system

Abstract

Provide a system. [Solution] a means of receiving legal inquiries from the user's device; A means of analyzing received questions and extracting key keywords; a means for querying a legal knowledge base based on the extracted keywords to retrieve relevant information; a means for generating a response based on the acquired information; means for selecting and embedding advertisements relevant to the generated answers; and means for transmitting the answer to the user's terminal.

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 life, ordinary people often face legal questions and problems in their daily lives, but it is difficult to access information to quickly and accurately resolve these problems. To receive professional legal advice, one must consult with a lawyer or expert with specialized knowledge and experience, which is costly and time-consuming. In addition, while there is a lot of information available on the Internet, there is no guarantee that this information is accurate or reliable. Therefore, there is a growing need for a system that provides reliable legal information that is easily accessible to the general public. [Means for solving the problem]

[0005] To solve this problem, the present invention provides the following means: First, a means for receiving a legal question from a user's device is provided. Next, a means for analyzing the received question and extracting key keywords is provided. Further, a means for querying a legal knowledge base based on the extracted keywords and obtaining relevant information is provided. After that, a means for generating an answer based on the obtained information is provided. Next, a means for selecting and embedding advertisements related to the generated answer is provided. Finally, a means for transmitting the answer to the user's device is provided. This allows users to quickly obtain reliable legal information and feel more secure about their actions.

[0006] "User's device" refers to the electronic device used by the User, such as a smartphone, computer, or tablet.

[0007] "Legal Question" means an inquiry from a user regarding a legal question or issue.

[0008] "Analysis" refers to the act of breaking down input data using natural language processing or other methods to extract meaning and important elements.

[0009] "Keywords" are the main words or phrases in a question that are important for identifying the content of the sentence.

[0010] A "legal knowledge base" refers to a database that systematically organizes legal information, including legal guidelines, legal provisions, and precedents.

[0011] "Querying" refers to the act of asking a database to search for specific information.

[0012] "Retrieving information" refers to the act of obtaining the required data as a result of a query against a database.

[0013] "Generating an answer" refers to the act of creating an appropriate response to a user's question based on the information obtained.

[0014] "Selecting an advertisement" refers to the act of appropriately selecting the advertisement content to be displayed based on the question and answer.

[0015] "Sending an answer" refers to the act of sending the generated answer to the user's terminal via data communication. [Brief explanation of the drawings]

[0016] [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 illustrating 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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] The present invention is a system that receives a legal question from a user's terminal, analyzes the question, retrieves information from an appropriate legal knowledge base to generate an answer, and provides the answer to the user. Specific embodiments of this system are described below.

[0038] Overall system configuration

[0039] The system mainly consists of the following components:

[0040] 1. User's device (smartphone, PC, tablet, etc.)

[0041] 2. Server (accepts questions, analyzes them, queries the database, generates answers, and sends them)

[0042] 3. Legal knowledge base (database containing legal information)

[0043] 4. Advertising database (stores relevant advertisements)

[0044] Program processing

[0045] The following provides a detailed natural language explanation of the operation of the system.

[0046] Posting and Submitting Questions

[0047] The user launches the application using their device and enters a legal question, which is then transmitted from the user's device to the server.

[0048] Receiving and parsing questions

[0049] When the server receives a user's question, it uses a natural language processing (NLP) engine to analyze the question, extracting key keywords and phrases from the query and formulating a query against a legal knowledge base.

[0050] Querying the Knowledge Base

[0051] The server then submits the generated query to a legal knowledge base to search for relevant information. The information returned from the legal knowledge base includes legal guidelines, statutes, and precedents that are directly related to the query.

[0052] Generate answers

[0053] The server uses the information it receives to generate a clear answer to the user's question, including legal reasons and specific advice.

[0054] Ad selection and placement

[0055] Advertisements relevant to the generated answer are selected from an advertisement database. For example, for a question about copyright, an advertisement for a copyright management service is selected and appropriately embedded in the answer text.

[0056] Submit your answer

[0057] The server sends data containing the final answer and the selected advertisement to the user's device. The user's device receives this data and uses a notification function to notify the user that the answer has arrived. The user can open the application and view the answer and advertisement.

[0058] Specific examples

[0059] Example 1: YouTube® music question

[0060] A user types the question "Is it illegal to use music from YouTube in my videos?" into their device and sends it. The server receives the question and extracts keywords such as "YouTube," "music," "video," and "illegal." It then searches a legal knowledge base based on these keywords, obtains information such as "YouTube music requires permission from the copyright holder," and generates an answer. It then selects an advertisement for a copyright management service and sends it to the user's device along with the answer. The user can review the answer and take further action if necessary.

