System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a legal question from a user; means for analyzing the received question; means for generating an appropriate answer based on the analyzed question; and means for providing the generated answer to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, while the use of digital content is increasing, it is difficult for ordinary users to understand legal terminology and complex rules. This can prevent users from obtaining appropriate legal advice quickly, leading to uncertainty and risk. Furthermore, when legal issues become serious, access to legal experts is necessary, but this process is time-consuming, and a speedy response is required. The objective of this invention is to provide prompt and accurate advice to users in situations where they have legal questions or concerns about illegality, thereby reducing their anxiety and risk. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including the following means: a means for accepting legal questions from users, a means for analyzing the accepted questions, a means for generating appropriate answers based on the analyzed questions, and a means for providing the generated answers to the users. The system also includes a means for providing a paid consultation option with a specialist when more detailed advice is needed on the generated answers. Furthermore, the system includes a means for analyzing questions accepted from users using a natural language processing engine, enabling faster and more accurate analysis and answer generation. This allows users to obtain fast and accurate legal advice, minimizing uncertainty and risk.

[0006] "User" means any person or entity that uses the System to ask a legal question.

[0007] "Legal inquiries" refer to questions or inquiries regarding the interpretation or application of law to specific situations or matters.

[0008] "Means for receiving" refers to the interface or process for obtaining legal questions as input from users and transmitting them to the system.

[0009] "Analysis tools" refers to the technology and algorithms used to process incoming legal queries and identify relevant keywords and legal topics.

[0010] A "natural language processing engine" refers to a program or system that understands human language, analyzes sentences, and extracts context.

[0011] "Generating means" refers to the techniques and algorithms used to construct appropriate answers to the analyzed legal questions.

[0012] "Means of providing" refers to the interface or process for displaying or notifying the user of the generated legal response.

[0013] "Paid professional consultation option" means a fee-based service or means of access to receive in-depth advice or counseling from a professional on a legal matter.

[0014] "Accurate legal advice" refers to reliable advice and interpretations that are based on up-to-date legal knowledge and relevant legal materials. [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 provides users with fast and accurate answers to legal questions. The system is designed to enable users to quickly obtain advice when they encounter legal questions or problems.

[0037] System Overview

[0038] The system mainly consists of the following elements:

[0039] 1. A device for inputting questions from users

[0040] 2. A server that receives the input question, analyzes it, and generates an answer

[0041] 3. Natural language processing engine for analysis and answer generation

[0042] 4. A means of providing generated answers to users

[0043] 5. Access to paid experts for in-depth advice

[0044] Program processing

[0045] User question input

[0046] Users enter legal questions from their devices (PCs, smartphones, etc.). For example, a user might enter a question like, "Can I use an image I found online on my blog?"

[0047] Submit a Question

[0048] The terminal sends the entered question to the server, which receives it and proceeds to the next step of the analysis process.

[0049] Question Analysis

[0050] The server passes the received question to a natural language processing engine, which analyzes the question. The natural language processing engine tokenizes the question and extracts phrases and keywords. For example, keywords such as "online images," "blog," and "use" are extracted.

[0051] Identifying Legal Topics

[0052] The server uses the extracted keywords to identify relevant legal topics, including referencing legal databases and related materials, such as "copyright law" or "digital media terms of use."

[0053] Generate answers

[0054] The server's AI engine generates appropriate answers based on the identified legal topic, references historical data and legal documents to build reliable answers, and then formats the generated answers in natural language. For example, a response might be, "Unauthorized use of images from the Internet may constitute copyright infringement."

[0055] Submit your answer

[0056] The generated answer is sent from the server to the user's device, where it is displayed. For example, the answer may read, "Unauthorized use of images may constitute copyright infringement. We recommend obtaining permission or using copyright-free images."

[0057] Actions to take in case of serious problems

[0058] If the user has additional questions or needs further advice, the server may offer a paid expert consultation option, where the user can obtain further detailed advice from the expert.

[0059] Specific examples

[0060] User Question: "Can I use your music in a YouTube video?"

[0061] Question analysis: "YouTube" "Music" "Use"

[0062] Identifying legal topics: "Copyright Law" and "YouTube Terms of Service"

[0063] Generate Answer: "Unauthorized use of music can be copyright infringement."

[0064] Answer provided: "Using music in your YouTube videos without permission may be copyright infringement. We recommend officially licensing or using royalty-free music."

[0065] By implementing this system, users can receive fast and accurate legal advice, minimizing uncertainty and risk, and can also seek expert advice if necessary.

[0066] The processing flow will be explained below.

[0067] Step 1:

[0068] A user enters a legal question into a terminal. For example, the user enters a question such as, "Can I use an image from the Internet on my blog?"

[0069] Step 2:

[0070] The terminal transmits the entered question to the server.

[0071] Step 3:

[0072] The server passes the received question to a natural language processing engine, which analyzes the question.

[0073] Step 4:

[0074] A natural language processing engine tokenizes the question and extracts phrases and keywords, such as "online images," "blogs," and "use."

[0075] Step 5:

[0076] The server then consults a legal database to identify relevant legal topics based on the extracted keywords, such as "copyright law" or "digital media terms of use."

[0077] Step 6:

[0078] The server's AI engine generates appropriate answers based on the identified legal topic, such as "Unauthorized use of images found online may constitute copyright infringement" by referencing historical data and legal documents.

[0079] Step 7:

[0080] The server formats the generated answer in natural language.

[0081] Step 8:

[0082] The server sends the formatted response to the user's terminal.

[0083] Step 9:

[0084] The device receives the response from the server and displays it to the user, for example, a message saying, "Unauthorized use of images may constitute copyright infringement. We recommend obtaining permission or using copyright-free images."

[0085] Step 10:

[0086] If the user has additional questions or needs more detailed assistance, the user enters further questions.

[0087] Step 11:

[0088] The server accepts and analyzes additional questions and suggests options for consultation with a paid expert, for example, by displaying "We recommend you consult with a paid expert" and providing a link to access the expert.

[0089] Example 1

[0090] 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."

[0091] In modern society, it is not easy to obtain fast and accurate advice when faced with legal questions or problems. In particular, the legal risks associated with using content on the Internet involve complex laws, making it difficult for the general public to obtain accurate information. Furthermore, under the current system, detailed legal advice requires direct consultation with an expert, which is a time-consuming and costly burden. To solve these issues, there is a need for a system that provides fast and accurate legal advice and allows access to expert advice when necessary.

[0092] 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.

[0093] In this invention, the server includes means for accepting legal questions from users, means for analyzing the accepted questions, means for generating appropriate answers based on the analyzed questions, means for providing the generated answers to the users, means for analyzing the accepted questions from users using a natural language processing engine, means for identifying legal topics based on the analyzed questions, and means for formatting the generated answers in natural language, thereby enabling the provision of prompt and accurate legal advice and, if necessary, the provision of detailed expert advice.

[0094] A "user" is an individual or company that enters a legal question into the system for the purpose of obtaining an answer.

[0095] A "terminal" is a device, such as a computer or smartphone, that a user uses to access the system and enter legal questions.

[0096] A "server" is a computer system that receives questions sent by users, analyzes them, and generates answers.

[0097] A "natural language processing engine" is a software technology that analyzes questions entered by users and extracts phrases and keywords.

[0098] "Tokenization" is the process by which a natural language processing engine breaks up input text and converts it into individual words and phrases.

[0099] A "legal topic" is a legal subject or issue area identified based on extracted keywords.

[0100] "Analysis" is the process of breaking down the received question and understanding its meaning.

[0101] "Answer generation" is the process of constructing an appropriate answer based on the parsed question.

[0102] "Natural language formatting" refers to converting the generated answers into text that is easy for humans to understand.

[0103] A "paid expert" is an individual or firm with a legal profession who is booked to provide in-depth legal advice.

[0104] The "Consultation Option" is an option to receive in-depth legal advice from a paid professional.

[0105] "Secure communication" is a communication method in which data is encrypted before transmission to prevent unauthorized access or tampering by third parties.

[0106] An "SQL query" is a standard query language used to retrieve specific information from a database.

[0107] A "generative AI model" is an artificial intelligence algorithm that is trained to generate appropriate answers to user input.

[0108] MODE FOR CARRYING OUT THE INVENTION

[0109] This invention is a system that provides users with fast and accurate answers to legal questions. The system is designed to enable users to quickly obtain advice when they encounter legal questions or problems.

[0110] System Overview

[0111] The system mainly consists of the following elements:

[0112] 1. A device for inputting questions from users

[0113] 2. A server that receives the input question, analyzes it, and generates an answer

[0114] 3. Natural language processing engine for question analysis and answer generation

[0115] 4. A means of providing generated answers to users

[0116] 5. Access to paid experts for in-depth advice

[0117] Specific Embodiments

[0118] User question input

[0119] Users enter legal questions from a device (such as a PC or smartphone). Specifically, the user enters a question such as, "Can I use an image I found online on my blog?" The device can be a web browser or a dedicated app.

[0120] Submit a Question

[0121] The device sends the entered question to the server, which receives it and proceeds to the next step of analysis. The HTTPS protocol is used to ensure secure communication when sending data.

[0122] Question Analysis

[0123] The server passes the received question to a natural language processing engine, which analyzes the question. The natural language processing engine tokenizes the question and extracts phrases and keywords. Specifically, the keywords extracted are "online images," "blog," and "use."

[0124] Identifying Legal Topics

[0125] The server uses the extracted keywords to identify relevant legal topics, including referencing legal databases and related materials, such as "copyright law" or "digital media terms of use."

[0126] Generate answers

[0127] The server's AI engine generates appropriate answers based on the identified legal topic. It references historical data and legal documents to build reliable answers. It then formats the generated answers in natural language. For example, it might generate an answer like, "Unauthorized use of images found online may be a copyright infringement."

[0128] Overview of the generative AI model and example prompts

[0129] The server generates answers using a generative AI model (e.g., GPT-4). For example, in response to the question, "Is it okay to use images from the Internet on my blog?", the answer would be, "Using images without permission may be a copyright infringement. We recommend obtaining permission or using copyright-free images."

[0130] Submit your answer

[0131] The generated answer is sent from the server to the user's device. The device receives the answer and displays it in a user-friendly format. For example, a message such as "Unauthorized use of images may constitute copyright infringement. We recommend obtaining permission or using copyright-free images" may be displayed.

[0132] Actions to take in case of serious problems

[0133] If the user has additional questions or needs further advice, the server may offer a paid expert consultation option, where the user can obtain further detailed advice from the expert.

[0134] By implementing this system, users can receive fast and accurate legal advice, minimizing uncertainty and risk, and can access expert advice when necessary.

[0135] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0136] Step 1:

[0137] The user inputs a legal question using a terminal. The terminal provides an interface for the user to input a string such as "Can I use an image from the Internet on my blog?" The input question is stored in an input field on the terminal.

[0138] Input: User's legal question text

[0139] Output: The question text entered

[0140] Step 2:

[0141] The terminal sends the entered question to the server. In actual operation, the request content is converted into JSON format and a POST request is sent to the server using the HTTPS protocol.

[0142] Input: Question text

[0143] Output: JSON request sent to the server

[0144] Step 3:

[0145] The server passes the received question to a natural language processing engine for analysis. Specifically, the question is tokenized and phrases and keywords are extracted. The server then provides the received text to an NLP (natural language processing) library to obtain keywords such as "online images," "blogs," and "use."

[0146] Input: Question text in JSON format

[0147] Output: Extracted keywords (e.g., "online images," "blogs," "uses")

[0148] Step 4:

[0149] The server then uses the extracted keywords to identify relevant legal topics, consulting pre-built legal databases and indexes. The server then issues search queries to the databases to identify topics such as "copyright law" and "digital media terms of use."

[0150] Input: Extracted keywords

[0151] Output: Identified legal topics

[0152] Step 5:

[0153] The server sends a prompt to the generative AI model based on the identified legal topic and generates an appropriate answer. For example, the AI ​​model receives a prompt such as "Is it okay to use an image from the Internet on my blog?" and receives the answer "Unauthorized use may constitute copyright infringement."

[0154] Input: Legal topic and prompt

[0155] Output: Generated answer text

[0156] Step 6:

[0157] The server converts the generated response into JSON format and returns it to the user's device. The server serializes the generated response into JSON format and sends the response to the device using the HTTPS protocol.

[0158] Input: Generated answer text

[0159] Output: JSON formatted response data

[0160] Step 7:

[0161] The device displays the response received from the server. Specifically, the device parses the JSON data and displays it on the screen in a user-friendly format. For example, it displays a message saying, "Unauthorized use of images may be a copyright infringement. We recommend obtaining permission or using copyright-free images."

[0162] Input: JSON formatted response data

[0163] Output: User-friendly formatted answer display

[0164] Step 8:

[0165] If the user has additional questions or needs further advice, the server offers the option of a paid expert consultation: after the user provides additional input, the server returns a link that takes the user to a paid consultation booking page.

[0166] Input: User's additional questions or requests for further assistance

[0167] Output: Paid consultation booking page link

[0168] (Application example 1)

[0169] 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."

[0170] In conventional virtual stores, when users had legal questions about products or services, there were limited ways to get appropriate and prompt answers. This often left users feeling uncertain and anxious, which could discourage them from making a purchase. Furthermore, when users needed specialized legal advice, there was no established way to quickly access an appropriate expert. This sometimes led to a poor user experience.

[0171] 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.

[0172] In this invention, the server includes means for accepting legal questions from users, means for analyzing the accepted questions, means for generating appropriate answers based on the analyzed questions, means for providing the generated answers to the users, means for accepting questions while using the virtual store, and means for quickly providing answers to legal questions in the virtual store, thereby enabling users to quickly resolve their legal questions in the virtual store and use products and services with peace of mind.

[0173] A "user" is someone who uses the system to ask a legal question.

[0174] "Legal Question" means a question that involves a question or issue relating to the law.

[0175] "Means for receiving" refers to the interface or process for receiving input from the user.

[0176] "Means of analysis" refers to the process of interpreting the questions received and extracting important keywords and context.

[0177] "Means for generating appropriate answers" refers to a process that automatically generates reliable answers based on the analyzed information.

[0178] "Means for providing" refers to the process or interface for presenting the generated answer to the user.

[0179] A "virtual store" refers to a shop or service provider operated on the Internet.

[0180] "Means for accepting questions (while using the virtual store)" refers to a mechanism for accepting questions at any time from users using the virtual store.

[0181] "Means for quickly providing answers to legal questions" refers to a system that efficiently carries out the process from entering a question to providing an answer.

[0182] The present invention provides a system for providing a prompt and appropriate answer when a user asks a legal question in a virtual store. Specific embodiments of the present invention will be described below.

[0183] System Overview

[0184] The system mainly consists of the following elements:

[0185] 1. A device for inputting questions from users

[0186] 2. A server that receives the input question, analyzes it, and generates an answer

[0187] 3. Natural language processing engine for analysis and answer generation

[0188] 4. A means of providing generated answers to users

[0189] 5. A means of answering questions while using the virtual store

[0190] 6. A fast way to get answers to your legal questions

[0191] Specific processing of the program

[0192] The server receives legal questions from the user's device. The questions are sent in JSON format and are received by the server. The received questions are then passed to a natural language processing engine, which uses Hugging Face's Transformers library.

[0193] The engine analyzes the question, extracts relevant keywords and context, and then consults legal documents and databases to generate an appropriate answer. This answer is formatted in natural language that is easy for the user to understand. The formatted answer is then returned from the server to the user's device and displayed to the user.

[0194] Hardware and software used

[0195] Hardware: Smartphones, servers

[0196] Software: Python, Flask, Transformers library

[0197] Data processing and calculation flow

[0198] The server first tokenizes the received question and extracts phrases and keywords. Based on the results of this analysis, it identifies relevant legal topics. Based on the identified topics, it references past data and legal documents to generate an appropriate answer. The generated answer is then formatted in natural language and sent to the user's device.

[0199] Specific examples

[0200] User Question: "Is it legal to share images of this product on social media?"

[0201] Question analysis: "Product", "Image", "Social Media", "Share", "Legal Issues"

[0202] Identifying legal topics: "Copyright Law" and "Social Media Terms of Use"

[0203] Generate Answer: "Sharing product images on social media without permission may be a copyright infringement."

[0204] Answer provided: "Sharing images without permission may be copyright infringement. We recommend getting permission or using copyright-free images."

[0205] Prompt Sentence Examples

[0206] Is it legal to share images of this product on social media?

[0207] In this way, by implementing the present invention, users can instantly resolve legal questions in the virtual store, allowing them to use products and services with peace of mind.

[0208] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0209] Step 1:

[0210] Users enter legal questions into the virtual storefront

[0211] Users use a device such as a smartphone to input legal questions into the virtual store's interface. The input questions are sent to the server via an API. The input data is in text format, and a question such as "Is it legal to share an image of this product on social media?" is sent.

[0212] Step 2:

[0213] The server receives the user's question.

[0214] The server first collects and stores data to process the received questions. Specifically, it prepares the received text data to be passed to an analysis engine (e.g., a Flask application). The input data is the question in text format, and the output data is the same question data in text format.

[0215] Step 3:

[0216] The server analyzes the question using a natural language processing engine

[0217] The server passes the received question to a natural language processing engine (for example, Hugging Face's Transformers library), which tokenizes (splits) the question and extracts keywords. For example, the keywords extracted are "SNS," "image," "share," and "legal issue." The input data is the question text, and the output data is a list of extracted keywords.

[0218] Step 4:

[0219] The server identifies legal topics based on the extracted keywords.

[0220] The server then uses the extracted keywords to refer to legal databases and materials to identify relevant legal topics. This process involves researching and identifying copyright laws, social networking site terms of use, etc. The input data is a list of keywords, and the output data is a list of identified legal topics.

[0221] Step 5:

[0222] The server generates answers based on legal topics

[0223] The server generates appropriate answers based on the identified legal topics. It uses a generative AI model to reference historical data and legal documents to build reliable answers. The input data is a list of legal topics, and the output data is the generated answer text. For example, a generated answer might be, "Sharing product images on social media without permission may be a copyright infringement."

[0224] Step 6:

[0225] The server formats the generated answer in natural language and provides it to the user.

[0226] The generated answer is formatted in natural language so that it is easy for the user to understand. The formatted answer is then sent back to the user's device from the server. The input data is the generated answer text, and the output data is the formatted answer text. For example, the answer may be provided in the form of "Unauthorized image sharing may be a copyright infringement. We recommend obtaining permission or using a copyright-free image."

