a user terminal.

[1090] "Means for parsing and extracting information relating to specific regulations" refers to a device or method that analyzes the received request and extracts information relating to specific regulations therefrom.

[1091] "External Database or External API" refers to an external database or application programming interface that provides regulatory information.

[1092] "Means for obtaining the latest information" refers to a device or method for collecting the latest information on laws and regulations from an external database or external API.

[1093] "Generative AI" refers to an artificial intelligence system that generates new information based on input information.

[1094] A "means for concisely summarizing" refers to a device or method for concisely summarizing acquired information to the main points.

[1095] "Means of summarizing" refers to a method of using generative AI to extract and summarize complex information in a concise form.

[1096] "Means for selecting and generating appropriate countermeasures" refers to a device or method for selecting and generating the most appropriate countermeasures based on the summarized regulatory information.

[1097] An "emotion analysis engine" refers to a system that analyzes a user's emotional state and outputs the results.

[1098] "Means for customizing summary information and responses based on emotional state" refers to a device or method that individually tailors summary information and selected responses based on the analysis results of an emotion analysis engine.

[1099] "Means for providing to the user" refers to a device or method for delivering the generated information and countermeasures to the user.

[1100] This invention is a system that receives requests related to laws and regulations, analyzes the requests to obtain information about specific laws and regulations, summarizes the information concisely using generative AI, selects appropriate countermeasures, and provides them to the user. This system is composed of a sentiment analysis engine that recognizes the user's emotions.

[1101] Overall system overview

[1102] 1. Accepting user requests

[1103] First, a user inputs a request for information about a specific law or regulation from the device. For example, the user might say through the smart glasses, "I want to know about the legal procedures when a suspicious person is found at the scene." This voice request is sent by the device to the server.

[1104] 2. Receiving and parsing the request

[1105] The server receives the request sent from the terminal. The received request data is the specific legal topic for which the user is seeking confirmation. The server analyzes the request data and identifies the specific legal topic being sought. In this case, it is identified as "Legal Procedures Regarding Suspicious Persons."

[1106] 3. Emotion analysis using an emotion engine

[1107] When the server receives a request, it uses an emotion analysis engine to analyze the user's emotional state. For example, it can read emotions from the user's request or voice input and determine whether the user is nervous.

[1108] 4. Obtaining legal and regulatory information

[1109] The server then accesses a database of laws and regulations or an external API to retrieve the latest information on the laws and regulations specified in the request. For example, it may call an external API to retrieve the latest data on the Peace Preservation Act or legal procedures for dealing with suspicious persons.

[1110] 5. Simplifying information with AI

[1111] The acquired legal and regulatory information is passed to a generative AI, which then processes it into a concise summary. For example, it might generate a summary such as, "When arresting a suspicious person, immediately contact the police and ensure safety at the scene."

[1112] 6. Propose solutions based on emotions

[1113] The server selects appropriate countermeasures based on the summarized legal information and the user's emotional state as determined by an emotion analysis engine. For example, if the server determines that the user is nervous, it selects content that emphasizes how to quickly contact the police or praises the first responders.

[1114] 7. Individual customization

[1115] The server generates a customized response based on the selected emotional state, and generates information including details of the response in a format that can be provided to the user.

[1116] 8. Providing Results to Users

[1117] The server sends the generated summary information and countermeasures to the user device (smart glasses). The user device receives this information and displays it to the user. Based on this information, the user can understand the specific countermeasures and proceed with the necessary procedures.

[1118] Hardware and software used

[1119] Hardware

[1120] Smart glasses: Wearable devices with voice recognition and display capabilities (e.g., Google Glass, Microsoft HoloLens)

[1121] software

[1122] Sentiment analysis engine: Python Sentiment Analysis library (e.g. textblob)

[1123] Generative AI: OpenAI's GPT-3 or GPT-4 API

[1124] Data acquisition: Realized using RESTful API (e.g., regulatory information API)

[1125] Prompt Sentence Examples

[1126] “Based on the summarized information about security regulations and considering that the user is anxious feeling, provide a suitable solution to handle unidentified persons in restricted areas.”

[1127] This system allows security staff to quickly obtain appropriate legal and regulatory information and countermeasures on-site, and to take the most appropriate action depending on the situation through sentiment analysis.

[1128] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1129] Step 1:

[1130] The user inputs a request for information about specific laws and regulations from the device. The request is generated by the user speaking into the smart glasses, such as "I would like to know about the legal procedures when a suspicious person is discovered at the scene." The input voice data is converted into text format, and the device sends the request to the server.

[1131] Step 2:

[1132] The server receives a request sent from the terminal. The received request data is text data containing a specific legal topic for which the user is seeking confirmation. The server analyzes this text data and identifies the specific legal topic being sought. In this case, "Legal procedures regarding suspicious persons" is extracted.

[1133] Step 3:

[1134] The server passes the received request to an emotion analysis engine, which analyzes the user's emotional state. Using the text data extracted from the voice request as input, the emotion analysis engine identifies the user's emotional state (e.g., nervousness, anxiety). The analysis result is that the user is "nervous."

[1135] Step 4:

[1136] The server accesses a regulatory database or external API to retrieve the latest information related to the identified regulatory topic. For example, to retrieve data on the Peace Preservation Act or legal procedures for dealing with suspicious persons, the server sends a request to the API endpoint and retrieves the required data.

[1137] Step 5:

[1138] The acquired information is passed to a generative AI, which then summarises it concisely. Using legal and regulatory data as input, the generative AI extracts key points and outputs a concise summary. For example, a generated summary might read, "When arresting a suspicious individual, contact the police immediately and ensure safety at the scene."

[1139] Step 6:

[1140] The server selects appropriate countermeasures based on the generated summary information and the user's emotional state recognized by the emotion analysis engine. Countermeasures are generated using the analysis results and summary data as input, such as "If the user is nervous, emphasize how to quickly contact the police or praise the first responders."

[1141] Step 7:

[1142] The server then generates and customizes the selected countermeasures based on the user's emotional state. Using the countermeasures and emotional data as input, the server customizes the countermeasures based on the user's emotional state, and generates specific details of the countermeasures. For example, the server adds details to the countermeasures, such as "procedures for contacting the public security department" and "the importance of initial response."

[1143] Step 8:

[1144] The server sends the customized summary information and countermeasures to the user's device. The device receives the generated data as input and displays the summary of legal proceedings and countermeasures on the smart glasses display. The user can review this information and take appropriate action based on it.