[0061] Example 2: Question about the legality of downloads

[0062] A user types "Is it illegal to download movies from this site?" into their device and sends the question. The server receives the question and extracts keywords such as "site," "movie," "download," and "illegal." Based on these keywords, the server searches a legal knowledge base to obtain information such as "Under copyright law, downloading movies without permission is illegal," and generates an answer. An advertisement for a relevant copyright protection tool is selected and sent to the user's device along with the answer. The user can review the answer and take appropriate action.

[0063] In this way, the system of the present invention provides users with prompt and accurate information regarding legal questions they may have, helping them to act with confidence.

[0064] The processing flow will be explained below.

[0065] Step 1:

[0066] The device launches the application, and the user enters the legal question. The user taps the "Submit" button to submit the question.

[0067] Step 2:

[0068] The device sends the user's question to the server, including the question text, user ID, timestamp, and other metadata.

[0069] Step 3:

[0070] The server receives the request data from the terminal, checks the consistency of the data format, and verifies that the data has been received correctly.

[0071] Step 4:

[0072] The server passes the received question to a natural language processing (NLP) engine for analysis, which analyzes the question and extracts key keywords and related phrases.

[0073] Step 5:

[0074] The server generates a query to the legal knowledge base based on the extracted keywords, and uses the generated query to query the legal knowledge base.

[0075] Step 6:

[0076] The legal knowledge base responds to queries with relevant information, which the server receives and uses to answer the user's questions.

[0077] Step 7:

[0078] Based on the information obtained, the server generates a specific response text, including any necessary legal justification or advice.

[0079] Step 8:

[0080] The server selects advertisements relevant to the answer from an advertisement database and embeds the selected advertisements appropriately in the answer text.

[0081] Step 9:

[0082] The server constructs response data containing the final answer and advertisement, and sends the response data to the user's device.

[0083] Step 10:

[0084] The device receives the response data from the server and uses the notification function to notify the user that a response has been received.

[0085] Step 11:

[0086] Users tap the notification on their device to open the application, view the answer and the advertisement, and then take appropriate action.

[0087] Example 1

[0088] 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."

[0089] In today's world, systems that provide fast and accurate answers to legal problems and questions faced by users are not yet fully developed. This makes it difficult for users to obtain reliable answers to their legal questions and requires them to go to the trouble of searching for information themselves. Furthermore, there is a need for systems that provide relevant advertising information and provide useful support to users.

[0090] 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.

[0091] In this invention, the server includes a means for receiving legal questions from a user's device, a means for analyzing the received questions and extracting key keywords, and a means for querying a legal knowledge base based on the extracted keywords to retrieve relevant information. This allows users to easily enter legal questions and obtain answers quickly and accurately. The server also includes a means for generating clear answers using a generative AI model, a means for embedding advertisements related to the generated answers based on relevance, and a means for notifying the user of the receipt of the answers using a notification function on the user's device. This allows users to simultaneously obtain necessary legal information as well as information on related services and products, realizing a comprehensive support system.

[0092] "User device" refers to electronic devices such as smartphones, computers, and tablets.

[0093] "Legal Questions" refers to questions or inquiries about the law.

[0094] "Means for receiving" refers to the function of receiving data from the user's terminal.

[0095] "Means of analyzing and extracting key keywords" refers to the process of using text analysis technology to identify important keywords from the question content.

[0096] "Legal knowledge base" refers to a database containing information related to the law.

[0097] "Means of submitting a query and retrieving relevant information" refers to the ability to send a specific query to a database and retrieve relevant information.

[0098] "Means of generating an answer based on retrieved information" refers to the process of using information obtained from a database to create an answer to a question.

[0099] "Means for selecting and embedding advertisements relevant to the generated answer" refers to a function for selecting advertisements relevant to the answer content and appropriately placing them within the answer text.

[0100] "Means for sending answers to the user's terminal" refers to the function of sending the generated answers and advertisements to the user's terminal.

[0101] "Generative AI model" refers to a technology that uses a large-scale artificial intelligence model to generate natural-language output for specific inputs.

[0102] "Means for notifying the receipt of a response using a notification function" refers to a function for notifying the user's terminal that a new response has arrived.

[0103] The present invention is a system that receives a legal question from a user terminal, analyzes the question, obtains information from an appropriate legal knowledge base to generate an answer, and provides the answer to the user. The following specific embodiments for implementing this system will be described.

[0104] The overall system configuration includes the following elements:

[0105] 1. User's device (smartphone, PC, tablet, etc.)

[0106] 2. Server (accepts questions, analyzes them, queries the database, generates answers, and sends them)

[0107] 3. Legal knowledge base (database containing legal information)

[0108] 4. Advertising database (stores relevant advertisements)

[0109] First, a user launches the application on their device and enters a legal question, which is then sent from the user's device to the server. Specifically, the user enters a question such as, "Is it illegal to use music from YouTube in my videos?"