[0227] Step 7:

[0228] If the user reviews the answers provided and asks additional questions, the server is prepared to provide more detailed advice.

[0229] If the user checks the provided answer and needs further advice, the server provides a paid expert consultation option. In this case, the user can obtain further detailed advice from the expert. The input data is the user's follow-up question, and the output data is the information provided by the consultation option.

[0230] 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.

[0231] This invention is a system that provides quick and accurate answers to legal questions from users. By incorporating an emotion engine that recognizes the user's emotions, this system achieves more personalized responses. The specific configuration of the system and the program processing are described below.

[0232] System Overview

[0233] The system mainly consists of the following elements:

[0234] 1. A device for inputting questions from users

[0235] 2. A server that receives the input question, analyzes it, and generates an answer

[0236] 3. Natural language processing engine for analysis and answer generation

[0237] 4. Emotion engine that analyzes user emotions

[0238] 5. How to provide generated answers to users

[0239] 6. Access to paid experts for in-depth advice

[0240] Program processing

[0241] User question input

[0242] Users enter legal questions from their devices (PCs, smartphones, etc.). For example, a user might enter a question like, "Can I use an image I found online on my blog?"

[0243] Submit a Question

[0244] The terminal sends the entered question to the server, which receives it and proceeds to the next step of the analysis process.

[0245] Question Analysis

[0246] The server passes the received question to a natural language processing engine, which analyzes the question. The natural language processing engine tokenizes the question and extracts phrases and keywords. For example, keywords such as "online images," "blog," and "use" are extracted.

[0247] Emotion Analysis

[0248] At the same time, the server passes the question to an emotion engine to analyze the user's emotions, which analyzes the emotional data contained in the text and identifies whether the user is anxious, angry, or confused.

[0249] Identifying Legal Topics

[0250] The server uses the extracted keywords and sentiment data to identify relevant legal topics, including referencing legal databases and related materials, such as "Copyright Law" and "Digital Media Terms of Use."

[0251] Generate answers

[0252] The server's AI engine generates appropriate answers based on the identified legal topic and emotional data. It references historical data and legal documents and adjusts the tone and content of the answer depending on the user's emotional state. For example, the answer might read, "Using images from the Internet without permission may constitute copyright infringement. If you are concerned, we recommend getting permission or using copyright-free images."

[0253] Submit your answer

[0254] The generated answer is sent from the server to the user's device, where it is displayed. For example, the answer may read, "Unauthorized use of images may constitute copyright infringement. We recommend obtaining permission or using copyright-free images."

[0255] Actions to take in case of serious problems

[0256] If the user has additional questions or needs more detailed advice, they can enter more questions. The server accepts and parses the additional questions and suggests options for consulting with a paid expert. For example, it might say, "We recommend you consult with a paid expert," and provide a link to access the expert.

[0257] Specific examples

[0258] User Question: "Can I use your music in a YouTube video?"

[0259] Question analysis: "YouTube" "Music" "Use"

[0260] Sentiment analysis: Identifying when a user may be feeling anxious

[0261] Identifying legal topics: "Copyright Law" and "YouTube Terms of Service"

[0262] Generate an answer: "Unauthorized use of music may constitute copyright infringement. If you are concerned, we recommend obtaining an official license or using royalty-free music."

[0263] Answer provided: "Using music in your YouTube videos without permission may be copyright infringement. We recommend officially licensing or using royalty-free music."

[0264] By implementing this system, users can not only receive fast and accurate legal advice, but also emotional support, and, if necessary, access to expert advice.

[0265] The processing flow will be explained below.

[0266] Step 1:

[0267] A user enters a legal question into a terminal. For example, the user enters a question such as, "Can I use an image from the Internet on my blog?"

[0268] Step 2:

[0269] The terminal transmits the entered question to the server.

[0270] Step 3:

[0271] The server passes the received question to a natural language processing engine, which analyzes the question. The natural language processing engine tokenizes the question and extracts phrases and keywords. For example, it extracts keywords such as "online images," "blog," and "use."

[0272] Step 4:

[0273] The server passes the parsed question to an emotion engine to analyze the user's emotions. The emotion engine analyzes the emotional data contained in the text and identifies whether the user is anxious, angry, or confused.

[0274] Step 5:

[0275] The server uses the extracted keywords and sentiment data to identify relevant legal topics, including referencing legal databases and related materials, such as "Copyright Law" and "Digital Media Terms of Use."

[0276] Step 6:

[0277] The server's AI engine generates appropriate answers based on the identified legal topic and emotional data. It references historical data and legal documents and adjusts the tone and content of the answer depending on the user's emotional state. For example, it might generate an answer like, "Using images from the Internet without permission may constitute copyright infringement. If you are concerned, we recommend getting permission or using copyright-free images."

[0278] Step 7:

[0279] The server formats the generated answer in natural language.

[0280] Step 8:

[0281] The server sends the formatted response to the user's terminal.

[0282] Step 9:

[0283] The device receives the response from the server and displays it to the user, for example, a message saying, "Unauthorized use of images may constitute copyright infringement. We recommend obtaining permission or using copyright-free images."

[0284] Step 10:

[0285] If the user has additional questions or needs more detailed assistance, the user enters further questions.

[0286] Step 11:

[0287] The server accepts and analyzes additional questions and suggests options for consultation with a paid expert, for example, by displaying "We recommend you consult with a paid expert" and providing a link to access the expert.

[0288] Example 2

[0289] 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."

[0290] Conventional legal advice systems provide standard answers without considering the user's feelings, which can result in users being unable to receive optimal advice. Furthermore, in cases of serious problems, detailed advice from an expert may be required, but the lack of such an option presents a challenge.

[0291] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for accepting legal questions from users, means for analyzing the questions using a natural language processing engine, means for identifying relevant legal topics, means for analyzing the user's emotions using an emotion engine, means for generating appropriate answers using a generative AI model, and means for providing the generated answers to the user. This enables more appropriate and personalized legal advice according to the user's emotional state. In addition, in cases of serious problems, a paid consultation option with a specialist can be provided.

[0292] The "means for accepting legal questions from users" refers to an interface that allows users to input legal questions using a device such as a PC or smartphone and send them to the system.

[0293] The "means of analyzing questions using a natural language processing engine" refers to a means of using natural language processing technology to tokenize received questions and extract phrases and keywords.

[0294] The "means for identifying relevant legal topics" refers to a means for finding appropriate legal topics by referring to legal databases and related materials based on the analyzed keywords.

[0295] The "means for analyzing user emotions using an emotion engine" refers to a means for analyzing text data of questions from users and using emotion analysis technology to identify whether the user is feeling emotions such as anxiety, anger, or confusion.

[0296] "Means for generating appropriate answers using a generative AI model" refers to means for using an artificial intelligence model to automatically generate appropriate legal advice based on relevant legal topics and sentiment data.

[0297] The "means for providing the generated answer to the user" refers to a means for transmitting the generated legal advice to the user's terminal and displaying it.

[0298] A "means for providing a fee-based expert consultation option" is a means for providing a user with the option to access a fee-based legal expert if the user requires more detailed advice.

[0299] MODE FOR CARRYING OUT THE INVENTION

[0300] This invention is a system that provides quick and accurate answers to legal questions from users. By incorporating an emotion engine that recognizes the user's emotions, this system achieves more personalized responses. The specific configuration of the system and the program processing are described below.

[0301] System Components

[0302] The system mainly consists of the following elements:

[0303] 1. A device for inputting questions from users

[0304] 2. A server that receives the input question, analyzes it, and generates an answer

[0305] 3. A natural language processing engine to analyze questions

[0306] 4. Emotion engine that analyzes user emotions

[0307] 5. How to provide generated answers to users

[0308] 6. Access to paid experts for in-depth advice

[0309] System Functions and Operation

[0310] A user inputs a legal question from a device (such as a PC or smartphone). For example, a user may input a question such as, "Can I use an image I found online on my blog?"

[0311] The device sends the entered question to the server. The server receives it and proceeds to the next step of analysis. First, the server passes the received question to a natural language processing engine (such as Hugging Face's Transformers) to analyze the question. The natural language processing engine tokenizes the question and extracts phrases and keywords. In this example, the keywords extracted are "online images," "blog," and "use."

[0312] At the same time, the server passes the question to an emotion engine (such as the Google Cloud Natural Language API) to analyze the user's emotions. The emotion engine analyzes the emotional data contained in the text and identifies whether the user is anxious, angry, or confused. For example, an emotion score of "Anxious: 0.75" is obtained.

[0313] The server uses the extracted keywords and sentiment data to identify relevant legal topics, including referencing legal databases and related materials, such as "Copyright Law" and "Digital Media Terms of Use."

[0314] Next, a generative AI model (such as OpenAI GPT-4) on the server generates an appropriate answer based on the identified legal topic and emotional data. The generative AI model refers to legal documents and past consultation cases and adjusts the tone and content of the answer depending on the user's emotional state. For example, a generated answer might read, "Using images from the Internet without permission may constitute copyright infringement. If you are concerned, we recommend obtaining permission or using copyright-free images."

[0315] The generated answer is sent from the server to the user's device. The device displays the answer, for example, a message saying, "Unauthorized use of images may constitute copyright infringement. We recommend obtaining permission or using copyright-free images."

[0316] If the user has additional questions or needs more detailed advice, they can enter their questions again. The server will accept the additional questions and perform the same analysis process. If the issue is deemed to be a serious legal problem, the server will offer the option of consulting a paid expert. For example, it may provide a message saying, "We recommend that you consult with a paid expert," along with a link to the expert.

[0317] Examples of prompt statements

[0318] Question: "Can I use your music in a YouTube video?"

[0319] Question analysis: Keywords "YouTube", "music", "usage"

[0320] Emotion analysis: Anxiety score 0.70

[0321] Legal topics: "Copyright Law" and "YouTube Terms of Service"

[0322] Generate an answer: "Using music without permission may be copyright infringement. We recommend obtaining an official license or using royalty-free music."

[0323] Answer provided: "Using music in your YouTube videos without permission may be copyright infringement. We recommend officially licensing or using royalty-free music."

[0324] This allows users to receive not only fast and accurate legal advice, but also emotional support, and the ability to seek professional advice if necessary.

[0325] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0326] System program processing flow

[0327] Step 1:

[0328] Users enter legal questions into their devices (PCs or smartphones). For example, a user might enter, "Can I use an image I found online on my blog?" This input becomes input data for the system.

[0329] Step 2:

[0330] The device sends the entered question to the server. The question content, which is input data, is securely transmitted using the HTTPS protocol. The device sends the question data in JSON format to the server's API endpoint as a POST request.

[0331] Step 3:

[0332] The server passes the received question data to a natural language processing engine. Specifically, the server tokenizes the received question and extracts phrases and keywords. For example, "online images," "blogs," and "use" are extracted. The input is the question data, and the output is the tokenized keywords.

[0333] Step 4:

[0334] At the same time, the server passes the question data to the emotion engine, which analyzes the user's emotions. The emotion engine analyzes the emotion data in the text and identifies whether the user is anxious, angry, or confused. For example, the emotion score obtained is "Anxiety: 0.75." The input is the question data, and the output is the emotion score.

[0335] Step 5:

[0336] The server identifies relevant legal topics based on tokenized keywords and sentiment scores obtained from the natural language processing engine and sentiment engine. Specifically, it queries a legal database to search for relevant information such as "copyright law" and "digital media terms of use." The input is keywords and sentiment scores, and the output is legal topics.

[0337] Step 6:

[0338] The server's generative AI model generates an appropriate answer based on the identified legal topic and sentiment score. The generative AI model references past data and legal documents to create an answer that corresponds to the emotional state. For example, it generates the answer, "Using images on the Internet without permission may constitute copyright infringement. If you are concerned, we recommend obtaining permission or using copyright-free images." The input is the legal topic and sentiment score, and the output is the generated answer.

[0339] Step 7:

[0340] The server sends the generated answer to the user's terminal. Before sending, the answer is converted to JSON format and sent securely to the terminal using the HTTPS protocol. The input is the generated answer, and the output is the answer displayed on the terminal.

[0341] Step 8:

[0342] The device displays the response received from the server to the user using JavaScript in the web browser or a mobile app UI framework (e.g., React or Flutter). The user can then decide what to do based on this.

[0343] Step 9:

[0344] If the user has additional questions or requires more detailed advice, they can enter the questions again. The server accepts the additional questions and runs the analysis process again. If the issue is deemed to be a serious legal problem, the server will suggest a paid consultation option with a specialist. For example, it may display a message saying, "We recommend that you consult with a paid specialist," and provide a link to access the specialist. The input is the additional question data, and the output is a suggestion of a paid consultation option.

[0345] (Application example 2)

[0346] 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."

[0347] In today's society, it is important for users to resolve legal questions quickly and accurately. However, conventional legal advice systems do not take into account the user's emotional state, which can potentially increase the user's anxiety and confusion. Furthermore, in certain situations, users often require additional, specialized legal advice. In such cases, the lack of means to quickly access legal experts presents a problem. Furthermore, in certain fields, such as electronic payment services, legal questions arise frequently and require special consideration for addressing them.

[0348] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0349] In this invention, the server includes means for accepting legal questions from users, means for analyzing the accepted questions, means for analyzing the user's emotions, means for generating appropriate answers based on the analyzed questions and emotion data, means for providing the generated answers to the users, and means for providing options for paid consultation with experts, thereby enabling the provision of prompt and accurate legal advice while taking into consideration the user's emotions, and enabling prompt access to experts as needed.

[0350] "Means for accepting legal questions from users" refers to a device or interface that allows the system to recognize and accept legal questions entered by users.

[0351] "Means for analyzing received questions" refers to the technology or process of using natural language processing technology to understand and analyze the meaning of input legal questions.

[0352] "Means for analyzing user emotions" refers to technology for recognizing and analyzing the user's emotional state (e.g., anxiety, joy, confusion, etc.) from text or voice input.

[0353] The "means for generating an appropriate answer based on the analyzed question and emotional data" is a system for automatically generating an optimal legal answer by taking into account the content of the analyzed question and the emotional state of the user.

[0354] The "means for providing the generated legal answer to the user" refers to an interface such as a display or an audio output device for presenting the generated legal answer to the user.

[0355] "Means for providing a fee-based expert consultation option" refers to means or options that allow a user to contact an expert for a fee if they require more detailed advice.

[0356] A "natural language processing engine" is a technology and software that enables computers to understand and analyze human language.

[0357] An "emotion engine" is a technology or software that analyzes emotions from a user's text or voice and outputs the results.

[0358] The following describes an embodiment of the present invention. The system accepts legal questions from users, analyzes them, and provides legal advice that takes into account their emotional state. It also provides paid expert consultations if necessary.

[0359] Hardware and software used

[0360] Device: Smartphone or tablet (iOS or Android)

[0361] Natural Language Processing Engine: Google NLU API or IBM Watson NLP

[0362] Sentiment analysis engine: Microsoft Azure Text Analytics

[0363] Server and answer generation engine: OpenAI GPT-4 model

[0364] Legal Database: A subscribed online legal resource library

[0365] System Programming and Processing

[0366] User question input and reception

[0367] A user launches an electronic payment app on their smartphone or tablet and enters a legal question, which is then sent from the device to the server.

[0368] Question analysis and sentiment analysis

[0369] The server passes the received question to a natural language processing engine (Google NLU API or IBM Watson NLP), which tokenizes the question and extracts keywords.

[0370] At the same time, the server passes the question to a sentiment analysis engine (Microsoft Azure Text Analytics) to analyze the sentiment data contained in the text.

[0371] Generate and provide answers

[0372] The server's AI engine (OpenAI GPT-4 model) generates appropriate legal answers based on the analyzed questions and sentiment data.

[0373] The generated answer is then sent back from the server to the device and provided to the user, who can then view the legal advice on the screen of their smartphone or tablet.

[0374] What to do in case of serious problems

[0375] If the user has additional questions or needs further advice, the server offers a paid expert consultation option, which allows the user to quickly connect with an expert and receive further advice to resolve the issue.

[0376] Specific use cases

[0377] User Questions and Analysis Examples

[0378] For example: "What do I do if I accidentally send money through PayPal?"

[0379] Question analysis: The keywords extracted were "PayPal," "incorrect remittance," and "how to deal with it."

[0380] Emotion analysis: Analyzes whether you are feeling anxious.

[0381] Example of generating and providing answers

[0382] Generated answer: "If you made a mistake with PayPal, select 'Cancel' from the transaction details page. If the problem persists, contact PayPal support."

[0383] Prompt Sentence Examples

[0384] "I accidentally sent money via PayPal. How do I cancel it?"

[0385] By implementing this system, users can receive prompt, accurate, and sensitive legal advice, with quick access to experts when needed.

[0386] In this way, a specific system for efficiently resolving legal issues in electronic payment services can be provided.

[0387] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0388] Step 1:

[0389] A user launches an electronic payment app on their smartphone or tablet and enters a legal question. The question is specific, such as "What should I do if I accidentally send money via PayPal?" After entering the question, the device sends it to the server. The entered data arrives at the server in text format.

[0390] Step 2:

[0391] The server passes the received question to a natural language processing engine (Google NLU API or IBM Watson NLP), where the question is tokenized and keywords are extracted. For example, keywords such as "PayPal," "incorrect transfer," and "how to resolve" are extracted. This makes it easier to understand the structure of the question.

[0392] Step 3:

[0393] At the same time, the server passes the question to a sentiment analysis engine (Microsoft Azure Text Analytics) to analyze the user's emotional state. Emotional data such as anxiety or confusion is extracted from the input text, and a judgment is made that the user is feeling anxious. This emotional data is taken into account when generating an answer.

[0394] Step 4:

[0395] The server identifies relevant legal topics based on the analyzed question data (keywords) and sentiment data. It references legal databases to collect information related to topics such as "electronic payment laws" and "consumer protection laws." This allows it to identify the appropriate legal basis for the question.

[0396] Step 5:

[0397] The server's AI engine (OpenAI GPT-4 model) generates appropriate answers based on the identified legal topic and sentiment data. For example, it generates specific advice such as, "To cancel an erroneous transfer on PayPal, select 'Cancel' from the transaction details page. If the issue persists, contact PayPal support." This generation process combines input data with legal resources to process the data.

[0398] Step 6:

[0399] The generated answer is sent from the server to the device. The user's smartphone or tablet receives the answer and displays it on the screen. The user can see specific advice such as, "If you made an incorrect transfer with PayPal, select 'Cancel' from the transaction details page. If the problem persists, contact PayPal support."