[1145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1146] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1147] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1148] [Fourth embodiment]

[1149] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1150] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1153] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1156] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1157] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1158] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1160] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1161] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1162] This invention is a system that receives requests related to legal regulations, analyzes the requests to obtain information about specific legal regulations, summarizes the information concisely using generative AI, selects appropriate countermeasures, and provides them to the user. Here, we will explain the specific program processing in natural language.

[1163] Overall system overview

[1164] 1. Accepting user requests

[1165] A user sends a request for information about a specific law or regulation from a terminal. For example, suppose the user types and submits "I want to know about amendments to the Labor Standards Act." The terminal then sends this request to the server.

[1166] 2. Receiving and parsing the request

[1167] The server receives the request from the device and analyzes the content to identify information about the specific law and regulation. This analysis determines the specific legal reform topic the user is seeking (in this case, amendments to the Labor Standards Act).

[1168] 3. Obtaining information on legal reforms

[1169] The server accesses a database of legal amendments or an external API to retrieve the latest information about the laws and regulations specified in the request, for example, the latest provisions and explanations regarding amendments to the Labor Standards Act.

[1170] 4. Simplifying information with AI

[1171] The acquired legal amendment information is passed to a generative AI, which then summarises the information concisely. For example, it might generate a summary such as "The upper limit on working hours has been changed to 45 hours per month."

[1172] 5. Solution Proposal

[1173] The server selects countermeasures for small and medium-sized enterprises based on the summarized information on legal amendments, for example, generating recommendations for the introduction of a working time management system.

[1174] 6. Providing Results to Users

[1175] The server sends the generated summary information and countermeasures to the user's terminal, where the user can view the information.

[1176] Specific examples

[1177] Responding to the revision of the Labor Standards Act

[1178] The user sends a request from their device saying, "I want to know about amendments to the Labor Standards Act." The server receives this request and retrieves the latest information on amendments to the Labor Standards Act from an external API. The generative AI summarizes the information it retrieves as "The upper limit on working hours has been changed to 45 hours per month," and based on this, the server proposes "the introduction of a working hours management system" as a solution. Finally, the server sends this summary information and solution to the user's device, and the user can confirm the details on their device.

[1179] Through the above process, users (agency staff) can easily understand the details of legal amendments and immediately provide appropriate countermeasures to their clients (small and medium-sized enterprises), thereby improving business efficiency and service.

[1180] The processing flow will be explained below.

[1181] Step 1:

[1182] The user inputs a request for information on legal regulations into the terminal and presses the send button, which then sends the request to the server.

[1183] Step 2:

[1184] The server receives the request sent from the terminal, and the received request data is a specific regulatory topic for which the user wishes to confirm.

[1185] Step 3:

[1186] The server parses the request data to identify the specific regulatory topic being sought, specifically extracting regulatory categories and specific amendments from the request data.

[1187] Step 4:

[1188] The server accesses an external database or external API to retrieve the latest information on the identified legal and regulatory topic. For example, an external API is called to retrieve revision data for the Labor Standards Act.

[1189] Step 5:

[1190] The server passes the acquired legal information to a generative AI, which then summarizes the information concisely. The AI ​​extracts the key points of the legal regulations and provides a short summary.

[1191] Step 6:

[1192] The server selects countermeasures for small and medium-sized enterprises based on the summarized legal and regulatory information. For example, if there is a change in the upper limit of working hours, the server will suggest the introduction of a working hour management system.

[1193] Step 7:

[1194] The server generates information about the selected countermeasure, including details of the countermeasure, and formats the information in a format that can be provided to the user.

[1195] Step 8:

[1196] The server sends the generated summary information and countermeasures to the user terminal, which receives the information and displays it to the user.

[1197] Step 9:

[1198] The user checks the information provided on the device and understands the specific countermeasures. Based on this information, the user provides appropriate advice to the client.

[1199] Example 1

[1200] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1201] Currently, many companies and individuals need to respond quickly to changes in laws and regulations, but it is difficult to understand the necessary content from the vast amount of information and determine appropriate countermeasures. Since it is particularly difficult for small and medium-sized enterprises and individuals to collect and apply information about changes in laws and regulations, they are required to obtain information efficiently and accurately and provide appropriate countermeasures based on that information.

[1202] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1203] In this invention, the server includes means for receiving requests related to laws and regulations, means for analyzing the received requests and extracting information related to specific laws and regulations, means for obtaining the latest information related to laws and regulations from an external database or external API, means for summarizing the obtained legal and regulatory information using a generative AI that concisely summarizes the obtained legal and regulatory information, means for selecting and generating appropriate countermeasures based on the summarized legal and regulatory information, means for providing the generated countermeasures to a user, means for analyzing data obtained from external information sources using natural language processing technology, and means for instructing the generative AI model to summarize the legal and regulatory information using prompt sentences. This enables users to quickly and efficiently respond to changes in laws and regulations and obtain accurate information and appropriate countermeasures.

[1204] "Legal regulations" refer to rules and laws enacted by government agencies such as countries and regions, which companies and individuals must comply with.

[1205] A "request" is a request that a user sends to a system for particular information or service.

[1206] "Receiving means" refers to the function or device that allows the server to receive requests sent from the user.

[1207] "Analysis means" refers to the processing method or software used to understand the content of the received request and extract the necessary information.

[1208] An "external database" is an external information aggregation system that stores information related to laws and regulations.

[1209] An "external API" is an interface for data exchange with other systems and services.

[1210] "Generative AI" refers to an artificial intelligence model that automatically generates sentences based on given data and questions.

[1211] "Summarization means" refers to a processing method or software for concisely summarizing acquired information.

[1212] A "solution" is a specific measure or action that should be taken in response to a particular problem or situation.

[1213] "Natural language processing technology" refers to technology that allows computers to understand, interpret, and generate human language.

[1214] A "prompt" is an instruction given to a generative AI when it is put into operation, and serves as a guideline for the AI ​​to generate the necessary information.

[1215] The present invention is a system that receives requests related to laws and regulations, analyzes the requests to obtain information about specific laws and regulations, summarizes the information concisely using generative AI, selects appropriate countermeasures, and provides them to users. The following describes how to specifically implement the present invention.

[1216] Overall overview

[1217] 1. Accepting user requests

[1218] A user uses their device to input and send a request for information about a specific law or regulation. For example, the user might write, "I want to know about amendments to the Labor Standards Act," and click the send button. The device then sends this request to the server.