[0110] When the server receives the user's question, it analyzes the content of the question using a natural language processing (NLP) engine. Examples of software used include Python's NLTK and spaCy. Based on the key keywords and phrases extracted through this analysis, a query is created against the legal knowledge base. For example, keywords such as "YouTube," "music," "video," and "illegal" are extracted.

[0111] The server then submits the generated query to a legal knowledge base to search for relevant information. The information returned from the legal knowledge base includes legal guidelines, statutes, and precedents that are directly related to the question. Software used includes ElasticSearch (registered trademark) and SQL databases.

[0112] The server then uses the information it has obtained to generate a clear answer to the user's question, using a generative AI model (e.g., GPT-3®). An example prompt might look like this:

[0113] "What is the best response in this legal situation?"

[0114] Based on the information obtained and the prompt, the generative AI model generates an answer such as "YouTube requires permission from the copyright holder to use music."

[0115] Advertisements related to the generated answer are selected from an advertisement database. Based on the question and answer, highly relevant advertisements are selected and pasted into the answer. For example, an advertisement for a "copyright management service" is selected.

[0116] Finally, the server sends the final answer and the selected ad to the user's device using a communication protocol such as HTTPS.

[0117] The user's device displays the received data within the application and uses a notification function to notify the user that a response has arrived. The user can then open the application to view the response and advertisement.

[0118] This allows users to obtain fast and accurate information on legal questions, as well as information on related services and products, providing comprehensive support.

[0119] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0120] Step 1:

[0121] A user launches the application using their device and enters a legal question. This question is sent from the user's device to the server. The input is a textual question. Specifically, the user enters a question such as "Is it illegal to use music from YouTube in my video?" and presses the submit button. The output is the question data sent to the server.

[0122] Step 2:

[0123] The server receives the user's question and analyzes the question using a natural language processing (NLP) engine. The user's question data is obtained as input. To process the data, the NLP engine (e.g., spaCy) analyzes the text and extracts key keywords such as "YouTube," "music," "video," and "illegal." Specifically, the server passes the question received to the NLP engine, which then performs keyword extraction. A list of extracted keywords is generated as output.

[0124] Step 3:

[0125] The server queries the legal knowledge base based on the extracted keywords. A keyword list is obtained as input. As data calculation, a query statement is generated based on the keywords and submitted to Elasticsearch or an SQL database. Specifically, the server generates a query using the keywords "YouTube," "music," "video," and "illegal" and queries the database. As output, the legal knowledge base returns the relevant legal information.

[0126] Step 4:

[0127] The server generates an answer to the user's question based on the legal information it has acquired. The input is legal information acquired from a database. Data processing involves inputting the information into a generative AI model (e.g., GPT-3) along with a prompt, which generates an answer in natural language. An example of a prompt is "What is the best response in this legal situation?" Specific operations involve inputting the legal information acquired by the server into the AI ​​model, which then generates an answer. The output is an answer written in natural language.

[0128] Step 5:

[0129] Advertisements relevant to the generated answer are selected from an advertisement database and embedded in the answer text. The generated answer sentence is obtained as input. Data calculation involves searching for advertisements that are highly relevant based on the answer content and inserting them in the appropriate position. In specific operations, the server selects relevant advertisements based on the answer sentence from the database and embeds the advertisements in the answer text. The output is the final answer sentence with the advertisements embedded.

[0130] Step 6:

[0131] The server sends the completed answer and advertisement to the user's device. The final answer is obtained as input. Data processing involves generating a data packet for transmission and sending it using a secure communication protocol (e.g., HTTPS). Specifically, the server sends data including the answer and advertisement to the user's device. The user's device receives the data as output.

[0132] Step 7:

[0133] The user's device displays the received answer and advertisement and notifies the user. The input is the data sent from the server. The data is processed by converting the answer and advertisement into a display format and displaying them on the device's UI. The specific operation is that the device notifies the user that "a new answer is available," and the user opens the application to check the answer and advertisement. The output is a state in which the user can check the answer and advertisement.

[0134] (Application example 1)

[0135] 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."

[0136] Physical stores lack the means to quickly and accurately respond to legal issues faced by staff and owners. Questions about laws such as the Consumer Protection Act and the Labor Standards Act often arise in daily operations, making it difficult to obtain appropriate answers on the spot. This raises concerns about business delays and increased legal risks.

[0137] 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.

[0138] In this invention, the server includes means for receiving legal questions from a user's device, means for analyzing the received questions and extracting key keywords, means for querying a legal knowledge base based on the extracted keywords and retrieving relevant information, means for selecting and embedding advertisements related to the generated answers, means for enabling store staff to ask legal questions using smartphones, means for providing legal information related to the Consumer Protection Act and the Labor Standards Act, and means for transmitting answers to the user's device, thereby enabling stores to quickly and accurately respond to legal issues.

[0139] "User device" refers to an information device used to input and send legal questions, including smartphones, PCs, tablets, etc.