[0400] Step 7:

[0401] If the user requires further advice, the terminal sends an additional question to the server. At this time, the server provides an option to consult with a paid expert. For example, the server may provide a link to access the expert along with a message saying, "If you require further advice, we recommend that you consult with a paid expert." The user can quickly access the expert through this link and receive further detailed legal advice.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] [Second embodiment]

[0406] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0407] 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.

[0408] 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).

[0409] 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.

[0410] 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.

[0411] 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).

[0412] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0417] 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."

[0418] This invention is a system that provides users with fast and accurate answers to legal questions. The system is designed to enable users to quickly obtain advice when they encounter legal questions or problems.

[0419] System Overview

[0420] The system mainly consists of the following elements:

[0421] 1. A device for inputting questions from users

[0422] 2. A server that receives the input question, analyzes it, and generates an answer

[0423] 3. Natural language processing engine for analysis and answer generation

[0424] 4. A means of providing generated answers to users

[0425] 5. Access to paid experts for in-depth advice

[0426] Program processing

[0427] User question input

[0428] Users enter legal questions from their devices (PCs, smartphones, etc.). For example, a user might enter a question like, "Can I use an image I found online on my blog?"

[0429] Submit a Question

[0430] The terminal sends the entered question to the server, which receives it and proceeds to the next step of the analysis process.

[0431] Question Analysis

[0432] The server passes the received question to a natural language processing engine, which analyzes the question. The natural language processing engine tokenizes the question and extracts phrases and keywords. For example, keywords such as "online images," "blog," and "use" are extracted.

[0433] Identifying Legal Topics

[0434] The server uses the extracted keywords to identify relevant legal topics, including referencing legal databases and related materials, such as "copyright law" or "digital media terms of use."

[0435] Generate answers

[0436] The server's AI engine generates appropriate answers based on the identified legal topic, references historical data and legal documents to build reliable answers, and then formats the generated answers in natural language. For example, a response might be, "Unauthorized use of images from the Internet may constitute copyright infringement."

[0437] Submit your answer

[0438] The generated answer is sent from the server to the user's device, where it is displayed. For example, the answer may read, "Unauthorized use of images may constitute copyright infringement. We recommend obtaining permission or using copyright-free images."

[0439] Actions to take in case of serious problems

[0440] If the user has additional questions or needs further advice, the server may offer a paid expert consultation option, where the user can obtain further detailed advice from the expert.

[0441] Specific examples

[0442] User Question: "Can I use your music in a YouTube video?"

[0443] Question analysis: "YouTube" "Music" "Use"

[0444] Identifying legal topics: "Copyright Law" and "YouTube Terms of Service"

[0445] Generate Answer: "Unauthorized use of music can be copyright infringement."

[0446] Answer provided: "Using music in your YouTube videos without permission may be copyright infringement. We recommend officially licensing or using royalty-free music."

[0447] By implementing this system, users can receive fast and accurate legal advice, minimizing uncertainty and risk, and can also seek expert advice if necessary.

[0448] The processing flow will be explained below.

[0449] Step 1:

[0450] A user enters a legal question into a terminal. For example, the user enters a question such as, "Can I use an image from the Internet on my blog?"

[0451] Step 2:

[0452] The terminal transmits the entered question to the server.

[0453] Step 3:

[0454] The server passes the received question to a natural language processing engine, which analyzes the question.

[0455] Step 4:

[0456] A natural language processing engine tokenizes the question and extracts phrases and keywords, such as "online images," "blogs," and "use."

[0457] Step 5:

[0458] The server then consults a legal database to identify relevant legal topics based on the extracted keywords, such as "copyright law" or "digital media terms of use."

[0459] Step 6:

[0460] The server's AI engine generates appropriate answers based on the identified legal topic, such as "Unauthorized use of images found online may constitute copyright infringement" by referencing historical data and legal documents.

[0461] Step 7:

[0462] The server formats the generated answer in natural language.

[0463] Step 8:

[0464] The server sends the formatted response to the user's terminal.

[0465] Step 9:

[0466] The device receives the response from the server and displays it to the user, for example, a message saying, "Unauthorized use of images may constitute copyright infringement. We recommend obtaining permission or using copyright-free images."

[0467] Step 10:

[0468] If the user has additional questions or needs more detailed assistance, the user enters further questions.

[0469] Step 11:

[0470] The server accepts and analyzes additional questions and suggests options for consultation with a paid expert, for example, by displaying "We recommend you consult with a paid expert" and providing a link to access the expert.

[0471] Example 1

[0472] 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."

[0473] In modern society, it is not easy to obtain fast and accurate advice when faced with legal questions or problems. In particular, the legal risks associated with using content on the Internet involve complex laws, making it difficult for the general public to obtain accurate information. Furthermore, under the current system, detailed legal advice requires direct consultation with an expert, which is a time-consuming and costly burden. To solve these issues, there is a need for a system that provides fast and accurate legal advice and allows access to expert advice when necessary.

[0474] 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.

[0475] In this invention, the server includes means for accepting legal questions from users, means for analyzing the accepted questions, means for generating appropriate answers based on the analyzed questions, means for providing the generated answers to the users, means for analyzing the accepted questions from users using a natural language processing engine, means for identifying legal topics based on the analyzed questions, and means for formatting the generated answers in natural language, thereby enabling the provision of prompt and accurate legal advice and, if necessary, the provision of detailed expert advice.

[0476] A "user" is an individual or company that enters a legal question into the system for the purpose of obtaining an answer.

[0477] A "terminal" is a device, such as a computer or smartphone, that a user uses to access the system and enter legal questions.

[0478] A "server" is a computer system that receives questions sent by users, analyzes them, and generates answers.

[0479] A "natural language processing engine" is a software technology that analyzes questions entered by users and extracts phrases and keywords.

[0480] "Tokenization" is the process by which a natural language processing engine breaks up input text and converts it into individual words and phrases.

[0481] A "legal topic" is a legal subject or issue area identified based on extracted keywords.

[0482] "Analysis" is the process of breaking down the received question and understanding its meaning.

[0483] "Answer generation" is the process of constructing an appropriate answer based on the parsed question.

[0484] "Natural language formatting" refers to converting the generated answers into text that is easy for humans to understand.

[0485] A "paid expert" is an individual or firm with a legal profession who is booked to provide in-depth legal advice.

[0486] The "Consultation Option" is an option to receive in-depth legal advice from a paid professional.

[0487] "Secure communication" is a communication method in which data is encrypted before transmission to prevent unauthorized access or tampering by third parties.

[0488] An "SQL query" is a standard query language used to retrieve specific information from a database.

[0489] A "generative AI model" is an artificial intelligence algorithm that is trained to generate appropriate answers to user input.

[0490] MODE FOR CARRYING OUT THE INVENTION

[0491] This invention is a system that provides users with fast and accurate answers to legal questions. The system is designed to enable users to quickly obtain advice when they encounter legal questions or problems.

[0492] System Overview

[0493] The system mainly consists of the following elements:

[0494] 1. A device for inputting questions from users

[0495] 2. A server that receives the input question, analyzes it, and generates an answer

[0496] 3. Natural language processing engine for question analysis and answer generation

[0497] 4. A means of providing generated answers to users

[0498] 5. Access to paid experts for in-depth advice

[0499] Specific Embodiments

[0500] User question input

[0501] Users enter legal questions from a device (such as a PC or smartphone). Specifically, the user enters a question such as, "Can I use an image I found online on my blog?" The device can be a web browser or a dedicated app.

[0502] Submit a Question

[0503] The device sends the entered question to the server, which receives it and proceeds to the next step of analysis. The HTTPS protocol is used to ensure secure communication when sending data.

[0504] Question Analysis

[0505] The server passes the received question to a natural language processing engine, which analyzes the question. The natural language processing engine tokenizes the question and extracts phrases and keywords. Specifically, the keywords extracted are "online images," "blog," and "use."

[0506] Identifying Legal Topics

[0507] The server uses the extracted keywords to identify relevant legal topics, including referencing legal databases and related materials, such as "copyright law" or "digital media terms of use."

[0508] Generate answers

[0509] The server's AI engine generates appropriate answers based on the identified legal topic. It references historical data and legal documents to build reliable answers. It then formats the generated answers in natural language. For example, it might generate an answer like, "Unauthorized use of images found online may be a copyright infringement."

[0510] Overview of the generative AI model and example prompts

[0511] The server generates answers using a generative AI model (e.g., GPT-4). For example, in response to the question, "Is it okay to use images from the Internet on my blog?", the answer would be, "Using images without permission may be a copyright infringement. We recommend obtaining permission or using copyright-free images."

[0512] Submit your answer

[0513] The generated answer is sent from the server to the user's device. The device receives the answer and displays it in a user-friendly format. For example, a message such as "Unauthorized use of images may constitute copyright infringement. We recommend obtaining permission or using copyright-free images" may be displayed.

[0514] Actions to take in case of serious problems

[0515] If the user has additional questions or needs further advice, the server may offer a paid expert consultation option, where the user can obtain further detailed advice from the expert.

[0516] By implementing this system, users can receive fast and accurate legal advice, minimizing uncertainty and risk, and can access expert advice when necessary.

[0517] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0518] Step 1:

[0519] The user inputs a legal question using a terminal. The terminal provides an interface for the user to input a string such as "Can I use an image from the Internet on my blog?" The input question is stored in an input field on the terminal.

[0520] Input: User's legal question text

[0521] Output: The question text entered

[0522] Step 2:

[0523] The terminal sends the entered question to the server. In actual operation, the request content is converted into JSON format and a POST request is sent to the server using the HTTPS protocol.

[0524] Input: Question text

[0525] Output: JSON request sent to the server

[0526] Step 3:

[0527] The server passes the received question to a natural language processing engine for analysis. Specifically, the question is tokenized and phrases and keywords are extracted. The server then provides the received text to an NLP (natural language processing) library to obtain keywords such as "online images," "blogs," and "use."

[0528] Input: Question text in JSON format

[0529] Output: Extracted keywords (e.g., "online images," "blogs," "uses")

[0530] Step 4:

[0531] The server then uses the extracted keywords to identify relevant legal topics, consulting pre-built legal databases and indexes. The server then issues search queries to the databases to identify topics such as "copyright law" and "digital media terms of use."

[0532] Input: Extracted keywords

[0533] Output: Identified legal topics

[0534] Step 5:

[0535] The server sends a prompt to the generative AI model based on the identified legal topic and generates an appropriate answer. For example, the AI ​​model receives a prompt such as "Is it okay to use an image from the Internet on my blog?" and receives the answer "Unauthorized use may constitute copyright infringement."

[0536] Input: Legal topic and prompt

[0537] Output: Generated answer text

[0538] Step 6:

[0539] The server converts the generated response into JSON format and returns it to the user's device. The server serializes the generated response into JSON format and sends the response to the device using the HTTPS protocol.

[0540] Input: Generated answer text

[0541] Output: JSON formatted response data

[0542] Step 7:

[0543] The device displays the response received from the server. Specifically, the device parses the JSON data and displays it on the screen in a user-friendly format. For example, it displays a message saying, "Unauthorized use of images may be a copyright infringement. We recommend obtaining permission or using copyright-free images."

[0544] Input: JSON formatted response data

[0545] Output: User-friendly formatted answer display

[0546] Step 8:

[0547] If the user has additional questions or needs further advice, the server offers the option of a paid expert consultation: after the user provides additional input, the server returns a link that takes the user to a paid consultation booking page.

[0548] Input: User's additional questions or requests for further assistance

[0549] Output: Paid consultation booking page link

[0550] (Application example 1)

[0551] 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."

[0552] In conventional virtual stores, when users had legal questions about products or services, there were limited ways to get appropriate and prompt answers. This often left users feeling uncertain and anxious, which could discourage them from making a purchase. Furthermore, when users needed specialized legal advice, there was no established way to quickly access an appropriate expert. This sometimes led to a poor user experience.

[0553] 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.

[0554] In this invention, the server includes means for accepting legal questions from users, means for analyzing the accepted questions, means for generating appropriate answers based on the analyzed questions, means for providing the generated answers to the users, means for accepting questions while using the virtual store, and means for quickly providing answers to legal questions in the virtual store, thereby enabling users to quickly resolve their legal questions in the virtual store and use products and services with peace of mind.

[0555] A "user" is someone who uses the system to ask a legal question.

[0556] "Legal Question" means a question that involves a question or issue relating to the law.

[0557] "Means for receiving" refers to the interface or process for receiving input from the user.

[0558] "Means of analysis" refers to the process of interpreting the questions received and extracting important keywords and context.

[0559] "Means for generating appropriate answers" refers to a process that automatically generates reliable answers based on the analyzed information.

[0560] "Means for providing" refers to the process or interface for presenting the generated answer to the user.

[0561] A "virtual store" refers to a shop or service provider operated on the Internet.

[0562] "Means for accepting questions (while using the virtual store)" refers to a mechanism for accepting questions at any time from users using the virtual store.

[0563] "Means for quickly providing answers to legal questions" refers to a system that efficiently carries out the process from entering a question to providing an answer.

[0564] The present invention provides a system for providing a prompt and appropriate answer when a user asks a legal question in a virtual store. Specific embodiments of the present invention will be described below.

[0565] System Overview

[0566] The system mainly consists of the following elements:

[0567] 1. A device for inputting questions from users

[0568] 2. A server that receives the input question, analyzes it, and generates an answer

[0569] 3. Natural language processing engine for analysis and answer generation

[0570] 4. A means of providing generated answers to users

[0571] 5. A means of answering questions while using the virtual store

[0572] 6. A fast way to get answers to your legal questions

[0573] Specific processing of the program

[0574] The server receives legal questions from the user's device. The questions are sent in JSON format and are received by the server. The received questions are then passed to a natural language processing engine, which uses Hugging Face's Transformers library.

[0575] The engine analyzes the question, extracts relevant keywords and context, and then consults legal documents and databases to generate an appropriate answer. This answer is formatted in natural language that is easy for the user to understand. The formatted answer is then returned from the server to the user's device and displayed to the user.

[0576] Hardware and software used

[0577] Hardware: Smartphones, servers

[0578] Software: Python, Flask, Transformers library

[0579] Data processing and calculation flow

[0580] The server first tokenizes the received question and extracts phrases and keywords. Based on the results of this analysis, it identifies relevant legal topics. Based on the identified topics, it references past data and legal documents to generate an appropriate answer. The generated answer is then formatted in natural language and sent to the user's device.

[0581] Specific examples

[0582] User Question: "Is it legal to share images of this product on social media?"

[0583] Question analysis: "Product", "Image", "Social Media", "Share", "Legal Issues"

[0584] Identifying legal topics: "Copyright Law" and "Social Media Terms of Use"

[0585] Generate Answer: "Sharing product images on social media without permission may be a copyright infringement."

[0586] Answer provided: "Sharing images without permission may be copyright infringement. We recommend getting permission or using copyright-free images."

[0587] Prompt Sentence Examples

[0588] Is it legal to share images of this product on social media?

[0589] In this way, by implementing the present invention, users can instantly resolve legal questions in the virtual store, allowing them to use products and services with peace of mind.

[0590] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0591] Step 1:

[0592] Users enter legal questions into the virtual storefront

[0593] Users use a device such as a smartphone to input legal questions into the virtual store's interface. The input questions are sent to the server via an API. The input data is in text format, and a question such as "Is it legal to share an image of this product on social media?" is sent.

[0594] Step 2:

[0595] The server receives the user's question.

[0596] The server first collects and stores data to process the received questions. Specifically, it prepares the received text data to be passed to an analysis engine (e.g., a Flask application). The input data is the question in text format, and the output data is the same question data in text format.

[0597] Step 3:

[0598] The server analyzes the question using a natural language processing engine

[0599] The server passes the received question to a natural language processing engine (for example, Hugging Face's Transformers library), which tokenizes (splits) the question and extracts keywords. For example, the keywords extracted are "SNS," "image," "share," and "legal issue." The input data is the question text, and the output data is a list of extracted keywords.

[0600] Step 4:

[0601] The server identifies legal topics based on the extracted keywords.

[0602] The server then uses the extracted keywords to refer to legal databases and materials to identify relevant legal topics. This process involves researching and identifying copyright laws, social networking site terms of use, etc. The input data is a list of keywords, and the output data is a list of identified legal topics.

[0603] Step 5:

[0604] The server generates answers based on legal topics

[0605] The server generates appropriate answers based on the identified legal topics. It uses a generative AI model to reference historical data and legal documents to build reliable answers. The input data is a list of legal topics, and the output data is the generated answer text. For example, a generated answer might be, "Sharing product images on social media without permission may be a copyright infringement."

[0606] Step 6:

[0607] The server formats the generated answer in natural language and provides it to the user.

[0608] The generated answer is formatted in natural language so that it is easy for the user to understand. The formatted answer is then sent back to the user's device from the server. The input data is the generated answer text, and the output data is the formatted answer text. For example, the answer may be provided in the form of "Unauthorized image sharing may be a copyright infringement. We recommend obtaining permission or using a copyright-free image."

[0609] Step 7:

[0610] If the user reviews the answers provided and asks additional questions, the server is prepared to provide more detailed advice.

[0611] If the user checks the provided answer and needs further advice, the server provides a paid expert consultation option. In this case, the user can obtain further detailed advice from the expert. The input data is the user's follow-up question, and the output data is the information provided by the consultation option.

[0612] 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.

[0613] This invention is a system that provides quick and accurate answers to legal questions from users. By incorporating an emotion engine that recognizes the user's emotions, this system achieves more personalized responses. The specific configuration of the system and the program processing are described below.

[0614] System Overview

[0615] The system mainly consists of the following elements:

[0616] 1. A device for inputting questions from users

[0617] 2. A server that receives the input question, analyzes it, and generates an answer

[0618] 3. Natural language processing engine for analysis and answer generation

[0619] 4. Emotion engine that analyzes user emotions

[0620] 5. How to provide generated answers to users

[0621] 6. Access to paid experts for in-depth advice

[0622] Program processing

[0623] User question input

[0624] Users enter legal questions from their devices (PCs, smartphones, etc.). For example, a user might enter a question like, "Can I use an image I found online on my blog?"

[0625] Submit a Question

[0626] The terminal sends the entered question to the server, which receives it and proceeds to the next step of the analysis process.

[0627] Question Analysis

[0628] The server passes the received question to a natural language processing engine, which analyzes the question. The natural language processing engine tokenizes the question and extracts phrases and keywords. For example, keywords such as "online images," "blog," and "use" are extracted.

[0629] Emotion Analysis

[0630] At the same time, the server passes the question to an emotion engine to analyze the user's emotions, which analyzes the emotional data contained in the text and identifies whether the user is anxious, angry, or confused.