[1219] 2. Receiving and parsing the request

[1220] The server receives a request sent from the user's device. The server analyzes the request and identifies the specific legal and regulatory topic the user is looking for. This analysis is performed using natural language processing (NLP) technology. As a result of the analysis, for example, "Amendments to the Labor Standards Act" may be identified as a topic.

[1221] 3. Obtaining legal and regulatory information

[1222] The server accesses external databases and APIs to obtain the latest information on specified laws and regulations. Specifically, it uses external information sources such as the "Government Gazette API" and "Legal Data Provision Service." The server sends an API request, analyzes the received data, and obtains the latest provisions and explanations.

[1223] 4. AI-powered information summarization

[1224] The server passes the acquired legal and regulatory information to a generative AI model (e.g., "OpenAI GPT-4"). The generative AI model then succinctly summarizes the information provided. This summarization process uses a pre-set prompt (e.g., "Please briefly summarize the latest amendments to the Labor Standards Act").

[1225] 5. Solution Proposal

[1226] The server selects countermeasures based on specific conditions (for example, for small and medium-sized enterprises) based on the information summarized by the generative AI model. In doing so, the server refers to a list of proposal candidates from an internal database and recommends, for example, the introduction of a working hours management system.

[1227] 6. Providing results to users

[1228] The server sends the final summary information and countermeasures to the user's device. The user can check this information on their own device. For example, a message such as "The upper limit on working hours has been changed to 45 hours per month. As a countermeasure, we recommend introducing a working hours management system" will be displayed on the user's device screen.

[1229] Specific examples

[1230] The user sends a request from their device saying, "I would like to know about amendments to the Labor Standards Act." The server receives this request and uses NLP technology to identify the topic "Amendments to the Labor Standards Act." The server accesses the "Government Gazette API" to obtain data on this year's amendments to the Labor Standards Act. The obtained data is passed to a generative AI model (OpenAI GPT-4) and the prompt is used: "Please briefly summarize the latest amendments to the Labor Standards Act." The generative AI model generates a summary: "The upper limit on working hours has been changed to 45 hours per month." The server selects a solution from its internal database that recommends "introducing a working hour management system," and sends the final summary information and solution to the user's device. The user checks the results on their device.

[1231] In this way, the system of the present invention allows users to quickly and efficiently respond to changes in laws and regulations and obtain accurate information and appropriate countermeasures.

[1232] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1233] Step 1:

[1234] A user uses their device to input and send a request for information about a specific law or regulation. Specifically, the user writes "I would like to know about amendments to the Labor Standards Act" and clicks the send button. This request is sent from the device to the server as an HTTP POST request.

[1235] Input: The regulatory request entered by the user into the device

[1236] Output: HTTP POST request sent to the server

[1237] Step 2:

[1238] The server receives the request sent from the user terminal. The server analyzes the content of the received request using natural language processing (NLP) technology to identify specific legal and regulatory topics. For example, this analysis extracts the topic "Amendments to the Labor Standards Act."

[1239] Input: HTTP POST request received by the server

[1240] Output: Parsed topic (e.g. "Amendment to the Labor Standards Act")

[1241] Step 3:

[1242] The server accesses external databases and APIs to obtain the latest information on specified laws and regulations. Specifically, the server sends API requests to external information sources such as "Government Gazette API" and "Legal Data Service" to obtain relevant data. This obtained data includes the latest provisions and explanations.

[1243] Input: Parsed topic, external database or API endpoint

[1244] Output: The latest legal information (e.g., latest provisions and explanations)

[1245] Step 4:

[1246] The server passes the acquired legal and regulatory information to a generative AI model (e.g., OpenAI GPT-4). Here, the server sets a prompt and instructs the generative AI model to provide a concise summary. The prompt is set to something like, "Please briefly summarize the latest amendments to the Labor Standards Act." The generative AI model generates a summary based on this prompt.

[1247] Input: Obtained legal information, prompt text

[1248] Output: The generated summary (e.g. "The working hour limit has been changed to 45 hours per month")

[1249] Step 5:

[1250] Based on the information summarized by the generative AI model, the server selects countermeasures according to specific conditions (for example, for small and medium-sized enterprises). The server refers to a list of proposal candidates from an internal database and selects the optimal countermeasure (for example, "introduction of a working hour management system").

[1251] Input: Generated summary, list of suggestion candidates from internal database

[1252] Output: Selected solution (e.g., "Introduce a working time management system")

[1253] Step 6:

[1254] The server sends the final summary information and countermeasures to the user's terminal. The user can check this information on their own terminal. For example, a message such as "The upper limit of working hours has been changed to 45 hours per month. As a countermeasure, we recommend introducing a working hours management system" will be displayed on the terminal screen.

[1255] Input: Final summary information, action plan

[1256] Output: Message sent to user terminal (summary information and action plan)

[1257] (Application example 1)

[1258] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1259] The food delivery industry needs to respond quickly to changes in food safety standards and hygiene control laws, but the current system tends to be slow in obtaining regulatory information and proposing countermeasures. As a result, it is difficult for businesses to comply with the latest regulations, leading to problems such as reduced business efficiency and compliance.

[1260] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1261] In this invention, the server includes means for receiving requests related to laws and regulations, means for analyzing the received requests and extracting information related to specific laws and regulations, means for obtaining the latest information related to laws and regulations from an external database or external API, means for summarizing the obtained information on laws and regulations using a generative AI that concisely summarizes the information, means for selecting and generating appropriate countermeasures based on the summarized information on laws and regulations, means for transmitting the summarized information on laws and regulations and the countermeasures to a user terminal, means for proposing countermeasures for food delivery companies in accordance with new laws and regulations, means for reflecting the proposed countermeasures in the business system of the food delivery company, and means for providing an appropriate prompt sentence to the generative AI model. This enables food delivery companies to quickly obtain the latest information on laws and regulations and reflect the countermeasures based on the information in their business systems.

[1262] "Legal regulations" is a general term for laws, regulations, guidelines, etc. established by governments and regulatory bodies, and are rules that industries and companies must follow.

[1263] A "request" refers to an instruction or inquiry that a user sends to a server seeking some information or service.

[1264] "Analysis" refers to the process of analyzing the content of a received request and identifying necessary information.

[1265] "Information" refers to data and knowledge about legal regulations, the latest provisions and amendments, etc.

[1266] An "external database" is a large-scale data repository located outside the server, and is an information source accessible via the Internet.

[1267] An "external API" is an application program interface that provides a means to connect to external services and databases.