[0140] "Means for receiving" refers to the function of receiving legal questions sent from the user's terminal on the server.

[0141] "Means for analyzing and extracting key keywords" refers to the function of analyzing received legal questions using a natural language processing engine and identifying important keywords.

[0142] "Means of querying a legal knowledge base and retrieving relevant information" refers to the function of querying a legal knowledge base based on extracted keywords and searching for and retrieving relevant legal information.

[0143] "Means for generating answers" refers to the functionality for generating answers to users' questions based on the acquired legal information.

[0144] "Means for selecting and embedding relevant advertisements" refers to a function for selecting from an advertisement database the advertisement most relevant to the generated answer and appropriately placing it within the answer.

[0145] "Means enabling store staff to use their smartphones to ask legal questions" refers to functionality that enables store staff to use their smartphones to submit legal questions to the system.

[0146] "Means of providing legal information on the Consumer Protection Act and the Labor Standards Act" refers to the function of providing information on the Consumer Protection Act and the Labor Standards Act that is related to the content of the question.

[0147] "Means for sending answers to the user's terminal" refers to a function for sending the generated answers to the user's terminal and allowing the user to check the answers.

[0148] MODE FOR CARRYING OUT THE INVENTION

[0149] The present invention is a system in which users input legal questions, and the server analyzes and generates answers. The system aims to provide store staff with fast and accurate information on legal issues they face in the field. The present invention is implemented in the following configuration.

[0150] Overall system configuration

[0151] The system mainly consists of the following components:

[0152] 1. User's device: An information device used to input and submit legal inquiries, including smartphones, PCs, tablets, etc.

[0153] 2. Server: Accepts questions, analyzes them, queries the database, generates answers, and sends them.

[0154] 3. Legal Knowledge Base: A database containing information about the law.

[0155] 4. Advertisement database: stores relevant advertisements.

[0156] Program processing

[0157] Posting and Submitting Questions

[0158] The user launches the application using their device and enters a legal question, which is then sent from the user's device to the server.

[0159] Receiving and parsing questions

[0160] Once the server receives the user's question, it analyzes it using a natural language processing (NLP) engine, such as SpaCy or NLTK, and uses the key keywords and phrases extracted from the analysis to formulate a query against a legal knowledge base.

[0161] Querying the Knowledge Base

[0162] The server then submits the generated query to a legal knowledge base, which stores a wide range of legal information, including consumer protection laws, labor standards laws, and copyright laws, and returns information such as specific legal guidelines, provisions, and precedents.

[0163] Generate answers

[0164] The server uses the information it receives to generate a clear answer to the user's question, including legal reasons and specific advice.

[0165] Ad selection and placement

[0166] Advertisements relevant to the generated answer are selected from an advertisement database and appropriately embedded in the answer text, for example, an advertisement for a complaint management service is selected for a consumer protection question.

[0167] Submit your answer

[0168] The server sends data containing the final answer and the selected advertisement to the user's device. The user's device receives this data and uses a notification function to notify the user that the answer has arrived. The user can open the application and view the answer and advertisement.

[0169] Specific examples

[0170] Specific examples are shown below.

[0171] Example 1: When a store staff member asks on their smartphone, "A consumer has requested a return. How should I handle this?" The server extracts keywords such as "return," "consumer," and "response" and retrieves information on the applicable consumer protection law. It then provides appropriate guidelines and simultaneously displays an advertisement for a complaint management service.

[0172] Example prompt sentence:

[0173] "What are the Consumer Protection Act guidelines regarding returns?"

[0174] This system will enable legal issues to be dealt with quickly and accurately even in physical stores.

[0175] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0176] Step 1:

[0177] The terminal provides an interface for users to input legal questions. The input content is a specific legal question, such as "A consumer has requested a return. How should I respond?" When the user inputs a question and presses the send button, the terminal sends the question data to the server.

[0178] Step 2:

[0179] The server receives legal question data sent from the user's device. The input includes the user's question text data. The server inputs this received data into a natural language processing (NLP) engine (e.g., SpaCy, NLTK) to analyze the question content. Specific processing includes sentence tokenization, semantic analysis, and keyword extraction. The output is a list of analyzed keywords.

[0180] Step 3:

[0181] The server generates a query to the legal knowledge base based on the keyword list extracted by the analysis. The keyword list obtained in step 2 is used as input. As a specific example of query generation, an SQL query to search for related legal information is constructed using keywords such as "consumer," "return," and "response." The generated query is obtained as output.

[0182] Step 4:

[0183] The server submits the generated query to a legal knowledge base to retrieve the relevant legal information. The generated query is used as input. A database engine (e.g., MySQL (registered trademark), PostgreSQL) is used to execute the query and search for relevant legal provisions and guidelines. The retrieved legal information is the output.