[0631] Identifying Legal Topics

[0632] The server uses the extracted keywords and sentiment data to identify relevant legal topics, including referencing legal databases and related materials, such as "Copyright Law" and "Digital Media Terms of Use."

[0633] Generate answers

[0634] The server's AI engine generates appropriate answers based on the identified legal topic and emotional data. It references historical data and legal documents and adjusts the tone and content of the answer depending on the user's emotional state. For example, the answer might read, "Using images from the Internet without permission may constitute copyright infringement. If you are concerned, we recommend getting permission or using copyright-free images."

[0635] Submit your answer

[0636] The generated answer is sent from the server to the user's device, where it is displayed. For example, the answer may read, "Unauthorized use of images may constitute copyright infringement. We recommend obtaining permission or using copyright-free images."

[0637] Actions to take in case of serious problems

[0638] If the user has additional questions or needs more detailed advice, they can enter more questions. The server accepts and parses the additional questions and suggests options for consulting with a paid expert. For example, it might say, "We recommend you consult with a paid expert," and provide a link to access the expert.

[0639] Specific examples

[0640] User Question: "Can I use your music in a YouTube video?"

[0641] Question analysis: "YouTube" "Music" "Use"

[0642] Sentiment analysis: Identifying when a user may be feeling anxious

[0643] Identifying legal topics: "Copyright Law" and "YouTube Terms of Service"

[0644] Generate an answer: "Unauthorized use of music may constitute copyright infringement. If you are concerned, we recommend obtaining an official license or using royalty-free music."

[0645] Answer provided: "Using music in your YouTube videos without permission may be copyright infringement. We recommend officially licensing or using royalty-free music."

[0646] By implementing this system, users can not only receive fast and accurate legal advice, but also emotional support, and, if necessary, access to expert advice.

[0647] The processing flow will be explained below.

[0648] Step 1:

[0649] A user enters a legal question into a terminal. For example, the user enters a question such as, "Can I use an image from the Internet on my blog?"

[0650] Step 2:

[0651] The terminal transmits the entered question to the server.

[0652] Step 3:

[0653] The server passes the received question to a natural language processing engine, which analyzes the question. The natural language processing engine tokenizes the question and extracts phrases and keywords. For example, it extracts keywords such as "online images," "blog," and "use."

[0654] Step 4:

[0655] The server passes the parsed question to an emotion engine to analyze the user's emotions. The emotion engine analyzes the emotional data contained in the text and identifies whether the user is anxious, angry, or confused.

[0656] Step 5:

[0657] The server uses the extracted keywords and sentiment data to identify relevant legal topics, including referencing legal databases and related materials, such as "Copyright Law" and "Digital Media Terms of Use."

[0658] Step 6:

[0659] The server's AI engine generates appropriate answers based on the identified legal topic and emotional data. It references historical data and legal documents and adjusts the tone and content of the answer depending on the user's emotional state. For example, it might generate an answer like, "Using images from the Internet without permission may constitute copyright infringement. If you are concerned, we recommend getting permission or using copyright-free images."

[0660] Step 7:

[0661] The server formats the generated answer in natural language.

[0662] Step 8:

[0663] The server sends the formatted response to the user's terminal.

[0664] Step 9:

[0665] The device receives the response from the server and displays it to the user, for example, a message saying, "Unauthorized use of images may constitute copyright infringement. We recommend obtaining permission or using copyright-free images."

[0666] Step 10:

[0667] If the user has additional questions or needs more detailed assistance, the user enters further questions.

[0668] Step 11:

[0669] The server accepts and analyzes additional questions and suggests options for consultation with a paid expert, for example, by displaying "We recommend you consult with a paid expert" and providing a link to access the expert.

[0670] Example 2

[0671] 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."

[0672] Conventional legal advice systems provide standard answers without considering the user's feelings, which can result in users being unable to receive optimal advice. Furthermore, in cases of serious problems, detailed advice from an expert may be required, but the lack of such an option presents a challenge.

[0673] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for accepting legal questions from users, means for analyzing the questions using a natural language processing engine, means for identifying relevant legal topics, means for analyzing the user's emotions using an emotion engine, means for generating appropriate answers using a generative AI model, and means for providing the generated answers to the user. This enables more appropriate and personalized legal advice according to the user's emotional state. In addition, in cases of serious problems, a paid consultation option with a specialist can be provided.

[0674] The "means for accepting legal questions from users" refers to an interface that allows users to input legal questions using a device such as a PC or smartphone and send them to the system.

[0675] The "means of analyzing questions using a natural language processing engine" refers to a means of using natural language processing technology to tokenize received questions and extract phrases and keywords.

[0676] The "means for identifying relevant legal topics" refers to a means for finding appropriate legal topics by referring to legal databases and related materials based on the analyzed keywords.

[0677] The "means for analyzing user emotions using an emotion engine" refers to a means for analyzing text data of questions from users and using emotion analysis technology to identify whether the user is feeling emotions such as anxiety, anger, or confusion.

[0678] "Means for generating appropriate answers using a generative AI model" refers to means for using an artificial intelligence model to automatically generate appropriate legal advice based on relevant legal topics and sentiment data.

[0679] The "means for providing the generated answer to the user" refers to a means for transmitting the generated legal advice to the user's terminal and displaying it.

[0680] A "means for providing a fee-based expert consultation option" is a means for providing a user with the option to access a fee-based legal expert if the user requires more detailed advice.

[0681] MODE FOR CARRYING OUT THE INVENTION

[0682] This invention is a system that provides quick and accurate answers to legal questions from users. By incorporating an emotion engine that recognizes the user's emotions, this system achieves more personalized responses. The specific configuration of the system and the program processing are described below.

[0683] System Components

[0684] The system mainly consists of the following elements:

[0685] 1. A device for inputting questions from users

[0686] 2. A server that receives the input question, analyzes it, and generates an answer

[0687] 3. A natural language processing engine to analyze questions

[0688] 4. Emotion engine that analyzes user emotions

[0689] 5. How to provide generated answers to users

[0690] 6. Access to paid experts for in-depth advice

[0691] System Functions and Operation

[0692] A user inputs a legal question from a device (such as a PC or smartphone). For example, a user may input a question such as, "Can I use an image I found online on my blog?"

[0693] The device sends the entered question to the server. The server receives it and proceeds to the next step of analysis. First, the server passes the received question to a natural language processing engine (such as Hugging Face's Transformers) to analyze the question. The natural language processing engine tokenizes the question and extracts phrases and keywords. In this example, the keywords extracted are "online images," "blog," and "use."

[0694] At the same time, the server passes the question to an emotion engine (such as the Google Cloud Natural Language API) to analyze the user's emotions. The emotion engine analyzes the emotional data contained in the text and identifies whether the user is anxious, angry, or confused. For example, an emotion score of "Anxious: 0.75" is obtained.

[0695] The server uses the extracted keywords and sentiment data to identify relevant legal topics, including referencing legal databases and related materials, such as "Copyright Law" and "Digital Media Terms of Use."

[0696] Next, a generative AI model (such as OpenAI GPT-4) on the server generates an appropriate answer based on the identified legal topic and emotional data. The generative AI model refers to legal documents and past consultation cases and adjusts the tone and content of the answer depending on the user's emotional state. For example, a generated answer might read, "Using images from the Internet without permission may constitute copyright infringement. If you are concerned, we recommend obtaining permission or using copyright-free images."

[0697] The generated answer is sent from the server to the user's device. The device displays the answer, for example, a message saying, "Unauthorized use of images may constitute copyright infringement. We recommend obtaining permission or using copyright-free images."

[0698] If the user has additional questions or needs more detailed advice, they can enter their questions again. The server will accept the additional questions and perform the same analysis process. If the issue is deemed to be a serious legal problem, the server will offer the option of consulting a paid expert. For example, it may provide a message saying, "We recommend that you consult with a paid expert," along with a link to the expert.

[0699] Examples of prompt statements

[0700] Question: "Can I use your music in a YouTube video?"

[0701] Question analysis: Keywords "YouTube", "music", "usage"

[0702] Emotion analysis: Anxiety score 0.70

[0703] Legal topics: "Copyright Law" and "YouTube Terms of Service"

[0704] Generate an answer: "Using music without permission may be copyright infringement. We recommend obtaining an official license or using royalty-free music."

[0705] Answer provided: "Using music in your YouTube videos without permission may be copyright infringement. We recommend officially licensing or using royalty-free music."

[0706] This allows users to receive not only fast and accurate legal advice, but also emotional support, and the ability to seek professional advice if necessary.

[0707] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0708] System program processing flow

[0709] Step 1:

[0710] Users enter legal questions into their devices (PCs or smartphones). For example, a user might enter, "Can I use an image I found online on my blog?" This input becomes input data for the system.

[0711] Step 2:

[0712] The device sends the entered question to the server. The question content, which is input data, is securely transmitted using the HTTPS protocol. The device sends the question data in JSON format to the server's API endpoint as a POST request.

[0713] Step 3:

[0714] The server passes the received question data to a natural language processing engine. Specifically, the server tokenizes the received question and extracts phrases and keywords. For example, "online images," "blogs," and "use" are extracted. The input is the question data, and the output is the tokenized keywords.

[0715] Step 4:

[0716] At the same time, the server passes the question data to the emotion engine, which analyzes the user's emotions. The emotion engine analyzes the emotion data in the text and identifies whether the user is anxious, angry, or confused. For example, the emotion score obtained is "Anxiety: 0.75." The input is the question data, and the output is the emotion score.

[0717] Step 5:

[0718] The server identifies relevant legal topics based on tokenized keywords and sentiment scores obtained from the natural language processing engine and sentiment engine. Specifically, it queries a legal database to search for relevant information such as "copyright law" and "digital media terms of use." The input is keywords and sentiment scores, and the output is legal topics.

[0719] Step 6:

[0720] The server's generative AI model generates an appropriate answer based on the identified legal topic and sentiment score. The generative AI model references past data and legal documents to create an answer that corresponds to the emotional state. For example, it generates the answer, "Using images on the Internet without permission may constitute copyright infringement. If you are concerned, we recommend obtaining permission or using copyright-free images." The input is the legal topic and sentiment score, and the output is the generated answer.

[0721] Step 7:

[0722] The server sends the generated answer to the user's terminal. Before sending, the answer is converted to JSON format and sent securely to the terminal using the HTTPS protocol. The input is the generated answer, and the output is the answer displayed on the terminal.

[0723] Step 8:

[0724] The device displays the response received from the server to the user using JavaScript in the web browser or a mobile app UI framework (e.g., React or Flutter). The user can then decide what to do based on this.

[0725] Step 9:

[0726] If the user has additional questions or requires more detailed advice, they can enter the questions again. The server accepts the additional questions and runs the analysis process again. If the issue is deemed to be a serious legal problem, the server will suggest a paid consultation option with a specialist. For example, it may display a message saying, "We recommend that you consult with a paid specialist," and provide a link to access the specialist. The input is the additional question data, and the output is a suggestion of a paid consultation option.

[0727] (Application example 2)

[0728] 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."

[0729] In today's society, it is important for users to resolve legal questions quickly and accurately. However, conventional legal advice systems do not take into account the user's emotional state, which can potentially increase the user's anxiety and confusion. Furthermore, in certain situations, users often require additional, specialized legal advice. In such cases, the lack of means to quickly access legal experts presents a problem. Furthermore, in certain fields, such as electronic payment services, legal questions arise frequently and require special consideration for addressing them.

[0730] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0731] In this invention, the server includes means for accepting legal questions from users, means for analyzing the accepted questions, means for analyzing the user's emotions, means for generating appropriate answers based on the analyzed questions and emotion data, means for providing the generated answers to the users, and means for providing options for paid consultation with experts, thereby enabling the provision of prompt and accurate legal advice while taking into consideration the user's emotions, and enabling prompt access to experts as needed.

[0732] "Means for accepting legal questions from users" refers to a device or interface that allows the system to recognize and accept legal questions entered by users.

[0733] "Means for analyzing received questions" refers to the technology or process of using natural language processing technology to understand and analyze the meaning of input legal questions.

[0734] "Means for analyzing user emotions" refers to technology for recognizing and analyzing the user's emotional state (e.g., anxiety, joy, confusion, etc.) from text or voice input.

[0735] The "means for generating an appropriate answer based on the analyzed question and emotional data" is a system for automatically generating an optimal legal answer by taking into account the content of the analyzed question and the emotional state of the user.

[0736] The "means for providing the generated legal answer to the user" refers to an interface such as a display or an audio output device for presenting the generated legal answer to the user.

[0737] "Means for providing a fee-based expert consultation option" refers to means or options that allow a user to contact an expert for a fee if they require more detailed advice.

[0738] A "natural language processing engine" is a technology and software that enables computers to understand and analyze human language.

[0739] An "emotion engine" is a technology or software that analyzes emotions from a user's text or voice and outputs the results.

[0740] The following describes an embodiment of the present invention. The system accepts legal questions from users, analyzes them, and provides legal advice that takes into account their emotional state. It also provides paid expert consultations if necessary.

[0741] Hardware and software used

[0742] Device: Smartphone or tablet (iOS or Android)

[0743] Natural Language Processing Engine: Google NLU API or IBM Watson NLP

[0744] Sentiment analysis engine: Microsoft Azure Text Analytics

[0745] Server and answer generation engine: OpenAI GPT-4 model

[0746] Legal Database: A subscribed online legal resource library

[0747] System Programming and Processing

[0748] User question input and reception

[0749] A user launches an electronic payment app on their smartphone or tablet and enters a legal question, which is then sent from the device to the server.

[0750] Question analysis and sentiment analysis

[0751] The server passes the received question to a natural language processing engine (Google NLU API or IBM Watson NLP), which tokenizes the question and extracts keywords.

[0752] At the same time, the server passes the question to a sentiment analysis engine (Microsoft Azure Text Analytics) to analyze the sentiment data contained in the text.

[0753] Generate and provide answers

[0754] The server's AI engine (OpenAI GPT-4 model) generates appropriate legal answers based on the analyzed questions and sentiment data.

[0755] The generated answer is then sent back from the server to the device and provided to the user, who can then view the legal advice on the screen of their smartphone or tablet.

[0756] What to do in case of serious problems

[0757] If the user has additional questions or needs further advice, the server offers a paid expert consultation option, which allows the user to quickly connect with an expert and receive further advice to resolve the issue.

[0758] Specific use cases

[0759] User Questions and Analysis Examples

[0760] For example: "What do I do if I accidentally send money through PayPal?"

[0761] Question analysis: The keywords extracted were "PayPal," "incorrect remittance," and "how to deal with it."

[0762] Emotion analysis: Analyzes whether you are feeling anxious.

[0763] Example of generating and providing answers

[0764] Generated answer: "If you made a mistake with PayPal, select 'Cancel' from the transaction details page. If the problem persists, contact PayPal support."

[0765] Prompt Sentence Examples

[0766] "I accidentally sent money via PayPal. How do I cancel it?"

[0767] By implementing this system, users can receive prompt, accurate, and sensitive legal advice, with quick access to experts when needed.

[0768] In this way, a specific system for efficiently resolving legal issues in electronic payment services can be provided.

[0769] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0770] Step 1:

[0771] A user launches an electronic payment app on their smartphone or tablet and enters a legal question. The question is specific, such as "What should I do if I accidentally send money via PayPal?" After entering the question, the device sends it to the server. The entered data arrives at the server in text format.

[0772] Step 2:

[0773] The server passes the received question to a natural language processing engine (Google NLU API or IBM Watson NLP), where the question is tokenized and keywords are extracted. For example, keywords such as "PayPal," "incorrect transfer," and "how to resolve" are extracted. This makes it easier to understand the structure of the question.

[0774] Step 3:

[0775] At the same time, the server passes the question to a sentiment analysis engine (Microsoft Azure Text Analytics) to analyze the user's emotional state. Emotional data such as anxiety or confusion is extracted from the input text, and a judgment is made that the user is feeling anxious. This emotional data is taken into account when generating an answer.

[0776] Step 4:

[0777] The server identifies relevant legal topics based on the analyzed question data (keywords) and sentiment data. It references legal databases to collect information related to topics such as "electronic payment laws" and "consumer protection laws." This allows it to identify the appropriate legal basis for the question.

[0778] Step 5:

[0779] The server's AI engine (OpenAI GPT-4 model) generates appropriate answers based on the identified legal topic and sentiment data. For example, it generates specific advice such as, "To cancel an erroneous transfer on PayPal, select 'Cancel' from the transaction details page. If the issue persists, contact PayPal support." This generation process combines input data with legal resources to process the data.

[0780] Step 6:

[0781] The generated answer is sent from the server to the device. The user's smartphone or tablet receives the answer and displays it on the screen. The user can see specific advice such as, "If you made an incorrect transfer with PayPal, select 'Cancel' from the transaction details page. If the problem persists, contact PayPal support."

[0782] Step 7:

[0783] If the user requires further advice, the terminal sends an additional question to the server. At this time, the server provides an option to consult with a paid expert. For example, the server may provide a link to access the expert along with a message saying, "If you require further advice, we recommend that you consult with a paid expert." The user can quickly access the expert through this link and receive further detailed legal advice.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] [Third embodiment]

[0788] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0789] 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.

[0790] 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).

[0791] 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.

[0792] 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.

[0793] 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).

[0794] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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."

[0800] This invention is a system that provides users with fast and accurate answers to legal questions. The system is designed to enable users to quickly obtain advice when they encounter legal questions or problems.

[0801] System Overview

[0802] The system mainly consists of the following elements:

[0803] 1. A device for inputting questions from users

[0804] 2. A server that receives the input question, analyzes it, and generates an answer

[0805] 3. Natural language processing engine for analysis and answer generation

[0806] 4. A means of providing generated answers to users

[0807] 5. Access to paid experts for in-depth advice

[0808] Program processing

[0809] User question input

[0810] Users enter legal questions from their devices (PCs, smartphones, etc.). For example, a user might enter a question like, "Can I use an image I found online on my blog?"

[0811] Submit a Question

[0812] The terminal sends the entered question to the server, which receives it and proceeds to the next step of the analysis process.

[0813] Question Analysis

[0814] The server passes the received question to a natural language processing engine, which analyzes the question. The natural language processing engine tokenizes the question and extracts phrases and keywords. For example, keywords such as "online images," "blog," and "use" are extracted.

[0815] Identifying Legal Topics

[0816] The server uses the extracted keywords to identify relevant legal topics, including referencing legal databases and related materials, such as "copyright law" or "digital media terms of use."