[1268] "Generative AI" is a type of artificial intelligence trained to perform specific tasks, specifically models capable of generating new information from data.

[1269] "Summarization" refers to the compilation of acquired regulatory information into a more concise format.

[1270] "Countermeasures" indicate specific actions or measures that businesses and users should take based on the summarized legal and regulatory information.

[1271] "User terminal" refers to a device used by a user, such as a smartphone, PC, or tablet, and is a medium for communicating with the server.

[1272] A "prompt" is an input sentence that instructs a generative AI to perform a specific task, and serves as an instruction for the AI ​​to generate an appropriate result.

[1273] This invention provides a system for the food delivery industry to quickly respond to new food safety standards and amendments to the Sanitation Control Act. This system performs all processes from obtaining legal information to proposing countermeasures, and is implemented by combining a server, user terminals, and generative AI models.

[1274] Overall system configuration

[1275] The system includes the following major components:

[1276] 1. User Device

[1277] A device through which a user can request new regulatory information, such as a smartphone, tablet, or computer.

[1278] 2. Server

[1279] Its role is to receive requests, analyze them, obtain information from external databases and APIs, summarize using generative AI, select and propose countermeasures, and send the results to the user's device.

[1280] 3. Generative AI Models

[1281] For example, language models such as OpenAI's GPT-4 are used to summarize legal information and generate prompts.

[1282] Program processing

[1283] The server performs the following steps:

[1284] 1. Receiving and parsing the request

[1285] A request such as "I would like to obtain the latest food safety standards" is received from the user terminal. The received request is analyzed on the server side to identify the required information.

[1286] 2. Obtaining legal and regulatory information

[1287] The server accesses an external database or API (e.g., a government regulatory database) to obtain the latest regulatory information.

[1288] 3. Summary of information

[1289] The acquired legal and regulatory information is passed to a generative AI model (e.g., GPT-4) to generate a concise summary. An example prompt is, "How can I comply with the latest food safety standards?"

[1290] 4. Selection and proposal of countermeasures

[1291] Based on the summarized information, the server selects specific countermeasures for food delivery companies, such as "Since all fresh vegetables are required to be washed before shipping, we recommend introducing fresh vegetable washing machines."

[1292] 5. Sending the results to the user device

[1293] The final summary information and countermeasures are sent to the user terminal so that the user can check them.

[1294] Specific examples

[1295] For example, if a food delivery company wants to know about new food safety standards, they might enter the following prompt:

[1296] "How can I comply with the latest food safety standards?"

[1297] The server analyzes this request and retrieves the latest information from an external database. Using a generative AI model, it generates a summary such as, "New food safety standards require all raw vegetables to be washed before shipping," and suggests a countermeasure such as, "We recommend installing a raw vegetable washing machine." This allows users to quickly and concisely obtain the latest regulatory information and take the necessary countermeasures.

[1298] As a result, the present invention enables the food delivery industry to quickly and efficiently comply with new regulations.

[1299] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1300] Step 1:

[1301] A user terminal generates and transmits a regulatory request.

[1302] Input: The user inputs a request into the terminal saying, "I would like to obtain the latest food safety standards."

[1303] Output: This request is sent from the user terminal to the server.

[1304] Specific operation: A user fills out a request form on a device such as a smartphone or tablet and presses the send button. This request is sent to the server as an HTTP request.

[1305] Step 2:

[1306] The server analyzes the received request and extracts specific regulatory information.

[1307] Input: A request sent from the user's device to "get the latest food safety standards."

[1308] Output: Extract the keyword "food safety standards" as the parsed regulatory information request.

[1309] What it does: The server uses a natural language processing (NLP) engine to analyze the request and extract key elements of the request (in this case, "food safety standards").

[1310] Step 3:

[1311] The server accesses an external database or API to retrieve the latest regulatory information.

[1312] Input: Request for "Food Safety Standards."

[1313] Output: The latest information on the acquired "Food Safety Standards."

[1314] How it works: The server sends an HTTP request to an external API (for example, a government regulatory database API) to obtain the latest data on "food safety standards." The obtained data is returned to the server in JSON format.

[1315] Step 4:

[1316] The server passes the acquired legal and regulatory information to a generative AI model for summarization.

[1317] Input: Latest information on "Food Safety Standards" (JSON data).

[1318] Output: A summary result from the generative AI model (e.g., "New food safety standards require all raw vegetables to be washed before shipping.").

[1319] How it works: The server converts the acquired information into text format and inputs it to a generative AI model (e.g., GPT-4) using a prompt sentence, such as "How can I comply with the latest food safety standards?". The AI ​​model generates a concise summary based on this prompt.

[1320] Step 5:

[1321] The server selects and generates appropriate countermeasures based on the summarized information.

[1322] Input: Summary result (e.g., "New food safety standards require all fresh vegetables to be washed before shipping.").

[1323] Output: The selected countermeasure (e.g., "We recommend installing a fresh vegetable washer").

[1324] Specific operation: The server checks the summary results and runs an algorithm to automatically select specific countermeasures for the contractor. It selects the optimal solution based on past countermeasures in the database and the contractor's request information.

[1325] Step 6:

[1326] The server transmits the generated summary information and countermeasures to the user terminal.

[1327] Input: Summary information and action plan (e.g., "New food safety standards require all fresh vegetables to be washed before shipping" and "We recommend installing fresh vegetable washers").

[1328] Output: The final result displayed on the user's terminal.

[1329] Specific operation: The server returns the generated summary information and countermeasures to the user device as an HTTP response. The user device receives this information and displays it on the screen. The user can then take the necessary measures based on this information.

[1330] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1331] This invention is a system that receives requests related to legal regulations, analyzes the requests to obtain information about specific legal regulations, summarizes them succinctly using generative AI, selects appropriate countermeasures, and provides them to the user, and further combines this with an emotion engine that recognizes the user's emotions. Here, we will explain the specific program processing in natural language.

[1332] Overall system overview

[1333] 1. Accepting user requests

[1334] A user inputs a request for information about a specific law and regulation from a terminal and presses the send button. For example, suppose a user inputs and sends "I want to know about amendments to the Labor Standards Act." The terminal then sends this request to the server.

[1335] 2. Receiving and parsing the request

[1336] The server receives a request sent from the terminal. The received request data is a specific legal topic for which the user is seeking confirmation. The server analyzes the request data and identifies the specific legal topic being sought. For example, the server identifies information about amendments to the Labor Standards Act from the user's request.