[0184] Step 5:

[0185] The server generates an answer to the user's question based on the acquired legal information. The legal information acquired in step 4 is used as input. Specifically, the server constructs an answer text to the user's question based on the legal information. The generated answer text is obtained as output.

[0186] Step 6:

[0187] The server selects advertisements from an advertisement database that are relevant to the generated answer and embeds them appropriately in the answer text. Keywords related to the generated answer text are used as input. A database query is used to search the advertisement database to select relevant advertisements, such as an advertisement for a "claim management service." The output is the answer text with the embedded advertisement.

[0188] Step 7:

[0189] The server sends data including the final answer and the selected advertisement to the user's device. The answer text with the embedded advertisement obtained in step 6 is used as input. The server sends this to the user's device, and the device uses a notification function to notify the user that the answer has arrived. The user can open the application and check the answer and advertisement. The output is the answer data that is displayed on the user's device.

[0190] The above are the specific processing steps of the system that realizes the application example.

[0191] 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.

[0192] The present invention is a system that receives legal questions from a user's device, analyzes the questions, retrieves information from an appropriate legal knowledge base to generate answers, and further combines it with an emotion engine that recognizes the user's emotions to provide more personalized answers and advertisements. Specific embodiments of this system are described below.

[0193] Overall system configuration

[0194] The system mainly consists of the following components:

[0195] 1. User's device (smartphone, PC, tablet, etc.)

[0196] 2. Server (accepts questions, analyzes them, recognizes emotions, queries the database, generates answers, and sends them)

[0197] 3. Legal knowledge base (database containing legal information)

[0198] 4. Advertising database (stores relevant advertisements)

[0199] 5. Emotion engine (recognizes user emotions and reflects them in analysis)

[0200] Program processing

[0201] The following provides a detailed natural language explanation of the operation of the system.

[0202] Posting and Submitting Questions

[0203] The user launches the application using their device and enters a legal question, which is then transmitted from the user's device to the server.

[0204] Receiving and parsing questions

[0205] The device sends the user's question to the server, which then uses a natural language processing (NLP) engine to analyze the question and extract key keywords and phrases.

[0206] Emotion recognition

[0207] The server uses an emotion engine to recognize emotions from the user's input. This emotion information is then associated with the analysis results of the question. The emotion engine determines emotions such as anger, sadness, joy, and surprise based on the user's vocabulary and sentence structure.

[0208] Querying the Knowledge Base

[0209] The server generates a query to the legal knowledge base based on the extracted keywords and the recognized sentiment, and uses the generated query to query the legal knowledge base.

[0210] Obtaining information and generating answers

[0211] The legal knowledge base responds to queries with relevant information. The server receives the information and generates a specific answer text tailored to the user's question and sentiment. The answer includes the necessary legal reasoning and advice.

[0212] Emotion-based ad selection

[0213] The server selects relevant ads from an advertising database based on the user's emotions, and embeds them in the answer depending on the user's emotions. For example, if the user is angry, an ad that conveys a more calming or reassuring feeling will be selected.

[0214] Submit your answer

[0215] The server constructs response data containing the final answer and advertisement, which is then sent to the user's device.

[0216] Receiving and notifying responses

[0217] The device receives the response data from the server and uses the notification function to notify the user that a response has been received.

[0218] Show Answers

[0219] Users tap the notification on their device, open the application, and view the response and advertisement content, which they can then take appropriate action based on.

[0220] Specific examples

[0221] Example 1: YouTube music question

[0222] The user types "Is it illegal to use music from YouTube in my videos?" into their device and sends the question. The server receives this question and extracts keywords such as "YouTube," "music," "video," and "illegal." At the same time, the emotion engine recognizes that the user is feeling anxious. Based on these keywords and emotion information, the server searches a legal knowledge base, obtains the information that "using music from YouTube requires permission from the copyright holder," and generates an answer. An advertisement for a copyright management service that gives a sense of security in line with the emotion is selected and sent to the user's device along with the answer. The user can check the answer and take the next step with peace of mind.

[0223] Example 2: Question about the legality of downloads

[0224] The user types "Is it illegal to download movies from this site?" into their device and sends the question. The server receives this question and extracts keywords such as "site," "movie," "download," and "illegal." At the same time, the emotion engine recognizes that the user has doubts. Based on these keywords and emotion information, the server searches the legal knowledge base to obtain the information that "Under copyright law, downloading movies without permission is illegal," and generates an answer. An advertisement for a related copyright protection tool is selected and sent to the user's device along with the answer. The user can check the answer and take appropriate action.

[0225] In this way, the system of the present invention can provide prompt and accurate information to answer legal questions while taking into consideration the user's feelings, allowing the user to receive answers that take their feelings into consideration and act with peace of mind.

[0226] The processing flow will be explained below.