[0817] Generate answers

[0818] The server's AI engine generates appropriate answers based on the identified legal topic, references historical data and legal documents to build reliable answers, and then formats the generated answers in natural language. For example, a response might be, "Unauthorized use of images from the Internet may constitute copyright infringement."

[0819] Submit your answer

[0820] The generated answer is sent from the server to the user's device, where it is displayed. For example, the answer may read, "Unauthorized use of images may constitute copyright infringement. We recommend obtaining permission or using copyright-free images."

[0821] Actions to take in case of serious problems

[0822] If the user has additional questions or needs further advice, the server may offer a paid expert consultation option, where the user can obtain further detailed advice from the expert.

[0823] Specific examples

[0824] User Question: "Can I use your music in a YouTube video?"

[0825] Question analysis: "YouTube" "Music" "Use"

[0826] Identifying legal topics: "Copyright Law" and "YouTube Terms of Service"

[0827] Generate Answer: "Unauthorized use of music can be copyright infringement."

[0828] Answer provided: "Using music in your YouTube videos without permission may be copyright infringement. We recommend officially licensing or using royalty-free music."

[0829] By implementing this system, users can receive fast and accurate legal advice, minimizing uncertainty and risk, and can also seek expert advice if necessary.

[0830] The processing flow will be explained below.

[0831] Step 1:

[0832] A user enters a legal question into a terminal. For example, the user enters a question such as, "Can I use an image from the Internet on my blog?"

[0833] Step 2:

[0834] The terminal transmits the entered question to the server.

[0835] Step 3:

[0836] The server passes the received question to a natural language processing engine, which analyzes the question.

[0837] Step 4:

[0838] A natural language processing engine tokenizes the question and extracts phrases and keywords, such as "online images," "blogs," and "use."

[0839] Step 5:

[0840] The server then consults a legal database to identify relevant legal topics based on the extracted keywords, such as "copyright law" or "digital media terms of use."

[0841] Step 6:

[0842] The server's AI engine generates appropriate answers based on the identified legal topic, such as "Unauthorized use of images found online may constitute copyright infringement" by referencing historical data and legal documents.

[0843] Step 7:

[0844] The server formats the generated answer in natural language.

[0845] Step 8:

[0846] The server sends the formatted response to the user's terminal.

[0847] Step 9:

[0848] The device receives the response from the server and displays it to the user, for example, a message saying, "Unauthorized use of images may constitute copyright infringement. We recommend obtaining permission or using copyright-free images."

[0849] Step 10:

[0850] If the user has additional questions or needs more detailed assistance, the user enters further questions.

[0851] Step 11:

[0852] The server accepts and analyzes additional questions and suggests options for consultation with a paid expert, for example, by displaying "We recommend you consult with a paid expert" and providing a link to access the expert.

[0853] Example 1

[0854] 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."

[0855] In modern society, it is not easy to obtain fast and accurate advice when faced with legal questions or problems. In particular, the legal risks associated with using content on the Internet involve complex laws, making it difficult for the general public to obtain accurate information. Furthermore, under the current system, detailed legal advice requires direct consultation with an expert, which is a time-consuming and costly burden. To solve these issues, there is a need for a system that provides fast and accurate legal advice and allows access to expert advice when necessary.

[0856] 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.

[0857] In this invention, the server includes means for accepting legal questions from users, means for analyzing the accepted questions, means for generating appropriate answers based on the analyzed questions, means for providing the generated answers to the users, means for analyzing the accepted questions from users using a natural language processing engine, means for identifying legal topics based on the analyzed questions, and means for formatting the generated answers in natural language, thereby enabling the provision of prompt and accurate legal advice and, if necessary, the provision of detailed expert advice.

[0858] A "user" is an individual or company that enters a legal question into the system for the purpose of obtaining an answer.

[0859] A "terminal" is a device, such as a computer or smartphone, that a user uses to access the system and enter legal questions.

[0860] A "server" is a computer system that receives questions sent by users, analyzes them, and generates answers.

[0861] A "natural language processing engine" is a software technology that analyzes questions entered by users and extracts phrases and keywords.

[0862] "Tokenization" is the process by which a natural language processing engine breaks up input text and converts it into individual words and phrases.

[0863] A "legal topic" is a legal subject or issue area identified based on extracted keywords.

[0864] "Analysis" is the process of breaking down the received question and understanding its meaning.

[0865] "Answer generation" is the process of constructing an appropriate answer based on the parsed question.

[0866] "Natural language formatting" refers to converting the generated answers into text that is easy for humans to understand.

[0867] A "paid expert" is an individual or firm with a legal profession who is booked to provide in-depth legal advice.

[0868] The "Consultation Option" is an option to receive in-depth legal advice from a paid professional.

[0869] "Secure communication" is a communication method in which data is encrypted before transmission to prevent unauthorized access or tampering by third parties.

[0870] An "SQL query" is a standard query language used to retrieve specific information from a database.

[0871] A "generative AI model" is an artificial intelligence algorithm that is trained to generate appropriate answers to user input.

[0872] MODE FOR CARRYING OUT THE INVENTION

[0873] This invention is a system that provides users with fast and accurate answers to legal questions. The system is designed to enable users to quickly obtain advice when they encounter legal questions or problems.

[0874] System Overview

[0875] The system mainly consists of the following elements:

[0876] 1. A device for inputting questions from users

[0877] 2. A server that receives the input question, analyzes it, and generates an answer

[0878] 3. Natural language processing engine for question analysis and answer generation

[0879] 4. A means of providing generated answers to users

[0880] 5. Access to paid experts for in-depth advice

[0881] Specific Embodiments

[0882] User question input

[0883] Users enter legal questions from a device (such as a PC or smartphone). Specifically, the user enters a question such as, "Can I use an image I found online on my blog?" The device can be a web browser or a dedicated app.

[0884] Submit a Question

[0885] The device sends the entered question to the server, which receives it and proceeds to the next step of analysis. The HTTPS protocol is used to ensure secure communication when sending data.

[0886] Question Analysis

[0887] The server passes the received question to a natural language processing engine, which analyzes the question. The natural language processing engine tokenizes the question and extracts phrases and keywords. Specifically, the keywords extracted are "online images," "blogs," and "use."

[0888] Identifying Legal Topics

[0889] The server uses the extracted keywords to identify relevant legal topics, including referencing legal databases and related materials, such as "copyright law" or "digital media terms of use."

[0890] Generate answers

[0891] The server's AI engine generates appropriate answers based on the identified legal topic. It references historical data and legal documents to build reliable answers. It then formats the generated answers in natural language. For example, it might generate an answer like, "Unauthorized use of images found online may be a copyright infringement."

[0892] Overview of the generative AI model and example prompts

[0893] The server generates answers using a generative AI model (e.g., GPT-4). For example, in response to the question, "Is it okay to use images from the Internet on my blog?", the answer would be, "Using images without permission may be a copyright infringement. We recommend obtaining permission or using copyright-free images."

[0894] Submit your answer

[0895] The generated answer is sent from the server to the user's device. The device receives the answer and displays it in a user-friendly format. For example, a message such as "Unauthorized use of images may constitute copyright infringement. We recommend obtaining permission or using copyright-free images" may be displayed.

[0896] Actions to take in case of serious problems

[0897] If the user has additional questions or needs further advice, the server may offer a paid expert consultation option, where the user can obtain further detailed advice from the expert.

[0898] By implementing this system, users can receive fast and accurate legal advice, minimizing uncertainty and risk, and can access expert advice when necessary.

[0899] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0900] Step 1:

[0901] The user inputs a legal question using a terminal. The terminal provides an interface for the user to input a string such as "Can I use an image from the Internet on my blog?" The input question is stored in an input field on the terminal.

[0902] Input: User's legal question text

[0903] Output: The question text entered

[0904] Step 2:

[0905] The terminal sends the entered question to the server. In actual operation, the request content is converted into JSON format and a POST request is sent to the server using the HTTPS protocol.

[0906] Input: Question text

[0907] Output: JSON request sent to the server

[0908] Step 3:

[0909] The server passes the received question to a natural language processing engine for analysis. Specifically, the question is tokenized and phrases and keywords are extracted. The server then provides the received text to an NLP (natural language processing) library to obtain keywords such as "online images," "blogs," and "use."

[0910] Input: Question text in JSON format

[0911] Output: Extracted keywords (e.g., "online images," "blogs," "uses")

[0912] Step 4:

[0913] The server then uses the extracted keywords to identify relevant legal topics, consulting pre-built legal databases and indexes. The server then issues search queries to the databases to identify topics such as "copyright law" and "digital media terms of use."

[0914] Input: Extracted keywords

[0915] Output: Identified legal topics

[0916] Step 5:

[0917] The server sends a prompt to the generative AI model based on the identified legal topic and generates an appropriate answer. For example, the AI ​​model receives a prompt such as "Is it okay to use an image from the Internet on my blog?" and receives the answer "Unauthorized use may constitute copyright infringement."

[0918] Input: Legal topic and prompt

[0919] Output: Generated answer text

[0920] Step 6:

[0921] The server converts the generated response into JSON format and returns it to the user's device. The server serializes the generated response into JSON format and sends the response to the device using the HTTPS protocol.

[0922] Input: Generated answer text

[0923] Output: JSON formatted response data

[0924] Step 7:

[0925] The device displays the response received from the server. Specifically, the device parses the JSON data and displays it on the screen in a user-friendly format. For example, it displays a message saying, "Unauthorized use of images may be a copyright infringement. We recommend obtaining permission or using copyright-free images."

[0926] Input: JSON formatted response data

[0927] Output: User-friendly formatted answer display

[0928] Step 8:

[0929] If the user has additional questions or needs further advice, the server offers the option of a paid expert consultation: after the user provides additional input, the server returns a link that takes the user to a paid consultation booking page.

[0930] Input: User's additional questions or requests for further assistance

[0931] Output: Paid consultation booking page link

[0932] (Application example 1)

[0933] 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."

[0934] In conventional virtual stores, when users had legal questions about products or services, there were limited ways to get appropriate and prompt answers. This often left users feeling uncertain and anxious, which could discourage them from making a purchase. Furthermore, when users needed specialized legal advice, there was no established way to quickly access an appropriate expert. This sometimes led to a poor user experience.

[0935] 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.

[0936] In this invention, the server includes means for accepting legal questions from users, means for analyzing the accepted questions, means for generating appropriate answers based on the analyzed questions, means for providing the generated answers to the users, means for accepting questions while using the virtual store, and means for quickly providing answers to legal questions in the virtual store, thereby enabling users to quickly resolve their legal questions in the virtual store and use products and services with peace of mind.

[0937] A "user" is someone who uses the system to ask a legal question.

[0938] "Legal Question" means a question that involves a question or issue relating to the law.

[0939] "Means for receiving" refers to the interface or process for receiving input from the user.

[0940] "Means of analysis" refers to the process of interpreting the questions received and extracting important keywords and context.

[0941] "Means for generating appropriate answers" refers to a process that automatically generates reliable answers based on the analyzed information.

[0942] "Means for providing" refers to the process or interface for presenting the generated answer to the user.

[0943] A "virtual store" refers to a shop or service provider operated on the Internet.

[0944] "Means for accepting questions (while using the virtual store)" refers to a mechanism for accepting questions at any time from users using the virtual store.

[0945] "Means for quickly providing answers to legal questions" refers to a system that efficiently carries out the process from entering a question to providing an answer.

[0946] The present invention provides a system for providing a prompt and appropriate answer when a user asks a legal question in a virtual store. Specific embodiments of the present invention will be described below.

[0947] System Overview

[0948] The system mainly consists of the following elements:

[0949] 1. A device for inputting questions from users

[0950] 2. A server that receives the input question, analyzes it, and generates an answer

[0951] 3. Natural language processing engine for analysis and answer generation

[0952] 4. A means of providing generated answers to users

[0953] 5. A means of answering questions while using the virtual store

[0954] 6. A fast way to get answers to your legal questions

[0955] Specific processing of the program

[0956] The server receives legal questions from the user's device. The questions are sent in JSON format and are received by the server. The received questions are then passed to a natural language processing engine, which uses Hugging Face's Transformers library.

[0957] The engine analyzes the question, extracts relevant keywords and context, and then consults legal documents and databases to generate an appropriate answer. This answer is formatted in natural language that is easy for the user to understand. The formatted answer is then returned from the server to the user's device and displayed to the user.

[0958] Hardware and software used

[0959] Hardware: Smartphones, servers

[0960] Software: Python, Flask, Transformers library

[0961] Data processing and calculation flow

[0962] The server first tokenizes the received question and extracts phrases and keywords. Based on the results of this analysis, it identifies relevant legal topics. Based on the identified topics, it references past data and legal documents to generate an appropriate answer. The generated answer is then formatted in natural language and sent to the user's device.

[0963] Specific examples

[0964] User Question: "Is it legal to share images of this product on social media?"

[0965] Question analysis: "Product", "Image", "Social Media", "Share", "Legal Issues"

[0966] Identifying legal topics: "Copyright Law" and "Social Media Terms of Use"

[0967] Generate Answer: "Sharing product images on social media without permission may be a copyright infringement."

[0968] Answer provided: "Sharing images without permission may be copyright infringement. We recommend getting permission or using copyright-free images."

[0969] Prompt Sentence Examples

[0970] Is it legal to share images of this product on social media?

[0971] In this way, by implementing the present invention, users can instantly resolve legal questions in the virtual store, allowing them to use products and services with peace of mind.

[0972] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0973] Step 1:

[0974] Users enter legal questions into the virtual storefront

[0975] Users use a device such as a smartphone to input legal questions into the virtual store's interface. The input questions are sent to the server via an API. The input data is in text format, and a question such as "Is it legal to share an image of this product on social media?" is sent.

[0976] Step 2:

[0977] The server receives the user's question.

[0978] The server first collects and stores data to process the received questions. Specifically, it prepares the received text data to be passed to an analysis engine (e.g., a Flask application). The input data is the question in text format, and the output data is the same question data in text format.

[0979] Step 3:

[0980] The server analyzes the question using a natural language processing engine

[0981] The server passes the received question to a natural language processing engine (for example, Hugging Face's Transformers library), which tokenizes (splits) the question and extracts keywords. For example, the keywords extracted are "SNS," "image," "share," and "legal issue." The input data is the question text, and the output data is a list of extracted keywords.

[0982] Step 4:

[0983] The server identifies legal topics based on the extracted keywords.

[0984] The server then uses the extracted keywords to refer to legal databases and materials to identify relevant legal topics. This process involves researching and identifying copyright laws, social networking site terms of use, etc. The input data is a list of keywords, and the output data is a list of identified legal topics.

[0985] Step 5:

[0986] The server generates answers based on legal topics

[0987] The server generates appropriate answers based on the identified legal topics. It uses a generative AI model to reference historical data and legal documents to build reliable answers. The input data is a list of legal topics, and the output data is the generated answer text. For example, a generated answer might be, "Sharing product images on social media without permission may be a copyright infringement."

[0988] Step 6:

[0989] The server formats the generated answer in natural language and provides it to the user.

[0990] The generated answer is formatted in natural language so that it is easy for the user to understand. The formatted answer is then sent back to the user's device from the server. The input data is the generated answer text, and the output data is the formatted answer text. For example, the answer may be provided in the form of "Unauthorized image sharing may be a copyright infringement. We recommend obtaining permission or using a copyright-free image."

[0991] Step 7:

[0992] If the user reviews the answers provided and asks additional questions, the server is prepared to provide more detailed advice.

[0993] If the user checks the provided answer and needs further advice, the server provides a paid expert consultation option. In this case, the user can obtain further detailed advice from the expert. The input data is the user's follow-up question, and the output data is the information provided by the consultation option.

[0994] 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.

[0995] This invention is a system that provides quick and accurate answers to legal questions from users. By incorporating an emotion engine that recognizes the user's emotions, this system achieves more personalized responses. The specific configuration of the system and the program processing are described below.

[0996] System Overview

[0997] The system mainly consists of the following elements:

[0998] 1. A device for inputting questions from users

[0999] 2. A server that receives the input question, analyzes it, and generates an answer

[1000] 3. Natural language processing engine for analysis and answer generation

[1001] 4. Emotion engine that analyzes user emotions

[1002] 5. How to provide generated answers to users

[1003] 6. Access to paid experts for in-depth advice

[1004] Program processing

[1005] User question input

[1006] Users enter legal questions from their devices (PCs, smartphones, etc.). For example, a user might enter a question like, "Can I use an image I found online on my blog?"

[1007] Submit a Question

[1008] The terminal sends the entered question to the server, which receives it and proceeds to the next step of the analysis process.

[1009] Question Analysis

[1010] The server passes the received question to a natural language processing engine, which analyzes the question. The natural language processing engine tokenizes the question and extracts phrases and keywords. For example, keywords such as "online images," "blog," and "use" are extracted.

[1011] Emotion Analysis

[1012] At the same time, the server passes the question to an emotion engine to analyze the user's emotions, which analyzes the emotional data contained in the text and identifies whether the user is anxious, angry, or confused.

[1013] Identifying Legal Topics

[1014] The server uses the extracted keywords and sentiment data to identify relevant legal topics, including referencing legal databases and related materials, such as "Copyright Law" and "Digital Media Terms of Use."

[1015] Generate answers

[1016] The server's AI engine generates appropriate answers based on the identified legal topic and emotional data. It references historical data and legal documents and adjusts the tone and content of the answer depending on the user's emotional state. For example, the answer might read, "Using images from the Internet without permission may constitute copyright infringement. If you are concerned, we recommend getting permission or using copyright-free images."

[1017] Submit your answer

[1018] The generated answer is sent from the server to the user's device, where it is displayed. For example, the answer may read, "Unauthorized use of images may constitute copyright infringement. We recommend obtaining permission or using copyright-free images."

[1019] Actions to take in case of serious problems

[1020] If the user has additional questions or needs more detailed advice, they can enter more questions. The server accepts and parses the additional questions and suggests options for consulting with a paid expert. For example, it might say, "We recommend you consult with a paid expert," and provide a link to access the expert.

[1021] Specific examples

[1022] User Question: "Can I use your music in a YouTube video?"

[1023] Question analysis: "YouTube" "Music" "Use"

[1024] Sentiment analysis: Identifying when a user may be feeling anxious

[1025] Identifying legal topics: "Copyright Law" and "YouTube Terms of Service"

[1026] Generate an answer: "Unauthorized use of music may constitute copyright infringement. If you are concerned, we recommend obtaining an official license or using royalty-free music."