[1337] 3. Emotion analysis using an emotion engine

[1338] When the server receives a request, it uses an emotion engine to analyze the user's emotional state. For example, it reads emotions from the user's request text or voice input and determines whether the user is anxious or excited.

[1339] 4. Obtaining information on legal reforms

[1340] The server accesses a database of legal amendments or an external API to retrieve the latest information on the laws and regulations specified in the request. For example, it calls an external API to retrieve data on amendments to the Labor Standards Act.

[1341] 5. Simplifying information with AI

[1342] The acquired legal amendment information is passed to a generative AI, which then processes the information into a concise summary. The AI ​​extracts the key points of the legal amendment and creates a short summary. For example, a summary such as "The upper limit on working hours has been changed to 45 hours per month" is generated.

[1343] 6. Propose solutions based on emotions

[1344] The server selects countermeasures for small and medium-sized enterprises based on the summarized legal amendment information and the user's emotional state recognized by the emotion engine. For example, if it recognizes that the user is feeling anxious, it selects content that strongly recommends a work hour management system.

[1345] 7. Individual customization

[1346] The server then generates a customized version of the selected solution based on the user's emotional state. It then generates information containing details of the solution and formats it for delivery to the user. For example, if the user's emotion is "anxiety," it also presents contact information for the support desk.

[1347] 8. Providing Results to Users

[1348] The server sends the generated summary information and countermeasures to the user's terminal, which receives this information and displays it to the user. Based on this information, the user can understand the specific countermeasures and proceed with the necessary procedures.

[1349] Specific examples

[1350] Responding to the revision of the Labor Standards Act

[1351] The user sends a request from their device saying, "I'd like to know about the revisions to the Labor Standards Act." The server receives this request, and its emotion engine recognizes that the user's emotion is "anxiety." The server retrieves the latest information about the revisions to the Labor Standards Act from an external API, and the generative AI summarizes it as "The upper limit on working hours has been changed to 45 hours per month." Based on this summary, the server takes into account the user's emotional state and makes a proposal strongly recommending the "introduction of a working hours management system," along with contact information for a support desk. Finally, the server sends this summary and a customized solution to the user's device, where the user can confirm the content on their device.

[1352] Through the above process, users (agency staff) can understand the details of legal changes and the appropriate countermeasures to them in a way that takes into account the user's emotional state, and can immediately provide appropriate advice to clients (small and medium-sized enterprises), thereby improving work efficiency and service.

[1353] The processing flow will be explained below.

[1354] Step 1:

[1355] A user inputs a request for information about a specific law or regulation into a terminal and presses the send button. For example, the user inputs a request such as "I want to know about amendments to the Labor Standards Act." The terminal then sends this request to the server.

[1356] Step 2:

[1357] The server receives a request sent from the terminal, the request data including the specific regulatory topic of interest to the user.

[1358] Step 3:

[1359] The server analyzes the received request data and extracts information about specific laws and regulations. In this case, it extracts information about amendments to the Labor Standards Act.

[1360] Step 4:

[1361] The server uses an emotion engine to analyze the user's emotional state, for example, by recognizing whether the user is feeling anxious based on the request or input text.

[1362] Step 5:

[1363] The server accesses an external database or external API to obtain the latest information on the identified laws and regulations. The server obtains amendment data for the Labor Standards Act from the external API.

[1364] Step 6:

[1365] The acquired legal and regulatory information is passed to a generative AI, which then summarises the information concisely. For example, it might generate a summary such as, "The upper limit on working hours has been changed to 45 hours per month."

[1366] Step 7:

[1367] The server selects countermeasures for small and medium-sized enterprises based on the summarized legal and regulatory information and the user's emotional state recognized by the emotion engine. For example, if the user feels anxious, it will strongly recommend the introduction of a working time management system.

[1368] Step 8:

[1369] The server then generates a customized version of the selected solution, including, for example, a detailed explanation of how to implement the time management system and contact information for support.

[1370] Step 9:

[1371] The server sends the generated summary information and the customized countermeasures to the user terminal, and the terminal displays the received information to the user.

[1372] Step 10:

[1373] The user can check the information provided on the device and understand specific countermeasures. For example, they can check how to implement a working time management system and contact support. Based on this information, the user can provide appropriate advice to the client.

[1374] Example 2

[1375] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1376] Conventional legal information provision systems have difficulty in quickly and accurately providing specific legal information that users desire. They also lack the ability to propose appropriate countermeasures based on the user's emotions and circumstances. Therefore, there is a need for efficient information provision and countermeasure proposals that take the user's emotions into consideration.

[1377] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1378] In this invention, the server includes means for receiving requests related to laws and regulations, means for analyzing the received requests and extracting information related to specific laws and regulations, means for analyzing user sentiment, means for acquiring the latest information related to laws and regulations from an external database or external API, means for summarizing the acquired legal and regulatory information using a generative AI that concisely summarizes the acquired legal and regulatory information, means for selecting and generating appropriate countermeasures based on the summarized legal and regulatory information and the user's sentiment, and means for providing the generated countermeasures to the user. This makes it possible to respond to user requests quickly and accurately and to propose appropriate countermeasures that take the user's sentiment into consideration.

[1379] A "regulatory request" is a request made by a user to the system for information about a specific regulation.

[1380] "Analysis" is a processing means for analyzing received data and understanding its contents.

[1381] "Emotion analysis" is a means of identifying and identifying a user's emotional state from their input and behavior.

[1382] An "external database" is a database that exists outside the system and stores data related to laws and regulations.

[1383] An "external API" is an application program interface for communicating with external services and obtaining data.

[1384] "Generative AI" is an AI that learns from large amounts of data and performs natural language processing. It is an AI module that is used, for example, to concisely summarize legal and regulatory information.

[1385] "Summarization" is the process of summarizing detailed information into a concise and easy-to-understand form.

[1386] "Appropriate responses" are specific guidelines or recommendations that the system provides based on the user's request or emotional state.

[1387] "Providing" means giving information or services to a user.

[1388] This system receives requests related to legal regulations, analyzes the requests to obtain information about specific legal regulations, summarizes them succinctly using generative AI, selects appropriate countermeasures, and provides them to the user. It also incorporates an emotion engine that recognizes the user's emotions.

[1389] Overall system overview

[1390] 1. Accepting user requests

[1391] The user inputs a request for information about a specific law and regulation into the terminal and presses the send button. For example, suppose the user inputs "I want to know about amendments to the Labor Standards Act" and sends it. The terminal then sends this request to the server.