[0227] Step 1:

[0228] The device launches the application, and the user enters the legal question. The user taps the "Submit" button to submit the question.

[0229] Step 2:

[0230] The device sends the user's question to the server, including the question text, user ID, timestamp, and other metadata.

[0231] Step 3:

[0232] The server receives the request data from the terminal. The server checks the consistency of the data format and verifies that the data has been received correctly.

[0233] Step 4:

[0234] The server passes the received question to a natural language processing (NLP) engine, which analyzes the question and extracts key keywords and phrases.

[0235] Step 5:

[0236] The server uses an emotion engine to recognize emotions from the user's input, and associates the recognized emotions with the analysis results of the question.

[0237] Step 6:

[0238] The server generates queries to a legal knowledge base based on the extracted keywords and the recognized sentiments.

[0239] Step 7:

[0240] The server queries the generated query against a legal knowledge base to retrieve relevant information.

[0241] Step 8:

[0242] Based on the information acquired, the server generates a specific answer text based on the user's question and the perceived sentiment, including any necessary legal justification or advice.

[0243] Step 9:

[0244] The server selects advertisements from an advertisement database that are relevant to the answer, and the relevant advertisements are selected based on the recognized sentiment.

[0245] Step 10:

[0246] The server appropriately embeds the selected advertisement into the answer text to construct the final response data.

[0247] Step 11:

[0248] The server sends the response data (answers and advertisements) to the user's terminal.

[0249] Step 12:

[0250] The device receives the response data from the server and uses the notification function to notify the user that a response has been received.

[0251] Step 13:

[0252] Users tap the notification on their device, open the application, and view the response and advertisement content, allowing them to take appropriate action.

[0253] Example 2

[0254] 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."

[0255] Conventional legal question-answering systems were unable to consider the user's emotions when providing appropriate answers to legal questions entered by the user, making it difficult to provide answers that give the user a sense of satisfaction and security.In addition, the selection of advertisements was not based on the user's emotions, making it difficult to improve the quality of the user experience.

[0256] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0257] In this invention, the server includes means for receiving a legal question from a user's terminal, means for analyzing the received question and extracting key keywords and the user's emotions, means for generating a query to a legal knowledge base based on the extracted keywords and emotions and acquiring relevant information, means for generating an answer based on the acquired information and emotions, means for selecting and embedding an advertisement based on the emotion associated with the generated answer, means for transmitting the answer and advertisement to the user's terminal, and means for notifying the user's terminal of the receipt of the answer using a notification function, thereby enabling the user to receive personalized answers and advertisements according to their emotions.

[0258] "User device" refers to a device such as a smartphone, computer, or tablet that a user uses to enter a question.

[0259] A "Legal Question" is text information entered by a user about a legal question or issue.

[0260] "Analysis" is the process of analyzing the user-entered question and extracting key keywords and sentiments.

[0261] "Keywords" are important words and phrases extracted to understand the content of a question and generate an appropriate answer.

[0262] "Emotion" refers to emotional information that can be read from the user's input, and includes, for example, anxiety, doubt, anger, joy, etc.

[0263] A "legal knowledge base" is a database that stores legal information and provides relevant information in response to queries.

[0264] A "query" is a search inquiry against the legal knowledge base, generated based on the extracted keywords and sentiment.

[0265] "Answer generation" is the process of creating specific answer texts to users' questions based on the acquired legal and emotional information.

[0266] "Advertising" refers to promotional information for related products and services that are provided based on the user's interests and emotions.

[0267] "Sending" refers to the act of sending responses and advertisements as data from the server to the user's terminal.

[0268] The "notification function" is an application function that notifies the user's device that a new answer has arrived.

[0269] The present invention is a system that receives legal questions from a user's device, analyzes the questions, retrieves information from an appropriate legal knowledge base to generate answers, and further combines it with an emotion engine that recognizes the user's emotions to provide more personalized answers and advertisements. Specific embodiments of this system are described below.

[0270] Hardware and software used

[0271] This system is implemented using the following hardware and software:

[0272] User devices: smartphones, computers, tablets, etc.

[0273] Server: A high-performance computer that accepts questions, analyzes them, recognizes emotions, queries the database, generates answers, and sends them.

[0274] Legal Knowledge Base: A database containing legal information (MySQL, MongoDB, etc.)

[0275] Advertisement database: stores relevant advertisements

[0276] Natural Language Processing (NLP) engines: spaCy, Google Cloud NLP API, etc.

[0277] Emotion engine: Microsoft® Azure® Text Analytics API, IBM Watson® Tone Analyzer, etc.

[0278] Specific operation of the system

[0279] Posting and Submitting Questions

[0280] The user launches the application using their device and enters a legal question, which is then transmitted from the user's device to the server.

[0281] Receiving and parsing questions

[0282] The device sends the user's question to the server, which then uses a natural language processing (NLP) engine to analyze the question and extract key keywords and phrases.