[1027] Answer provided: "Using music in your YouTube videos without permission may be copyright infringement. We recommend officially licensing or using royalty-free music."

[1028] By implementing this system, users can not only receive fast and accurate legal advice, but also emotional support, and, if necessary, the option to seek professional advice.

[1029] The processing flow will be explained below.

[1030] Step 1:

[1031] A user enters a legal question into a terminal. For example, the user enters a question such as, "Can I use an image from the Internet on my blog?"

[1032] Step 2:

[1033] The terminal transmits the entered question to the server.

[1034] Step 3:

[1035] The server passes the received question to a natural language processing engine, which analyzes the question. The natural language processing engine tokenizes the question and extracts phrases and keywords. For example, it extracts keywords such as "online images," "blog," and "use."

[1036] Step 4:

[1037] The server passes the parsed question to an emotion engine to analyze the user's emotions. The emotion engine analyzes the emotional data contained in the text and identifies whether the user is anxious, angry, or confused.

[1038] Step 5:

[1039] The server uses the extracted keywords and sentiment data to identify relevant legal topics, including referencing legal databases and related materials, such as "Copyright Law" and "Digital Media Terms of Use."

[1040] Step 6:

[1041] The server's AI engine generates appropriate answers based on the identified legal topic and emotional data. It references historical data and legal documents and adjusts the tone and content of the answer depending on the user's emotional state. For example, it might generate an answer like, "Using images from the Internet without permission may constitute copyright infringement. If you are concerned, we recommend getting permission or using copyright-free images."

[1042] Step 7:

[1043] The server formats the generated answer in natural language.

[1044] Step 8:

[1045] The server sends the formatted response to the user's terminal.

[1046] Step 9:

[1047] The device receives the response from the server and displays it to the user, for example, a message saying, "Unauthorized use of images may constitute copyright infringement. We recommend obtaining permission or using copyright-free images."

[1048] Step 10:

[1049] If the user has additional questions or needs more detailed assistance, the user enters further questions.

[1050] Step 11:

[1051] The server accepts and analyzes additional questions and suggests options for consultation with a paid expert, for example, by displaying "We recommend you consult with a paid expert" and providing a link to access the expert.

[1052] Example 2

[1053] 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."

[1054] Conventional legal advice systems provide standard answers without considering the user's feelings, which can result in users being unable to receive optimal advice. Furthermore, in cases of serious problems, detailed advice from an expert may be required, but the lack of such an option presents a challenge.

[1055] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for accepting legal questions from users, means for analyzing the questions using a natural language processing engine, means for identifying relevant legal topics, means for analyzing the user's emotions using an emotion engine, means for generating appropriate answers using a generative AI model, and means for providing the generated answers to the user. This enables more appropriate and personalized legal advice according to the user's emotional state. In addition, in cases of serious problems, a paid consultation option with a specialist can be provided.

[1056] The "means for accepting legal questions from users" refers to an interface that allows users to input legal questions using a device such as a PC or smartphone and send them to the system.

[1057] The "means of analyzing questions using a natural language processing engine" refers to a means of using natural language processing technology to tokenize received questions and extract phrases and keywords.

[1058] The "means for identifying relevant legal topics" refers to a means for finding appropriate legal topics by referring to legal databases and related materials based on the analyzed keywords.

[1059] The "means for analyzing user emotions using an emotion engine" refers to a means for analyzing text data of questions from users and using emotion analysis technology to identify whether the user is feeling emotions such as anxiety, anger, or confusion.

[1060] "Means for generating appropriate answers using a generative AI model" refers to means for using an artificial intelligence model to automatically generate appropriate legal advice based on relevant legal topics and sentiment data.

[1061] The "means for providing the generated answer to the user" refers to a means for transmitting the generated legal advice to the user's terminal and displaying it.

[1062] A "means for providing a fee-based expert consultation option" is a means for providing a user with the option to access a fee-based legal expert if the user requires more detailed advice.

[1063] MODE FOR CARRYING OUT THE INVENTION

[1064] This invention is a system that provides quick and accurate answers to legal questions from users. By incorporating an emotion engine that recognizes the user's emotions, this system achieves more personalized responses. The specific configuration of the system and the program processing are described below.

[1065] System Components

[1066] The system mainly consists of the following elements:

[1067] 1. A device for inputting questions from users

[1068] 2. A server that receives the input question, analyzes it, and generates an answer

[1069] 3. A natural language processing engine to analyze questions

[1070] 4. Emotion engine that analyzes user emotions

[1071] 5. How to provide generated answers to users

[1072] 6. Access to paid experts for in-depth advice

[1073] System Functions and Operation

[1074] A user inputs a legal question from a device (such as a PC or smartphone). For example, a user may input a question such as, "Can I use an image I found online on my blog?"

[1075] The device sends the entered question to the server. The server receives it and proceeds to the next step of analysis. First, the server passes the received question to a natural language processing engine (such as Hugging Face's Transformers) to analyze the question. The natural language processing engine tokenizes the question and extracts phrases and keywords. In this example, the keywords extracted are "online images," "blog," and "use."

[1076] At the same time, the server passes the question to an emotion engine (such as the Google Cloud Natural Language API) to analyze the user's emotions. The emotion engine analyzes the emotional data contained in the text and identifies whether the user is anxious, angry, or confused. For example, an emotion score of "Anxious: 0.75" is obtained.

[1077] The server uses the extracted keywords and sentiment data to identify relevant legal topics, including referencing legal databases and related materials, such as "Copyright Law" and "Digital Media Terms of Use."

[1078] Next, a generative AI model (such as OpenAI GPT-4) on the server generates an appropriate answer based on the identified legal topic and emotional data. The generative AI model refers to legal documents and past consultation cases and adjusts the tone and content of the answer depending on the user's emotional state. For example, a generated answer might read, "Using images from the Internet without permission may constitute copyright infringement. If you are concerned, we recommend obtaining permission or using copyright-free images."

[1079] The generated answer is sent from the server to the user's device. The device displays the answer, for example, a message saying, "Unauthorized use of images may constitute copyright infringement. We recommend obtaining permission or using copyright-free images."

[1080] If the user has additional questions or needs more detailed advice, they can enter their questions again. The server will accept the additional questions and perform the same analysis process. If the issue is deemed to be a serious legal problem, the server will offer the option of consulting a paid expert. For example, it may provide a message saying, "We recommend that you consult with a paid expert," along with a link to the expert.

[1081] Examples of prompt statements

[1082] Question: "Can I use your music in a YouTube video?"

[1083] Question analysis: Keywords "YouTube", "music", "usage"

[1084] Emotion analysis: Anxiety score 0.70

[1085] Legal topics: "Copyright Law" and "YouTube Terms of Service"

[1086] Generate an answer: "Using music without permission may be copyright infringement. We recommend obtaining an official license or using royalty-free music."

[1087] Answer provided: "Using music in your YouTube videos without permission may be copyright infringement. We recommend officially licensing or using royalty-free music."

[1088] This allows users to receive not only fast and accurate legal advice, but also emotional support, and the ability to seek professional advice if necessary.

[1089] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1090] System program processing flow

[1091] Step 1:

[1092] Users enter legal questions into their devices (PCs or smartphones). For example, a user might enter, "Can I use an image I found online on my blog?" This input becomes input data for the system.

[1093] Step 2:

[1094] The device sends the entered question to the server. The question content, which is input data, is securely transmitted using the HTTPS protocol. The device sends the question data in JSON format to the server's API endpoint as a POST request.

[1095] Step 3:

[1096] The server passes the received question data to a natural language processing engine. Specifically, the server tokenizes the received question and extracts phrases and keywords. For example, "online images," "blogs," and "use" are extracted. The input is the question data, and the output is the tokenized keywords.

[1097] Step 4:

[1098] At the same time, the server passes the question data to the emotion engine, which analyzes the user's emotions. The emotion engine analyzes the emotion data in the text and identifies whether the user is anxious, angry, or confused. For example, the emotion score obtained is "Anxiety: 0.75." The input is the question data, and the output is the emotion score.

[1099] Step 5:

[1100] The server identifies relevant legal topics based on tokenized keywords and sentiment scores obtained from the natural language processing engine and sentiment engine. Specifically, it queries a legal database to search for relevant information such as "copyright law" and "digital media terms of use." The input is keywords and sentiment scores, and the output is legal topics.

[1101] Step 6:

[1102] The server's generative AI model generates an appropriate answer based on the identified legal topic and sentiment score. The generative AI model references past data and legal documents to create an answer that corresponds to the emotional state. For example, it generates the answer, "Using images on the Internet without permission may constitute copyright infringement. If you are concerned, we recommend obtaining permission or using copyright-free images." The input is the legal topic and sentiment score, and the output is the generated answer.

[1103] Step 7:

[1104] The server sends the generated answer to the user's terminal. Before sending, the answer is converted to JSON format and sent securely to the terminal using the HTTPS protocol. The input is the generated answer, and the output is the answer displayed on the terminal.

[1105] Step 8:

[1106] The device displays the response received from the server to the user using JavaScript in the web browser or a mobile app UI framework (e.g., React or Flutter). The user can then decide what to do based on this.

[1107] Step 9:

[1108] If the user has additional questions or requires more detailed advice, they can enter the questions again. The server accepts the additional questions and runs the analysis process again. If the issue is deemed to be a serious legal problem, the server will suggest a paid consultation option with a specialist. For example, it may display a message saying, "We recommend that you consult with a paid specialist," and provide a link to access the specialist. The input is the additional question data, and the output is a suggestion of a paid consultation option.

[1109] (Application example 2)

[1110] 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."

[1111] In today's society, it is important for users to resolve legal questions quickly and accurately. However, conventional legal advice systems do not take into account the user's emotional state, which can potentially increase the user's anxiety and confusion. Furthermore, in certain situations, users often require additional, specialized legal advice. In such cases, the lack of means to quickly access legal experts presents a problem. Furthermore, in certain fields, such as electronic payment services, legal questions arise frequently and require special consideration for addressing them.

[1112] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1113] In this invention, the server includes means for accepting legal questions from users, means for analyzing the accepted questions, means for analyzing the user's emotions, means for generating appropriate answers based on the analyzed questions and emotion data, means for providing the generated answers to the users, and means for providing options for paid consultation with experts, thereby enabling the provision of prompt and accurate legal advice while taking into consideration the user's emotions, and enabling prompt access to experts as needed.

[1114] "Means for accepting legal questions from users" refers to a device or interface that allows the system to recognize and accept legal questions entered by users.

[1115] "Means for analyzing received questions" refers to the technology or process of using natural language processing technology to understand and analyze the meaning of input legal questions.

[1116] "Means for analyzing user emotions" refers to technology for recognizing and analyzing the user's emotional state (e.g., anxiety, joy, confusion, etc.) from text or voice input.

[1117] The "means for generating an appropriate answer based on the analyzed question and emotional data" is a system for automatically generating an optimal legal answer by taking into account the content of the analyzed question and the emotional state of the user.

[1118] The "means for providing the generated legal answer to the user" refers to an interface such as a display or an audio output device for presenting the generated legal answer to the user.

[1119] "Means for providing a fee-based expert consultation option" refers to means or options that allow a user to contact an expert for a fee if they require more detailed advice.

[1120] A "natural language processing engine" is a technology and software that enables computers to understand and analyze human language.

[1121] An "emotion engine" is a technology or software that analyzes emotions from a user's text or voice and outputs the results.

[1122] The following describes an embodiment of the present invention. The system accepts legal questions from users, analyzes them, and provides legal advice that takes into account their emotional state. It also provides paid expert consultations if necessary.

[1123] Hardware and software used

[1124] Device: Smartphone or tablet (iOS or Android)

[1125] Natural Language Processing Engine: Google NLU API or IBM Watson NLP

[1126] Sentiment analysis engine: Microsoft Azure Text Analytics

[1127] Server and answer generation engine: OpenAI GPT-4 model

[1128] Legal Database: A subscribed online legal resource library

[1129] System Programming and Processing

[1130] User question input and reception

[1131] A user launches an electronic payment app on their smartphone or tablet and enters a legal question, which is then sent from the device to the server.

[1132] Question analysis and sentiment analysis

[1133] The server passes the received question to a natural language processing engine (Google NLU API or IBM Watson NLP), which tokenizes the question and extracts keywords.

[1134] At the same time, the server passes the question to a sentiment analysis engine (Microsoft Azure Text Analytics) to analyze the sentiment data contained in the text.

[1135] Generate and provide answers

[1136] The server's AI engine (OpenAI GPT-4 model) generates appropriate legal answers based on the analyzed questions and sentiment data.

[1137] The generated answer is then sent back from the server to the device and provided to the user, who can then view the legal advice on the screen of their smartphone or tablet.

[1138] What to do in case of serious problems

[1139] If the user has additional questions or needs further advice, the server offers a paid expert consultation option, which allows the user to quickly connect with an expert and receive further advice to resolve the issue.

[1140] Specific use cases

[1141] User Questions and Analysis Examples

[1142] For example: "What do I do if I accidentally send money through PayPal?"

[1143] Question analysis: The keywords extracted were "PayPal," "incorrect remittance," and "how to deal with it."

[1144] Emotion analysis: Analyzes whether you are feeling anxious.

[1145] Example of generating and providing answers

[1146] Generated answer: "If you made a mistake with PayPal, select 'Cancel' from the transaction details page. If the problem persists, contact PayPal support."

[1147] Prompt Sentence Examples

[1148] "I accidentally sent money via PayPal. How do I cancel it?"

[1149] By implementing this system, users can receive prompt, accurate, and sensitive legal advice, with quick access to experts when needed.

[1150] In this way, a specific system for efficiently resolving legal issues in electronic payment services can be provided.

[1151] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1152] Step 1:

[1153] A user launches an electronic payment app on their smartphone or tablet and enters a legal question. The question is specific, such as "What should I do if I accidentally send money via PayPal?" After entering the question, the device sends it to the server. The entered data arrives at the server in text format.

[1154] Step 2:

[1155] The server passes the received question to a natural language processing engine (Google NLU API or IBM Watson NLP), where the question is tokenized and keywords are extracted. For example, keywords such as "PayPal," "incorrect transfer," and "how to resolve" are extracted. This makes it easier to understand the structure of the question.

[1156] Step 3:

[1157] At the same time, the server passes the question to a sentiment analysis engine (Microsoft Azure Text Analytics) to analyze the user's emotional state. Emotional data such as anxiety or confusion is extracted from the input text, and a judgment is made that the user is feeling anxious. This emotional data is taken into account when generating an answer.

[1158] Step 4:

[1159] The server identifies relevant legal topics based on the analyzed question data (keywords) and sentiment data. It references legal databases to collect information related to topics such as "electronic payment laws" and "consumer protection laws." This allows it to identify the appropriate legal basis for the question.

[1160] Step 5:

[1161] The server's AI engine (OpenAI GPT-4 model) generates appropriate answers based on the identified legal topic and sentiment data. For example, it generates specific advice such as, "To cancel an erroneous transfer on PayPal, select 'Cancel' from the transaction details page. If the issue persists, contact PayPal support." This generation process combines input data with legal resources to process the data.

[1162] Step 6:

[1163] The generated answer is sent from the server to the device. The user's smartphone or tablet receives the answer and displays it on the screen. The user can see specific advice such as, "If you made an incorrect transfer with PayPal, select 'Cancel' from the transaction details page. If the problem persists, contact PayPal support."

[1164] Step 7:

[1165] If the user requires further advice, the terminal sends an additional question to the server. At this time, the server provides an option to consult with a paid expert. For example, the server may provide a link to access the expert along with a message saying, "If you require further advice, we recommend that you consult with a paid expert." The user can quickly access the expert through this link and receive further detailed legal advice.

[1166] 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.

[1167] 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.

[1168] 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.

[1169] [Fourth embodiment]

[1170] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1171] 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.

[1172] 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).

[1173] 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.

[1174] 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.

[1175] 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).

[1176] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1177] 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.

[1178] 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.

[1179] 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.

[1180] 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.

[1181] 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.

[1182] 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."

[1183] This invention is a system that provides users with fast and accurate answers to legal questions. The system is designed to enable users to quickly obtain advice when they encounter legal questions or problems.

[1184] System Overview

[1185] The system mainly consists of the following elements:

[1186] 1. A device for inputting questions from users

[1187] 2. A server that receives the input question, analyzes it, and generates an answer

[1188] 3. Natural language processing engine for analysis and answer generation

[1189] 4. A means of providing generated answers to users

[1190] 5. Access to paid experts for in-depth advice

[1191] Program processing

[1192] User question input

[1193] Users enter legal questions from their devices (PCs, smartphones, etc.). For example, a user might enter a question like, "Can I use an image I found online on my blog?"

[1194] Submit a Question

[1195] The terminal sends the entered question to the server, which receives it and proceeds to the next step of the analysis process.

[1196] Question Analysis

[1197] The server passes the received question to a natural language processing engine, which analyzes the question. The natural language processing engine tokenizes the question and extracts phrases and keywords. For example, keywords such as "online images," "blog," and "use" are extracted.

[1198] Identifying Legal Topics

[1199] The server uses the extracted keywords to identify relevant legal topics, including referencing legal databases and related materials, such as "copyright law" or "digital media terms of use."

[1200] Generate answers

[1201] The server's AI engine generates appropriate answers based on the identified legal topic, references historical data and legal documents to build reliable answers, and then formats the generated answers in natural language. For example, a response might be, "Unauthorized use of images from the Internet may constitute copyright infringement."

[1202] Submit your answer

[1203] The generated answer is sent from the server to the user's device, where it is displayed. For example, the answer may read, "Unauthorized use of images may constitute copyright infringement. We recommend obtaining permission or using copyright-free images."

[1204] Actions to take in case of serious problems

[1205] If the user has additional questions or needs further advice, the server may offer a paid expert consultation option, where the user can obtain further detailed advice from the expert.

[1206] Specific examples

[1207] User Question: "Can I use your music in a YouTube video?"

[1208] Question analysis: "YouTube" "Music" "Use"

[1209] Identifying legal topics: "Copyright Law" and "YouTube Terms of Service"

[1210] Generate Answer: "Unauthorized use of music can be copyright infringement."

[1211] Answer provided: "Using music in your YouTube videos without permission may be copyright infringement. We recommend officially licensing or using royalty-free music."

[1212] By implementing this system, users can receive fast and accurate legal advice, minimizing uncertainty and risk, and can also seek expert advice if necessary.

[1213] The processing flow will be explained below.

[1214] Step 1:

[1215] A user enters a legal question into a terminal. For example, the user enters a question such as, "Can I use an image from the Internet on my blog?"