[1392] 2. Receiving and parsing the request

[1393] The server receives a request sent from a terminal. The received request data includes a specific legal and regulatory topic. The server analyzes the request data and identifies the specific legal and regulatory topic. For example, it identifies a request regarding "amendments to the Labor Standards Act."

[1394] 3. Emotion analysis using an emotion engine

[1395] When the server receives a request, it uses an emotion engine to analyze the user's emotional state. For example, it reads emotions from the user's request text or voice input and determines whether the user is anxious or excited.

[1396] 4. Obtaining information on legal reforms

[1397] The server accesses a database of legal amendments or an external API to obtain the latest information on the laws and regulations specified in the request. For example, it calls an external API to obtain "Labor Standards Act amendment data."

[1398] 5. Simplifying information with AI

[1399] The server then passes the acquired legal change information to a generative AI, which then summarizes the information concisely. This generative AI uses an advanced natural language processing model such as GPT-4. For example, it generates a summary such as, "The upper limit on working hours has been changed to 45 hours per month."

[1400] 6. Propose solutions based on emotions

[1401] The server selects appropriate countermeasures based on the summarized legal amendment information and the user's emotional state recognized by the emotion engine. For example, if the user is feeling anxious, it will select a solution that strongly recommends a work time management system.

[1402] 7. Individual customization

[1403] The server then generates a customized response based on the user's emotional state. For example, it can add support contact information to the response to reassure the user.

[1404] 8. Providing Results to Users

[1405] The server sends the generated summary information and countermeasures to the user's terminal, which receives this information and displays it to the user. Based on this information, the user can understand the specific countermeasures and proceed with the necessary procedures.

[1406] Specific examples

[1407] The user sends a request from their device saying, "I'd like to know about the revisions to the Labor Standards Act." The server receives this request, and its emotion engine recognizes the user's emotion as "anxiety." The server retrieves the latest information on legal revisions from an external API, and the generative AI summarizes it as "The upper limit on working hours has been changed to 45 hours per month." Based on this summary, the server takes into account the user's emotional state and makes a proposal strongly recommending the "introduction of a working hours management system," along with contact information for a support desk. Finally, the server sends this summary and a customized solution to the user's device, where the user can view the content on their device.

[1408] In this way, users can understand the details of the legal changes and the appropriate measures to take in response, and receive support in taking concrete action.

[1409] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1410] Step 1:

[1411] The user inputs a request for information about a specific law and regulation into the terminal and presses the send button.

[1412] Input: The user enters the text "I want to know about the revision of the Labor Standards Act."

[1413] Action: The user enters text into the input field and clicks the "Submit" button.

[1414] Output: The terminal sends the input request text to the server.

[1415] Step 2:

[1416] The server receives the request sent from the terminal.

[1417] Input: Request data in the HTTP request sent from the terminal.

[1418] What it does: The server extracts text data from the body of the HTTP request.

[1419] Output: Extracted text data (e.g., "I would like to know about the revision of the Labor Standards Act").

[1420] Step 3:

[1421] The server analyzes the received request data to identify specific regulatory topics.

[1422] Input: Text data (e.g., "I would like to know about the revision of the Labor Standards Act").

[1423] How it works: The server uses natural language processing libraries to parse the request and extract specific regulatory topics.

[1424] Output: Extracted topics (e.g., "Amendments to the Labor Standards Act").

[1425] Step 4:

[1426] When the server receives a request, it uses an emotion engine to analyze the user's emotional state.

[1427] Input: Request text (e.g., "I would like to know about the revision of the Labor Standards Act").

[1428] Operation: The server calls the emotion engine API to perform emotion analysis.

[1429] Output: User's emotional state (e.g., anxiety).

[1430] Step 5:

[1431] The server accesses a legal change database or external API to retrieve the latest information about the laws and regulations identified in the request.

[1432] Input: Identified legal and regulatory topic (e.g., "Amendments to the Labor Standards Act").

[1433] How it works: The server sends a request to an external API to get the latest regulatory information.

[1434] Output: Retrieved legal change information data.

[1435] Step 6:

[1436] The server passes the legal amendment information it has acquired to a generative AI, which then summarizes it concisely.

[1437] Input: Obtained legal reform information data.

[1438] How it works: The server inputs data into a generative AI model and generates a summary result.

[1439] Output: Summarized legal change information (e.g., "The maximum working hours has been changed to 45 hours per month").

[1440] Step 7:

[1441] The server selects appropriate countermeasures based on the summarized legal amendment information and the user's emotional state recognized by the emotion engine.

[1442] Input: Abstracted legal change information and the user's emotional state.

[1443] Action: The server chooses a course of action based on the conditions.

[1444] Output: Selected solution (e.g., "Implementation of a working time management system").

[1445] Step 8:

[1446] The server then customizes the selected response individually based on the user's emotional state.

[1447] Input: The selected response and the user's emotional state.

[1448] Operation: The server customizes the solution by adding contact information for the support desk, etc.

[1449] Output: Customized action information (e.g. "Implementing a working time management system. Contact support here.").

[1450] Step 9:

[1451] The server transmits the generated summary information and countermeasures to the user terminal.

[1452] Input: Customized workaround information.

[1453] Operation: The server formats the countermeasure information in JSON format as an HTTP response and sends it to the device.

[1454] Output: Summary information and remedial actions sent to the user terminal.

[1455] Step 10:

[1456] The terminal displays the information received from the server to the user.

[1457] Input: Summary information and remedial actions received from the server.

[1458] Behavior: The user device parses the received JSON data and formats it as text for display in the user interface.

[1459] Output: On-screen summary information and suggested actions.

[1460] (Application example 2)

[1461] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1462] In situations where local legal regulations must be addressed, it is difficult for security staff to quickly obtain appropriate information. Furthermore, appropriate responses that take into account the local situation and the user's emotional state may not be provided, potentially resulting in a decline in the quality of responses. This can lead to issues such as delayed legal responses and an increased risk of incorrect actions.

[1463] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving a request related to laws and regulations; means for analyzing the received request and extracting information related to the specific laws and regulations; means for acquiring the latest information related to laws and regulations from an external database or an external API; means for summarizing the acquired laws and regulations using a generative AI that concisely summarizes the acquired laws and regulations; means for selecting and generating appropriate countermeasures based on the summarized laws and regulations information; emotion analysis engine means for analyzing the user's emotional state; means for customizing the summary information and countermeasures based on the emotional state; and means for providing the user with appropriate countermeasures and customized information. This enables security staff to quickly obtain appropriate laws and regulations information and countermeasures on site and take appropriate action according to the situation.