[0283] Emotion recognition

[0284] The server uses an emotion engine to recognize emotions from the user's input. This emotion information is then associated with the analysis results of the question. The emotion engine determines emotions such as anger, sadness, joy, and surprise based on the user's vocabulary and sentence structure.

[0285] Querying the Knowledge Base

[0286] The server generates a query to the legal knowledge base based on the extracted keywords and the recognized sentiment, and uses the generated query to query the legal knowledge base.

[0287] Obtaining information and generating answers

[0288] The legal knowledge base responds to queries with relevant information. The server receives the information and generates a specific answer text tailored to the user's question and sentiment. The answer includes the necessary legal reasoning and advice.

[0289] Emotion-based ad selection

[0290] The server selects relevant ads from an advertising database based on the user's emotions, and embeds them in the answer depending on the user's emotions. For example, if the user is angry, an ad that conveys a more calming or reassuring feeling will be selected.

[0291] Submit your answer

[0292] The server constructs response data containing the final answer and advertisement, which is then sent to the user's device.

[0293] Receiving and notifying responses

[0294] The device receives the response data from the server and uses the notification function to notify the user that a response has been received.

[0295] Show Answers

[0296] Users tap the notification on their device, open the application, and view the response and advertisement content, which they can then take appropriate action based on.

[0297] Specific examples

[0298] Example 1: YouTube music question

[0299] The user types "Is it illegal to use music from YouTube in my videos?" into their device and sends the question. The server receives this question and extracts keywords such as "YouTube," "music," "video," and "illegal." At the same time, the emotion engine recognizes that the user is feeling anxious. Based on these keywords and emotion information, the server searches a legal knowledge base, obtains the information that "using music from YouTube requires permission from the copyright holder," and generates an answer. An advertisement for a copyright management service that gives a sense of security in line with the emotion is selected and sent to the user's device along with the answer. The user can check the answer and take the next step with peace of mind.

[0300] Example 2: Question about the legality of downloads

[0301] The user types "Is it illegal to download movies from this site?" into their device and sends the question. The server receives this question and extracts keywords such as "site," "movie," "download," and "illegal." At the same time, the emotion engine recognizes that the user has doubts. Based on these keywords and emotion information, the server searches the legal knowledge base to obtain the information that "Under copyright law, downloading movies without permission is illegal," and generates an answer. An advertisement for a related copyright protection tool is selected and sent to the user's device along with the answer. The user can check the answer and take appropriate action.

[0302] Prompt Sentence Examples

[0303] For example, a prompt for a generative AI model might look like this:

[0304] "Analyze the legal question entered by the user and generate relevant answers and sentiment-based ads, but also provide the sentiment the user is feeling about the question."

[0305] This system allows users to receive fast, accurate, and emotionally sensitive legal information, allowing them to take their next steps with confidence.

[0306] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0307] Step 1:

[0308] The user uses the terminal to launch the application and enter the legal questions.

[0309] What it does: A user launches the application and types a question into the question field, for example, "Is it illegal to use music from YouTube in my videos?"

[0310] Input: Legal question text.

[0311] Output: The question data sent from the device to the server.

[0312] Step 2:

[0313] The terminal transmits the entered question to the server.

[0314] Specific operation: The device sends the question text to the server as an HTTP POST request.

[0315] Input: The question text entered on the terminal.

[0316] Output: The HTTP request sent to the server.

[0317] Step 3:

[0318] The server analyzes the received question using a natural language processing (NLP) engine and extracts key keywords.

[0319] Specific operation: The server uses an NLP engine (e.g., spaCy) to extract the main keywords: "YouTube," "music," "video," and "illegal."

[0320] Input: The question text in the HTTP request.

[0321] Output: A list of extracted main keywords.

[0322] Step 4:

[0323] The server uses an emotion engine to recognize emotions from user input.

[0324] Specific operation: The server runs an emotion engine (for example, Microsoft Azure's Text Analytics API) and determines the user's emotion as "anxiety."

[0325] Input: Question text.

[0326] Output: Recognized emotion information.

[0327] Step 5:

[0328] The server generates a query to a legal knowledge base based on the extracted keywords and sentiment information.

[0329] What it does: The server generates a MySQL database query to search the legal knowledge base for the keywords "YouTube," "music," "video," and "illegal."

[0330] Input: Extracted keywords and sentiment information.

[0331] Output: A query against the legal knowledge base.

[0332] Step 6:

[0333] It retrieves information from a legal knowledge base in response to a query and generates a specific answer text.

[0334] Specific operation: The legal knowledge base returns the information that "YouTube requires permission from the copyright holder to use music," and the server creates an answer text based on this information.

[0335] Input: The generated query.

[0336] Output: The specific answer text.