[1216] Step 2:

[1217] The terminal transmits the entered question to the server.

[1218] Step 3:

[1219] The server passes the received question to a natural language processing engine, which analyzes the question.

[1220] Step 4:

[1221] A natural language processing engine tokenizes the question and extracts phrases and keywords, such as "online images," "blogs," and "use."

[1222] Step 5:

[1223] The server then consults a legal database to identify relevant legal topics based on the extracted keywords, such as "copyright law" or "digital media terms of use."

[1224] Step 6:

[1225] The server's AI engine generates appropriate answers based on the identified legal topic, such as "Unauthorized use of images found online may constitute copyright infringement" by referencing historical data and legal documents.

[1226] Step 7:

[1227] The server formats the generated answer in natural language.

[1228] Step 8:

[1229] The server sends the formatted response to the user's terminal.

[1230] Step 9:

[1231] The device receives the response from the server and displays it to the user, for example, a message saying, "Unauthorized use of images may constitute copyright infringement. We recommend obtaining permission or using copyright-free images."

[1232] Step 10:

[1233] If the user has additional questions or needs more detailed assistance, the user enters further questions.

[1234] Step 11:

[1235] The server accepts and analyzes additional questions and suggests options for consultation with a paid expert, for example, by displaying "We recommend you consult with a paid expert" and providing a link to access the expert.

[1236] Example 1

[1237] 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."

[1238] In modern society, it is not easy to obtain fast and accurate advice when faced with legal questions or problems. In particular, the legal risks associated with using content on the Internet involve complex laws, making it difficult for the general public to obtain accurate information. Furthermore, under the current system, detailed legal advice requires direct consultation with an expert, which is a time-consuming and costly burden. To solve these issues, there is a need for a system that provides fast and accurate legal advice and allows access to expert advice when necessary.

[1239] 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.

[1240] In this invention, the server includes means for accepting legal questions from users, means for analyzing the accepted questions, means for generating appropriate answers based on the analyzed questions, means for providing the generated answers to the users, means for analyzing the accepted questions from users using a natural language processing engine, means for identifying legal topics based on the analyzed questions, and means for formatting the generated answers in natural language, thereby enabling the provision of prompt and accurate legal advice and, if necessary, the provision of detailed expert advice.

[1241] A "user" is an individual or company that enters a legal question into the system for the purpose of obtaining an answer.

[1242] A "terminal" is a device, such as a computer or smartphone, that a user uses to access the system and enter legal questions.

[1243] A "server" is a computer system that receives questions sent by users, analyzes them, and generates answers.

[1244] A "natural language processing engine" is a software technology that analyzes questions entered by users and extracts phrases and keywords.

[1245] "Tokenization" is the process by which a natural language processing engine breaks up input text and converts it into individual words and phrases.

[1246] A "legal topic" is a legal subject or issue area identified based on extracted keywords.

[1247] "Analysis" is the process of breaking down the received question and understanding its meaning.

[1248] "Answer generation" is the process of constructing an appropriate answer based on the parsed question.

[1249] "Natural language formatting" refers to converting the generated answers into text that is easy for humans to understand.

[1250] A "paid expert" is an individual or firm with a legal profession who is booked to provide in-depth legal advice.

[1251] The "Consultation Option" is an option to receive in-depth legal advice from a paid professional.

[1252] "Secure communication" is a communication method in which data is encrypted before transmission to prevent unauthorized access or tampering by third parties.

[1253] An "SQL query" is a standard query language used to retrieve specific information from a database.

[1254] A "generative AI model" is an artificial intelligence algorithm that is trained to generate appropriate answers to user input.

[1255] MODE FOR CARRYING OUT THE INVENTION

[1256] This invention is a system that provides users with fast and accurate answers to legal questions. The system is designed to enable users to quickly obtain advice when they encounter legal questions or problems.

[1257] System Overview

[1258] The system mainly consists of the following elements:

[1259] 1. A device for inputting questions from users

[1260] 2. A server that receives the input question, analyzes it, and generates an answer

[1261] 3. Natural language processing engine for question analysis and answer generation

[1262] 4. A means of providing generated answers to users

[1263] 5. Access to paid experts for in-depth advice

[1264] Specific Embodiments

[1265] User question input

[1266] Users enter legal questions from a device (such as a PC or smartphone). Specifically, the user enters a question such as, "Can I use an image I found online on my blog?" The device can be a web browser or a dedicated app.

[1267] Submit a Question

[1268] The device sends the entered question to the server, which receives it and proceeds to the next step of analysis. The HTTPS protocol is used to ensure secure communication when sending data.

[1269] Question Analysis

[1270] The server passes the received question to a natural language processing engine, which analyzes the question. The natural language processing engine tokenizes the question and extracts phrases and keywords. Specifically, the keywords extracted are "online images," "blogs," and "use."

[1271] Identifying Legal Topics

[1272] The server uses the extracted keywords to identify relevant legal topics, including referencing legal databases and related materials, such as "copyright law" or "digital media terms of use."

[1273] Generate answers

[1274] The server's AI engine generates appropriate answers based on the identified legal topic. It references historical data and legal documents to build reliable answers. It then formats the generated answers in natural language. For example, it might generate an answer like, "Unauthorized use of images found online may be a copyright infringement."

[1275] Overview of the generative AI model and example prompts

[1276] The server generates answers using a generative AI model (e.g., GPT-4). For example, in response to the question, "Is it okay to use images from the Internet on my blog?", the answer would be, "Using images without permission may be a copyright infringement. We recommend obtaining permission or using copyright-free images."

[1277] Submit your answer

[1278] The generated answer is sent from the server to the user's device. The device receives the answer and displays it in a user-friendly format. For example, a message such as "Unauthorized use of images may constitute copyright infringement. We recommend obtaining permission or using copyright-free images" may be displayed.

[1279] Actions to take in case of serious problems

[1280] If the user has additional questions or needs further advice, the server may offer a paid expert consultation option, where the user can obtain further detailed advice from the expert.

[1281] By implementing this system, users can receive fast and accurate legal advice, minimizing uncertainty and risk, and can access expert advice when necessary.

[1282] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1283] Step 1:

[1284] The user inputs a legal question using a terminal. The terminal provides an interface for the user to input a string such as "Can I use an image from the Internet on my blog?" The input question is stored in an input field on the terminal.

[1285] Input: User's legal question text

[1286] Output: The question text entered

[1287] Step 2:

[1288] The terminal sends the entered question to the server. In actual operation, the request content is converted into JSON format and a POST request is sent to the server using the HTTPS protocol.

[1289] Input: Question text

[1290] Output: JSON request sent to the server

[1291] Step 3:

[1292] The server passes the received question to a natural language processing engine for analysis. Specifically, the question is tokenized and phrases and keywords are extracted. The server then provides the received text to an NLP (natural language processing) library to obtain keywords such as "online images," "blogs," and "use."

[1293] Input: Question text in JSON format

[1294] Output: Extracted keywords (e.g., "online images," "blogs," "uses")

[1295] Step 4:

[1296] The server then uses the extracted keywords to identify relevant legal topics, consulting pre-built legal databases and indexes. The server then issues search queries to the databases to identify topics such as "copyright law" and "digital media terms of use."

[1297] Input: Extracted keywords

[1298] Output: Identified legal topics

[1299] Step 5:

[1300] The server sends a prompt to the generative AI model based on the identified legal topic and generates an appropriate answer. For example, the AI ​​model receives a prompt such as "Is it okay to use an image from the Internet on my blog?" and receives the answer "Unauthorized use may constitute copyright infringement."

[1301] Input: Legal topic and prompt

[1302] Output: Generated answer text

[1303] Step 6:

[1304] The server converts the generated response into JSON format and returns it to the user's device. The server serializes the generated response into JSON format and sends the response to the device using the HTTPS protocol.

[1305] Input: Generated answer text

[1306] Output: JSON formatted response data

[1307] Step 7:

[1308] The device displays the response received from the server. Specifically, the device parses the JSON data and displays it on the screen in a user-friendly format. For example, it displays a message saying, "Unauthorized use of images may be a copyright infringement. We recommend obtaining permission or using copyright-free images."

[1309] Input: JSON formatted response data

[1310] Output: User-friendly formatted answer display

[1311] Step 8:

[1312] If the user has additional questions or needs further advice, the server offers the option of a paid expert consultation: after the user provides additional input, the server returns a link that takes the user to a paid consultation booking page.

[1313] Input: User's additional questions or requests for further assistance

[1314] Output: Paid consultation booking page link

[1315] (Application example 1)

[1316] 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."

[1317] In conventional virtual stores, when users had legal questions about products or services, there were limited ways to get appropriate and prompt answers. This often left users feeling uncertain and anxious, which could discourage them from making a purchase. Furthermore, when users needed specialized legal advice, there was no established way to quickly access an appropriate expert. This sometimes led to a poor user experience.

[1318] 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.

[1319] In this invention, the server includes means for accepting legal questions from users, means for analyzing the accepted questions, means for generating appropriate answers based on the analyzed questions, means for providing the generated answers to the users, means for accepting questions while using the virtual store, and means for quickly providing answers to legal questions in the virtual store, thereby enabling users to quickly resolve their legal questions in the virtual store and use products and services with peace of mind.

[1320] A "user" is someone who uses the system to ask a legal question.

[1321] "Legal Question" means a question that involves a question or issue relating to the law.

[1322] "Means for receiving" refers to the interface or process for receiving input from the user.

[1323] "Means of analysis" refers to the process of interpreting the questions received and extracting important keywords and context.

[1324] "Means for generating appropriate answers" refers to a process that automatically generates reliable answers based on the analyzed information.

[1325] "Means for providing" refers to the process or interface for presenting the generated answer to the user.

[1326] A "virtual store" refers to a shop or service provider operated on the Internet.

[1327] "Means for accepting questions (while using the virtual store)" refers to a mechanism for accepting questions at any time from users using the virtual store.

[1328] "Means for quickly providing answers to legal questions" refers to a system that efficiently carries out the process from entering a question to providing an answer.

[1329] The present invention provides a system for providing a prompt and appropriate answer when a user asks a legal question in a virtual store. Specific embodiments of the present invention will be described below.

[1330] System Overview

[1331] The system mainly consists of the following elements:

[1332] 1. A device for inputting questions from users

[1333] 2. A server that receives the input question, analyzes it, and generates an answer

[1334] 3. Natural language processing engine for analysis and answer generation

[1335] 4. A means of providing generated answers to users

[1336] 5. A means of answering questions while using the virtual store

[1337] 6. A fast way to get answers to your legal questions

[1338] Specific processing of the program

[1339] The server receives legal questions from the user's device. The questions are sent in JSON format and are received by the server. The received questions are then passed to a natural language processing engine, which uses Hugging Face's Transformers library.

[1340] The engine analyzes the question, extracts relevant keywords and context, and then consults legal documents and databases to generate an appropriate answer. This answer is formatted in natural language that is easy for the user to understand. The formatted answer is then returned from the server to the user's device and displayed to the user.

[1341] Hardware and software used

[1342] Hardware: Smartphones, servers

[1343] Software: Python, Flask, Transformers library

[1344] Data processing and calculation flow

[1345] The server first tokenizes the received question and extracts phrases and keywords. Based on the results of this analysis, it identifies relevant legal topics. Based on the identified topics, it references past data and legal documents to generate an appropriate answer. The generated answer is then formatted in natural language and sent to the user's device.

[1346] Specific examples

[1347] User Question: "Is it legal to share images of this product on social media?"

[1348] Question analysis: "Product", "Image", "Social Media", "Share", "Legal Issues"

[1349] Identifying legal topics: "Copyright Law" and "Social Media Terms of Use"

[1350] Generate Answer: "Sharing product images on social media without permission may be a copyright infringement."

[1351] Answer provided: "Sharing images without permission may be copyright infringement. We recommend getting permission or using copyright-free images."

[1352] Prompt Sentence Examples

[1353] Is it legal to share images of this product on social media?

[1354] In this way, by implementing the present invention, users can instantly resolve legal questions in the virtual store, allowing them to use products and services with peace of mind.

[1355] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1356] Step 1:

[1357] Users enter legal questions into the virtual storefront

[1358] Users use a device such as a smartphone to input legal questions into the virtual store's interface. The input questions are sent to the server via an API. The input data is in text format, and a question such as "Is it legal to share an image of this product on social media?" is sent.

[1359] Step 2:

[1360] The server receives the user's question.

[1361] The server first collects and stores data to process the received questions. Specifically, it prepares the received text data to be passed to an analysis engine (e.g., a Flask application). The input data is the question in text format, and the output data is the same question data in text format.

[1362] Step 3:

[1363] The server analyzes the question using a natural language processing engine

[1364] The server passes the received question to a natural language processing engine (for example, Hugging Face's Transformers library), which tokenizes (splits) the question and extracts keywords. For example, the keywords extracted are "SNS," "image," "share," and "legal issue." The input data is the question text, and the output data is a list of extracted keywords.

[1365] Step 4:

[1366] The server identifies legal topics based on the extracted keywords.

[1367] The server then uses the extracted keywords to refer to legal databases and materials to identify relevant legal topics. This process involves researching and identifying copyright laws, social networking site terms of use, etc. The input data is a list of keywords, and the output data is a list of identified legal topics.

[1368] Step 5:

[1369] The server generates answers based on legal topics

[1370] The server generates appropriate answers based on the identified legal topics. It uses a generative AI model to reference historical data and legal documents to build reliable answers. The input data is a list of legal topics, and the output data is the generated answer text. For example, a generated answer might be, "Sharing product images on social media without permission may be a copyright infringement."

[1371] Step 6:

[1372] The server formats the generated answer in natural language and provides it to the user.

[1373] The generated answer is formatted in natural language so that it is easy for the user to understand. The formatted answer is then sent back to the user's device from the server. The input data is the generated answer text, and the output data is the formatted answer text. For example, the answer may be provided in the form of "Unauthorized image sharing may be a copyright infringement. We recommend obtaining permission or using a copyright-free image."

[1374] Step 7:

[1375] If the user reviews the answers provided and asks additional questions, the server is prepared to provide more detailed advice.

[1376] If the user checks the provided answer and needs further advice, the server provides a paid expert consultation option. In this case, the user can obtain further detailed advice from the expert. The input data is the user's follow-up question, and the output data is the information provided by the consultation option.

[1377] 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.

[1378] This invention is a system that provides quick and accurate answers to legal questions from users. By incorporating an emotion engine that recognizes the user's emotions, this system achieves more personalized responses. The specific configuration of the system and the program processing are described below.

[1379] System Overview

[1380] The system mainly consists of the following elements:

[1381] 1. A device for inputting questions from users

[1382] 2. A server that receives the input question, analyzes it, and generates an answer

[1383] 3. Natural language processing engine for analysis and answer generation

[1384] 4. Emotion engine that analyzes user emotions

[1385] 5. How to provide generated answers to users

[1386] 6. Access to paid experts for in-depth advice

[1387] Program processing

[1388] User question input

[1389] Users enter legal questions from their devices (PCs, smartphones, etc.). For example, a user might enter a question like, "Can I use an image I found online on my blog?"

[1390] Submit a Question

[1391] The terminal sends the entered question to the server, which receives it and proceeds to the next step of the analysis process.

[1392] Question Analysis

[1393] The server passes the received question to a natural language processing engine, which analyzes the question. The natural language processing engine tokenizes the question and extracts phrases and keywords. For example, keywords such as "online images," "blog," and "use" are extracted.

[1394] Emotion Analysis

[1395] At the same time, the server passes the question to an emotion engine to analyze the user's emotions, which analyzes the emotional data contained in the text and identifies whether the user is anxious, angry, or confused.

[1396] Identifying Legal Topics

[1397] The server uses the extracted keywords and sentiment data to identify relevant legal topics, including referencing legal databases and related materials, such as "Copyright Law" and "Digital Media Terms of Use."

[1398] Generate answers

[1399] The server's AI engine generates appropriate answers based on the identified legal topic and emotional data. It references historical data and legal documents and adjusts the tone and content of the answer depending on the user's emotional state. For example, the answer might read, "Using images from the Internet without permission may constitute copyright infringement. If you are concerned, we recommend getting permission or using copyright-free images."

[1400] Submit your answer

[1401] The generated answer is sent from the server to the user's device, where it is displayed. For example, the answer may read, "Unauthorized use of images may constitute copyright infringement. We recommend obtaining permission or using copyright-free images."

[1402] Actions to take in case of serious problems

[1403] If the user has additional questions or needs more detailed advice, they can enter more questions. The server accepts and parses the additional questions and suggests options for consulting with a paid expert. For example, it might say, "We recommend you consult with a paid expert," and provide a link to access the expert.

[1404] Specific examples

[1405] User Question: "Can I use your music in a YouTube video?"

[1406] Question analysis: "YouTube" "Music" "Use"

[1407] Sentiment analysis: Identifying when a user may be feeling anxious

[1408] Identifying legal topics: "Copyright Law" and "YouTube Terms of Service"

[1409] Generate an answer: "Unauthorized use of music may constitute copyright infringement. If you are concerned, we recommend obtaining an official license or using royalty-free music."

[1410] Answer provided: "Using music in your YouTube videos without permission may be copyright infringement. We recommend officially licensing or using royalty-free music."

[1411] By implementing this system, users can not only receive fast and accurate legal advice, but also emotional support, and, if necessary, the option to seek professional advice.

[1412] The processing flow will be explained below.

[1413] Step 1:

[1414] A user enters a legal question into a terminal. For example, the user enters a question such as, "Can I use an image from the Internet on my blog?"

[1415] Step 2:

[1416] The terminal transmits the entered question to the server.

[1417] Step 3:

[1418] The server passes the received question to a natural language processing engine, which analyzes the question. The natural language processing engine tokenizes the question and extracts phrases and keywords. For example, it extracts keywords such as "online images," "blog," and "use."

[1419] Step 4:

[1420] The server passes the parsed question to an emotion engine to analyze the user's emotions. The emotion engine analyzes the emotional data contained in the text and identifies whether the user is anxious, angry, or confused.

[1421] Step 5:

[1422] The server uses the extracted keywords and sentiment data to identify relevant legal topics, including referencing legal databases and related materials, such as "Copyright Law" and "Digital Media Terms of Use."

[1423] Step 6:

[1424] The server's AI engine generates appropriate answers based on the identified legal topic and emotional data. It references historical data and legal documents and adjusts the tone and content of the answer depending on the user's emotional state. For example, it might generate an answer like, "Using images from the Internet without permission may constitute copyright infringement. If you are concerned, we recommend getting permission or using copyright-free images."