[1464] A "regulatory request" is a request sent by a user to a server for information about a particular regulation.

[1465] "Means for receiving" refers to a device or method for receiving a request sent from a user terminal.

[1466] "Means for parsing and extracting information relating to specific regulations" refers to a device or method that analyzes the received request and extracts information relating to specific regulations therefrom.

[1467] "External Database or External API" refers to an external database or application programming interface that provides regulatory information.

[1468] "Means for obtaining the latest information" refers to a device or method for collecting the latest information on laws and regulations from an external database or external API.

[1469] "Generative AI" refers to an artificial intelligence system that generates new information based on input information.

[1470] A "means for concisely summarizing" refers to a device or method for concisely summarizing acquired information to the main points.

[1471] "Means of summarizing" refers to a method of using generative AI to extract and summarize complex information in a concise form.

[1472] "Means for selecting and generating appropriate countermeasures" refers to a device or method for selecting and generating the most appropriate countermeasures based on the summarized regulatory information.

[1473] An "emotion analysis engine" refers to a system that analyzes a user's emotional state and outputs the results.

[1474] "Means for customizing summary information and responses based on emotional state" refers to a device or method that individually tailors summary information and selected responses based on the analysis results of an emotion analysis engine.

[1475] "Means for providing to the user" refers to a device or method for delivering the generated information and countermeasures to the user.

[1476] This invention is a system that receives requests related to laws and regulations, analyzes the requests to obtain information about specific laws and regulations, summarizes the information concisely using generative AI, selects appropriate countermeasures, and provides them to the user. This system is composed of a sentiment analysis engine that recognizes the user's emotions.

[1477] Overall system overview

[1478] 1. Accepting user requests

[1479] First, a user inputs a request for information about a specific law or regulation from the device. For example, the user might say through the smart glasses, "I want to know about the legal procedures when a suspicious person is found at the scene." This voice request is sent by the device to the server.

[1480] 2. Receiving and parsing the request

[1481] The server receives the request sent from the terminal. The received request data is the specific legal topic for which the user is seeking confirmation. The server analyzes the request data and identifies the specific legal topic being sought. In this case, it is identified as "Legal Procedures Regarding Suspicious Persons."

[1482] 3. Emotion analysis using an emotion engine

[1483] When the server receives a request, it uses an emotion analysis engine to analyze the user's emotional state. For example, it can read emotions from the user's request or voice input and determine whether the user is nervous.

[1484] 4. Obtaining legal and regulatory information

[1485] The server then accesses a database of laws and regulations or an external API to retrieve the latest information on the laws and regulations specified in the request. For example, it may call an external API to retrieve the latest data on the Peace Preservation Act or legal procedures for dealing with suspicious persons.

[1486] 5. Simplifying information with AI

[1487] The acquired legal and regulatory information is passed to a generative AI, which then processes it into a concise summary. For example, it might generate a summary such as, "When arresting a suspicious person, immediately contact the police and ensure safety at the scene."

[1488] 6. Propose solutions based on emotions

[1489] The server selects appropriate countermeasures based on the summarized legal information and the user's emotional state as determined by an emotion analysis engine. For example, if the server determines that the user is nervous, it selects content that emphasizes how to quickly contact the police or praises the first responders.

[1490] 7. Individual customization

[1491] The server generates a customized response based on the selected emotional state, and generates information including details of the response in a format that can be provided to the user.

[1492] 8. Providing Results to Users

[1493] The server sends the generated summary information and countermeasures to the user device (smart glasses). The user device receives this information and displays it to the user. Based on this information, the user can understand the specific countermeasures and proceed with the necessary procedures.

[1494] Hardware and software used

[1495] Hardware

[1496] Smart glasses: Wearable devices with voice recognition and display capabilities (e.g., Google Glass, Microsoft HoloLens)

[1497] software

[1498] Sentiment analysis engine: Python Sentiment Analysis library (e.g. textblob)

[1499] Generative AI: OpenAI's GPT-3 or GPT-4 API

[1500] Data acquisition: Realized using RESTful API (e.g., regulatory information API)

[1501] Prompt Sentence Examples

[1502] “Based on the summarized information about security regulations and considering that the user is anxious feeling, provide a suitable solution to handle unidentified persons in restricted areas.”

[1503] This system allows security staff to quickly obtain appropriate legal and regulatory information and countermeasures on-site, and to take the most appropriate action depending on the situation through sentiment analysis.

[1504] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1505] Step 1:

[1506] The user inputs a request for information about specific laws and regulations from the device. The request is generated by the user speaking into the smart glasses, such as "I would like to know about the legal procedures when a suspicious person is discovered at the scene." The input voice data is converted into text format, and the device sends the request to the server.

[1507] Step 2:

[1508] The server receives a request sent from the terminal. The received request data is text data containing a specific legal topic for which the user is seeking confirmation. The server analyzes this text data and identifies the specific legal topic being sought. In this case, "Legal procedures regarding suspicious persons" is extracted.

[1509] Step 3:

[1510] The server passes the received request to an emotion analysis engine, which analyzes the user's emotional state. Using the text data extracted from the voice request as input, the emotion analysis engine identifies the user's emotional state (e.g., nervousness, anxiety). The analysis result is that the user is "nervous."

[1511] Step 4:

[1512] The server accesses a regulatory database or external API to retrieve the latest information related to the identified regulatory topic. For example, to retrieve data on the Peace Preservation Act or legal procedures for dealing with suspicious persons, the server sends a request to the API endpoint and retrieves the required data.

[1513] Step 5:

[1514] The acquired information is passed to a generative AI, which then summarises it concisely. Using legal and regulatory data as input, the generative AI extracts key points and outputs a concise summary. For example, a generated summary might read, "When arresting a suspicious individual, contact the police immediately and ensure safety at the scene."

[1515] Step 6:

[1516] The server selects appropriate countermeasures based on the generated summary information and the user's emotional state recognized by the emotion analysis engine. Countermeasures are generated using the analysis results and summary data as input, such as "If the user is nervous, emphasize how to quickly contact the police or praise the first responders."

[1517] Step 7:

[1518] The server then generates and customizes the selected countermeasures based on the user's emotional state. Using the countermeasures and emotional data as input, the server customizes the countermeasures based on the user's emotional state, and generates specific details of the countermeasures. For example, the server adds details to the countermeasures, such as "procedures for contacting the public security department" and "the importance of initial response."