[1425] Step 7:

[1426] The server formats the generated answer in natural language.

[1427] Step 8:

[1428] The server sends the formatted response to the user's terminal.

[1429] Step 9:

[1430] The device receives the response from the server and displays it to the user, for example, a message saying, "Unauthorized use of images may constitute copyright infringement. We recommend obtaining permission or using copyright-free images."

[1431] Step 10:

[1432] If the user has additional questions or needs more detailed assistance, the user enters further questions.

[1433] Step 11:

[1434] The server accepts and analyzes additional questions and suggests options for consultation with a paid expert, for example, by displaying "We recommend you consult with a paid expert" and providing a link to access the expert.

[1435] Example 2

[1436] 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."

[1437] Conventional legal advice systems provide standard answers without considering the user's feelings, which can result in users being unable to receive optimal advice. Furthermore, in cases of serious problems, detailed advice from an expert may be required, but the lack of such an option presents a challenge.

[1438] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for accepting legal questions from users, means for analyzing the questions using a natural language processing engine, means for identifying relevant legal topics, means for analyzing the user's emotions using an emotion engine, means for generating appropriate answers using a generative AI model, and means for providing the generated answers to the user. This enables more appropriate and personalized legal advice according to the user's emotional state. In addition, in cases of serious problems, a paid consultation option with a specialist can be provided.

[1439] The "means for accepting legal questions from users" refers to an interface that allows users to input legal questions using a device such as a PC or smartphone and send them to the system.

[1440] The "means of analyzing questions using a natural language processing engine" refers to a means of using natural language processing technology to tokenize received questions and extract phrases and keywords.

[1441] The "means for identifying relevant legal topics" refers to a means for finding appropriate legal topics by referring to legal databases and related materials based on the analyzed keywords.

[1442] The "means for analyzing user emotions using an emotion engine" refers to a means for analyzing text data of questions from users and using emotion analysis technology to identify whether the user is feeling emotions such as anxiety, anger, or confusion.

[1443] "Means for generating appropriate answers using a generative AI model" refers to means for using an artificial intelligence model to automatically generate appropriate legal advice based on relevant legal topics and sentiment data.

[1444] The "means for providing the generated answer to the user" refers to a means for transmitting the generated legal advice to the user's terminal and displaying it.

[1445] A "means for providing a fee-based expert consultation option" is a means for providing a user with the option to access a fee-based legal expert if the user requires more detailed advice.

[1446] MODE FOR CARRYING OUT THE INVENTION

[1447] This invention is a system that provides quick and accurate answers to legal questions from users. By incorporating an emotion engine that recognizes the user's emotions, this system achieves more personalized responses. The specific configuration of the system and the program processing are described below.

[1448] System Components

[1449] The system mainly consists of the following elements:

[1450] 1. A device for inputting questions from users

[1451] 2. A server that receives the input question, analyzes it, and generates an answer

[1452] 3. A natural language processing engine to analyze questions

[1453] 4. Emotion engine that analyzes user emotions

[1454] 5. How to provide generated answers to users

[1455] 6. Access to paid experts for in-depth advice

[1456] System Functions and Operation

[1457] A user inputs a legal question from a device (such as a PC or smartphone). For example, a user may input a question such as, "Can I use an image I found online on my blog?"

[1458] The device sends the entered question to the server. The server receives it and proceeds to the next step of analysis. First, the server passes the received question to a natural language processing engine (such as Hugging Face's Transformers) to analyze the question. The natural language processing engine tokenizes the question and extracts phrases and keywords. In this example, the keywords extracted are "online images," "blog," and "use."

[1459] At the same time, the server passes the question to an emotion engine (such as the Google Cloud Natural Language API) to analyze the user's emotions. The emotion engine analyzes the emotional data contained in the text and identifies whether the user is anxious, angry, or confused. For example, an emotion score of "Anxious: 0.75" is obtained.

[1460] The server uses the extracted keywords and sentiment data to identify relevant legal topics, including referencing legal databases and related materials, such as "Copyright Law" and "Digital Media Terms of Use."

[1461] Next, a generative AI model (such as OpenAI GPT-4) on the server generates an appropriate answer based on the identified legal topic and emotional data. The generative AI model refers to legal documents and past consultation cases and adjusts the tone and content of the answer depending on the user's emotional state. For example, a generated answer might read, "Using images from the Internet without permission may constitute copyright infringement. If you are concerned, we recommend obtaining permission or using copyright-free images."

[1462] The generated answer is sent from the server to the user's device. The device displays the answer, for example, a message saying, "Unauthorized use of images may constitute copyright infringement. We recommend obtaining permission or using copyright-free images."

[1463] If the user has additional questions or needs more detailed advice, they can enter their questions again. The server will accept the additional questions and perform the same analysis process. If the issue is deemed to be a serious legal problem, the server will offer the option of consulting a paid expert. For example, it may provide a message saying, "We recommend that you consult with a paid expert," along with a link to the expert.

[1464] Examples of prompt statements

[1465] Question: "Can I use your music in a YouTube video?"

[1466] Question analysis: Keywords "YouTube", "music", "usage"

[1467] Emotion analysis: Anxiety score 0.70

[1468] Legal topics: "Copyright Law" and "YouTube Terms of Service"

[1469] Generate an answer: "Using music without permission may be copyright infringement. We recommend obtaining an official license or using royalty-free music."

[1470] Answer provided: "Using music in your YouTube videos without permission may be copyright infringement. We recommend officially licensing or using royalty-free music."

[1471] This allows users to receive not only fast and accurate legal advice, but also emotional support, and the ability to seek professional advice if necessary.

[1472] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1473] System program processing flow

[1474] Step 1:

[1475] Users enter legal questions into their devices (PCs or smartphones). For example, a user might enter, "Can I use an image I found online on my blog?" This input becomes input data for the system.

[1476] Step 2:

[1477] The device sends the entered question to the server. The question content, which is input data, is securely transmitted using the HTTPS protocol. The device sends the question data in JSON format to the server's API endpoint as a POST request.

[1478] Step 3:

[1479] The server passes the received question data to a natural language processing engine. Specifically, the server tokenizes the received question and extracts phrases and keywords. For example, "online images," "blogs," and "use" are extracted. The input is the question data, and the output is the tokenized keywords.

[1480] Step 4:

[1481] At the same time, the server passes the question data to the emotion engine, which analyzes the user's emotions. The emotion engine analyzes the emotion data in the text and identifies whether the user is anxious, angry, or confused. For example, the emotion score obtained is "Anxiety: 0.75." The input is the question data, and the output is the emotion score.

[1482] Step 5:

[1483] The server identifies relevant legal topics based on tokenized keywords and sentiment scores obtained from the natural language processing engine and sentiment engine. Specifically, it queries a legal database to search for relevant information such as "copyright law" and "digital media terms of use." The input is keywords and sentiment scores, and the output is legal topics.

[1484] Step 6:

[1485] The server's generative AI model generates an appropriate answer based on the identified legal topic and sentiment score. The generative AI model references past data and legal documents to create an answer that corresponds to the emotional state. For example, it generates the answer, "Using images on the Internet without permission may constitute copyright infringement. If you are concerned, we recommend obtaining permission or using copyright-free images." The input is the legal topic and sentiment score, and the output is the generated answer.

[1486] Step 7:

[1487] The server sends the generated answer to the user's terminal. Before sending, the answer is converted to JSON format and sent securely to the terminal using the HTTPS protocol. The input is the generated answer, and the output is the answer displayed on the terminal.

[1488] Step 8:

[1489] The device displays the response received from the server to the user using JavaScript in the web browser or a mobile app UI framework (e.g., React or Flutter). The user can then decide what to do based on this.

[1490] Step 9:

[1491] If the user has additional questions or requires more detailed advice, they can enter the questions again. The server accepts the additional questions and runs the analysis process again. If the issue is deemed to be a serious legal problem, the server will suggest a paid consultation option with a specialist. For example, it may display a message saying, "We recommend that you consult with a paid specialist," and provide a link to access the specialist. The input is the additional question data, and the output is a suggestion of a paid consultation option.

[1492] (Application example 2)

[1493] 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."

[1494] In today's society, it is important for users to resolve legal questions quickly and accurately. However, conventional legal advice systems do not take into account the user's emotional state, which can potentially increase the user's anxiety and confusion. Furthermore, in certain situations, users often require additional, specialized legal advice. In such cases, the lack of means to quickly access legal experts presents a problem. Furthermore, in certain fields, such as electronic payment services, legal questions arise frequently and require special consideration for addressing them.

[1495] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1496] In this invention, the server includes means for accepting legal questions from users, means for analyzing the accepted questions, means for analyzing the user's emotions, means for generating appropriate answers based on the analyzed questions and emotion data, means for providing the generated answers to the users, and means for providing options for paid consultation with experts, thereby enabling the provision of prompt and accurate legal advice while taking into consideration the user's emotions, and enabling prompt access to experts as needed.

[1497] "Means for accepting legal questions from users" refers to a device or interface that allows the system to recognize and accept legal questions entered by users.

[1498] "Means for analyzing received questions" refers to the technology or process of using natural language processing technology to understand and analyze the meaning of input legal questions.

[1499] "Means for analyzing user emotions" refers to technology for recognizing and analyzing the user's emotional state (e.g., anxiety, joy, confusion, etc.) from text or voice input.

[1500] The "means for generating an appropriate answer based on the analyzed question and emotional data" is a system for automatically generating an optimal legal answer by taking into account the content of the analyzed question and the emotional state of the user.

[1501] The "means for providing the generated legal answer to the user" refers to an interface such as a display or an audio output device for presenting the generated legal answer to the user.

[1502] "Means for providing a fee-based expert consultation option" refers to means or options that allow a user to contact an expert for a fee if they require more detailed advice.

[1503] A "natural language processing engine" is a technology and software that enables computers to understand and analyze human language.

[1504] An "emotion engine" is a technology or software that analyzes emotions from a user's text or voice and outputs the results.

[1505] The following describes an embodiment of the present invention. The system accepts legal questions from users, analyzes them, and provides legal advice that takes into account their emotional state. It also provides paid expert consultations if necessary.

[1506] Hardware and software used

[1507] Device: Smartphone or tablet (iOS or Android)

[1508] Natural Language Processing Engine: Google NLU API or IBM Watson NLP

[1509] Sentiment analysis engine: Microsoft Azure Text Analytics

[1510] Server and answer generation engine: OpenAI GPT-4 model

[1511] Legal Database: A subscribed online legal resource library

[1512] System Programming and Processing

[1513] User question input and reception

[1514] A user launches an electronic payment app on their smartphone or tablet and enters a legal question, which is then sent from the device to the server.

[1515] Question analysis and sentiment analysis

[1516] The server passes the received question to a natural language processing engine (Google NLU API or IBM Watson NLP), which tokenizes the question and extracts keywords.

[1517] At the same time, the server passes the question to a sentiment analysis engine (Microsoft Azure Text Analytics) to analyze the sentiment data contained in the text.

[1518] Generate and provide answers

[1519] The server's AI engine (OpenAI GPT-4 model) generates appropriate legal answers based on the analyzed questions and sentiment data.

[1520] The generated answer is then sent back from the server to the device and provided to the user, who can then view the legal advice on the screen of their smartphone or tablet.

[1521] What to do in case of serious problems

[1522] If the user has additional questions or needs further advice, the server offers a paid expert consultation option, which allows the user to quickly connect with an expert and receive further advice to resolve the issue.

[1523] Specific use cases

[1524] User Questions and Analysis Examples

[1525] For example: "What do I do if I accidentally send money through PayPal?"

[1526] Question analysis: The keywords extracted were "PayPal," "incorrect remittance," and "how to deal with it."

[1527] Emotion analysis: Analyzes whether you are feeling anxious.

[1528] Example of generating and providing answers

[1529] Generated answer: "If you made a mistake with PayPal, select 'Cancel' from the transaction details page. If the problem persists, contact PayPal support."

[1530] Prompt Sentence Examples

[1531] "I accidentally sent money via PayPal. How do I cancel it?"

[1532] By implementing this system, users can receive prompt, accurate, and sensitive legal advice, with quick access to experts when needed.

[1533] In this way, a specific system for efficiently resolving legal issues in electronic payment services can be provided.

[1534] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1535] Step 1:

[1536] A user launches an electronic payment app on their smartphone or tablet and enters a legal question. The question is specific, such as "What should I do if I accidentally send money via PayPal?" After entering the question, the device sends it to the server. The entered data arrives at the server in text format.

[1537] Step 2:

[1538] The server passes the received question to a natural language processing engine (Google NLU API or IBM Watson NLP), where the question is tokenized and keywords are extracted. For example, keywords such as "PayPal," "incorrect transfer," and "how to resolve" are extracted. This makes it easier to understand the structure of the question.

[1539] Step 3:

[1540] At the same time, the server passes the question to a sentiment analysis engine (Microsoft Azure Text Analytics) to analyze the user's emotional state. Emotional data such as anxiety or confusion is extracted from the input text, and a judgment is made that the user is feeling anxious. This emotional data is taken into account when generating an answer.

[1541] Step 4:

[1542] The server identifies relevant legal topics based on the analyzed question data (keywords) and sentiment data. It references legal databases to collect information related to topics such as "electronic payment laws" and "consumer protection laws." This allows it to identify the appropriate legal basis for the question.

[1543] Step 5:

[1544] The server's AI engine (OpenAI GPT-4 model) generates appropriate answers based on the identified legal topic and sentiment data. For example, it generates specific advice such as, "To cancel an erroneous transfer on PayPal, select 'Cancel' from the transaction details page. If the issue persists, contact PayPal support." This generation process combines input data with legal resources to process the data.

[1545] Step 6:

[1546] The generated answer is sent from the server to the device. The user's smartphone or tablet receives the answer and displays it on the screen. The user can see specific advice such as, "If you made an incorrect transfer with PayPal, select 'Cancel' from the transaction details page. If the problem persists, contact PayPal support."

[1547] Step 7:

[1548] If the user requires further advice, the terminal sends an additional question to the server. At this time, the server provides an option to consult with a paid expert. For example, the server may provide a link to access the expert along with a message saying, "If you require further advice, we recommend that you consult with a paid expert." The user can quickly access the expert through this link and receive further detailed legal advice.

[1549] 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.

[1550] 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.

[1551] 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.

[1552] 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.

[1553] 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.

[1554] 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.

[1555] 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).

[1556] 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.

[1557] 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."

[1558] 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.

[1559] 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).

[1560] 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.

[1561] 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.

[1562] 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.

[1563] 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.

[1564] 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.

[1565] 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.

[1566] 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.

[1567] 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.

[1568] 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.

[1569] 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.

[1570] The following is further disclosed regarding the above embodiment.

[1571] (Claim 1)

[1572] a means for receiving legal inquiries from users;

[1573] a means for analyzing received questions;

[1574] means for generating an appropriate answer based on the parsed question;

[1575] means for providing the generated answer to the user;

[1576] A system including:

[1577] (Claim 2)

[1578] 10. The system of claim 1, further comprising means for providing a paid expert consultation option if further guidance is needed on the generated answer.

[1579] (Claim 3)

[1580] 10. The system according to claim 1, further comprising means for analyzing a question received from a user using a natural language processing engine.

[1581] "Example 1"

[1582] (Claim 1)

[1583] a means for receiving legal inquiries from users;

[1584] a means for analyzing received questions;

[1585] means for generating an appropriate answer based on the parsed question;

[1586] means for providing the generated answer to the user;

[1587] A means for analyzing questions received from users using a natural language processing engine;

[1588] a means for identifying legal topics based on the parsed questions;

[1589] a means for formatting the generated answers in natural language;

[1590] A system including:

[1591] (Claim 2)

[1592] 10. The system of claim 1, further comprising means for providing a paid expert consultation option if further guidance is needed on the generated answer.

[1593] (Claim 3)

[1594] 10. The system of claim 1, further comprising means for tokenizing a user's question and extracting phrases and keywords.

[1595] "Application Example 1"

[1596] (Claim 1)

[1597] a means for receiving legal inquiries from users;

[1598] a means for analyzing received questions;

[1599] means for generating an appropriate answer based on the parsed question;

[1600] means for providing the generated answer to the user;

[1601] a means for receiving questions during use of the virtual store;

[1602] A means of providing quick answers to legal questions in the virtual storefront;

[1603] A system including:

[1604] (Claim 2)

[1605] 10. The system of claim 1, further comprising means for providing a paid expert consultation option if further guidance is needed on the generated answer.

[1606] (Claim 3)

[1607] 10. The system according to claim 1, further comprising means for analyzing a question received from a user using a natural language processing engine.

[1608] "Example 2: Combining Emotion Engines"

[1609] (Claim 1)

[1610] a means for receiving legal inquiries from users;

[1611] A means for analyzing the received questions using a natural language processing engine;

[1612] a means for identifying relevant legal topics based on the parsed questions;

[1613] an emotion engine that analyzes the user's emotions;

[1614] A generative AI model that generates appropriate answers based on identified legal topics and sentiment data;

[1615] means for providing the generated answer to the user;

[1616] A system including:

[1617] (Claim 2)

[1618] 10. The system of claim 1, further comprising means for providing a paid expert consultation option if further guidance is needed on the generated answer.

[1619] (Claim 3)

[1620] 10. The system of claim 1, further comprising means for analyzing sentiment data relating to a legal question from a user.

[1621] "Application example 2 when combining emotion engines"

[1622] (Claim 1)

[1623] a means for receiving legal inquiries from users;

[1624] a means for analyzing received questions;

[1625] means for analyzing user emotions;

[1626] means for generating an appropriate answer based on the analyzed question and sentiment data;

[1627] means for providing the generated answer to the user;

[1628] ...

[1629] A system including:

[1630] (Claim 2)

[1631] 10. The system of claim 1, further comprising means for providing a paid expert consultation option if further guidance is needed on the generated answer.

[1632] (Claim 3)

[1633] 10. The system according to claim 1, further comprising: means for analyzing a question received from a user with a natural language processing engine; and means for analyzing emotions with an emotion engine. [Explanation of symbols]

[1634] 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 for receiving legal inquiries from users; a means for analyzing received questions; means for generating an appropriate answer based on the parsed question; means for providing the generated answer to the user; A system including:

2. 10. The system of claim 1, further comprising means for providing a paid expert consultation option if further guidance is needed on the generated answer.

3. 2. The system according to claim 1, further comprising means for analyzing a question received from a user by a natural language processing engine.

Images