[1519] Step 8:

[1520] The server sends the customized summary information and countermeasures to the user's device. The device receives the generated data as input and displays the summary of legal proceedings and countermeasures on the smart glasses display. The user can review this information and take appropriate action based on it.

[1521] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1522] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1523] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1524] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1525] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1526] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1527] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1528] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1529] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1530] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1531] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1532] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1533] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1534] 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.

[1535] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1536] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1537] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1538] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1539] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1540] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1541] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1542] The following is further disclosed regarding the above embodiment.

[1543] (Claim 1)

[1544] a means of receiving regulatory requests;

[1545] means for parsing the received request to extract specific regulatory information;

[1546] A means to retrieve up-to-date regulatory information from external databases or APIs; and

[1547] A method for summarizing acquired legal and regulatory information using generative AI to concisely summarize it, and

[1548] a means for selecting and generating appropriate countermeasures based on the summarized regulatory information;

[1549] The system includes a means for providing the generated remedy to a user.

[1550] (Claim 2)

[1551] 10. The system of claim 1, further comprising: receiving a request regarding a law or regulation sent from a user terminal; analyzing the request to extract information regarding the specific law or regulation.

[1552] (Claim 3)

[1553] The system of claim 1 uses generative AI to summarize legal and regulatory information obtained from an external database or external API, generates appropriate countermeasures based on the summary results, and provides the countermeasures to the user.

[1554] "Example 1"

[1555] (Claim 1)

[1556] a means of receiving regulatory requests;

[1557] means for parsing the received request to extract specific regulatory information;

[1558] A means to retrieve up-to-date regulatory information from external databases or APIs; and

[1559] A method for summarizing acquired legal and regulatory information using generative AI to concisely summarize it, and

[1560] a means for selecting and generating appropriate countermeasures based on the summarized regulatory information;

[1561] means for providing the generated countermeasures to a user;

[1562] A means for analyzing data obtained from external information sources using natural language processing techniques;

[1563] A system including a means for prompting a generative AI model to generate a summary of legal and regulatory information using a prompt statement.

[1564] (Claim 2)

[1565] 10. The system of claim 1, further comprising: receiving a request for legal regulations sent from a user terminal; analyzing the request to extract information relating to the specific legal regulations; and analyzing data from external sources using natural language processing techniques.

[1566] (Claim 3)

[1567] The system of claim 1 uses generative AI to summarize legal and regulatory information obtained from an external database or external API, prompts the user to summarize using prompt sentences, generates appropriate countermeasures based on the summary results, and provides the countermeasures to the user.

[1568] "Application Example 1"

[1569] (Claim 1)

[1570] a means of receiving regulatory requests;

[1571] means for parsing the received request to extract specific regulatory information;

[1572] A means to retrieve up-to-date regulatory information from external databases or APIs; and

[1573] A method for summarizing acquired legal and regulatory information using generative AI to concisely summarize it, and

[1574] a means for selecting and generating appropriate countermeasures based on the summarized regulatory information;

[1575] means for transmitting the summarized regulatory information and countermeasures to a user terminal;

[1576] A means to propose measures for food delivery companies in accordance with new regulations, and

[1577] A means to reflect the proposed countermeasures in the food delivery company's business system, and

[1578] a means for providing an appropriate prompt to the generative AI model;

[1579] A system including:

[1580] (Claim 2)

[1581] 10. The system of claim 1, further comprising: receiving a request regarding a law or regulation sent from a user terminal; analyzing the request to extract information regarding the specific law or regulation.

[1582] (Claim 3)

[1583] The system of claim 1 uses generative AI to summarize legal and regulatory information obtained from an external database or external API, generates appropriate countermeasures based on the summary results, and provides the countermeasures to the user.

[1584] "Example 2: Combining Emotion Engines"

[1585] (Claim 1)

[1586] a means of receiving regulatory requests;

[1587] means for parsing the received request to extract specific regulatory information;

[1588] means for analyzing user emotions;

[1589] A means to retrieve up-to-date regulatory information from external databases or APIs; and

[1590] A method for summarizing acquired legal and regulatory information using generative AI to concisely summarize it, and

[1591] A means for selecting and generating appropriate countermeasures based on the summarized legal and regulatory information and the user's sentiment;

[1592] The system includes a means for providing the generated remedy to a user.

[1593] (Claim 2)

[1594] 10. The system of claim 1, wherein the system receives a request regarding a law or regulation sent from a user terminal, analyzes the request, and extracts information regarding the specific law or regulation and the user's sentiment.

[1595] (Claim 3)

[1596] The system of claim 1 uses generative AI to summarize legal and regulatory information obtained from an external database or external API, generates appropriate countermeasures based on the summary results and the user's feelings, and provides the countermeasures to the user.

[1597] "Application example 2 when combining emotion engines"

[1598] (Claim 1)

[1599] a means of receiving regulatory requests;

[1600] means for parsing the received request to extract specific regulatory information;

[1601] A means to retrieve up-to-date regulatory information from external databases or APIs; and

[1602] A method for summarizing acquired legal and regulatory information using generative AI to concisely summarize it, and

[1603] a means for selecting and generating appropriate countermeasures based on the summarized regulatory information;

[1604] an emotion analysis engine means for analyzing the user's emotional state;

[1605] a means for customizing summary information and responses based on emotional state;

[1606] A system that includes a means to provide appropriate remedial action and customized information to users.

[1607] (Claim 2)

[1608] 10. The system of claim 1, further comprising: receiving a request regarding a law or regulation sent from a user terminal; analyzing the request to extract information regarding the specific law or regulation.

[1609] (Claim 3)

[1610] The system of claim 1 uses generative AI to summarize legal and regulatory information obtained from an external database or external API, generates appropriate countermeasures based on the summary results, and provides the countermeasures to the user. [Explanation of symbols]

[1611] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means of receiving regulatory requests; means for parsing the received request to extract specific regulatory information; A means to retrieve up-to-date regulatory information from external databases or APIs; and A method for summarizing acquired legal and regulatory information using generative AI to concisely summarize it, and a means for selecting and generating appropriate countermeasures based on the summarized regulatory information; The system includes a means for providing the generated remedy to a user.

2. 10. The system of claim 1, further comprising: receiving a regulatory request sent from a user terminal; analyzing the request to extract information relating to a particular regulatory request.

3. The system of claim 1, which uses generative AI to summarize legal and regulatory information obtained from an external database or external API, generates appropriate countermeasures based on the summary results, and provides the countermeasures to the user.

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