[0337] Step 7:

[0338] The server selects an appropriate advertisement from an advertisement database according to the emotion information.

[0339] Specific operation: The server recognizes that the emotion is "anxiety" and selects an advertisement for a copyright management service that gives a sense of security.

[0340] Input: Recognized emotion information.

[0341] Output: The selected ad.

[0342] Step 8:

[0343] The server sends the final answer and advertisement to the user's terminal.

[0344] Specific operation: The server combines the selected advertisement and answer and sends it to the user's device as an HTTP response.

[0345] Input: Answer text and selected ad.

[0346] Output: The HTTP response sent to the user's device.

[0347] Step 9:

[0348] The user's terminal receives the response data from the server and notifies the user.

[0349] Specific behavior: The device displays a push notification to inform the user that a response has been received.

[0350] Input: HTTP response data from the server.

[0351] Output: A push notification to the user.

[0352] Step 10:

[0353] Users tap the notification to open the application and view the answers and advertisements.

[0354] What happens: The user sees the answer "YouTube music usage requires permission from the copyright holder" within the application, along with an advertisement for a related copyright management service.

[0355] Input: Answers and ad data displayed by user actions.

[0356] Output: The answer and advertisement that the user sees.

[0357] (Application example 2)

[0358] 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."

[0359] Current virtual store legal consultation systems are unable to provide answers that take users' emotions into account and are unable to meet users' needs for personalized advice. As a result, users are unable to receive appropriate advice and find it difficult to resolve their legal questions and concerns. Furthermore, they are unable to select relevant advertisements based on users' emotions, which reduces the quality of the user experience.

[0360] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a legal question from a user's terminal, means for analyzing the received question and extracting key keywords, means for querying a legal knowledge base based on the extracted keywords and obtaining relevant information, means for generating an answer based on the obtained information, means for recognizing the user's emotions, means for personalizing the answer based on the recognized emotions, means for selecting and embedding advertisements related to the generated answer, and means for transmitting the answer to the user's terminal. This makes it possible to provide personalized legal advice that takes user emotions into consideration and to appropriately select relevant advertisements.

[0361] "User device" refers to an electronic device, such as a smartphone, tablet, head-mounted display, or personal computer, that a user uses to input and receive questions.

[0362] A "legal question" is text or voice input that a user makes into a device to request information or advice about a legal matter.

[0363] "Analysis" is the process of categorizing incoming legal questions using natural language processing techniques and extracting key keywords and phrases.

[0364] "Keywords" are words or phrases that are particularly important in a legal question and form the basis for querying a legal knowledge base.

[0365] A "legal knowledge base" is a database that systematically collects legal information and provides relevant legal information in response to a query.

[0366] A "query" refers to an inquiry made to a legal knowledge base to obtain information.

[0367] "Retrieving information" means querying a legal knowledge base to retrieve relevant law or advice and gathering it to answer the user's question.

[0368] "Generating an answer" means creating a specific and appropriate response to a user's question based on the legal information obtained.

[0369] "Emotion recognition" means using natural language processing and artificial intelligence technology to determine a user's emotional state from their questions and voice.

[0370] "Personalizing" means tailoring responses to the user's emotions and specific circumstances to provide individually optimized answers.

[0371] "Selecting ads" means selecting relevant ads from an ad database based on the user's emotions and questions.

[0372] "Embedding advertisements in answers" means placing selected advertisements in appropriate positions within the generated legal answer text.

[0373] "Sending an answer" refers to the process of sending the generated answer and related advertisement to the user's terminal using telecommunications means.

[0374] The present invention provides a legal consultation assistant system that allows users to obtain immediate and personalized answers to legal questions in a virtual store. A detailed description of specific embodiments of the system is provided below.

[0375] Overall system configuration

[0376] The system consists of the following components:

[0377] 1. User's Device

[0378] 2. Server

[0379] 3. Legal Knowledge Base

[0380] 4. Advertising Database

[0381] 5. Emotion Recognition Engine

[0382] User's device

[0383] The user's device is an electronic device such as a smartphone, tablet, or head-mounted display. An application is installed on the device that allows the user to enter legal questions. The application includes a text box for entering questions and voice recognition capabilities.

[0384] server

[0385] The server receives legal questions sent from the user's device, analyzes them, recognizes emotions, generates queries to a legal knowledge base to obtain information, and finally performs a series of processes to generate personalized answers. The specific technologies used are as follows:

[0386] Natural language processing engine: Analyzes user questions and extracts key keywords. This uses technologies such as Google Cloud NLP API.

[0387] Emotion Recognition Engine: Analyzes emotions from the content of the user's question using technologies such as IBM Watson Tone Analyzer.

[0388] Legal knowledge base: Collects legal information and provides the required information based on queries. This can be done using APIs such as LexisNexis.

[0389] Advertisement database: Selects ads based on user sentiment and questions. This is done using Google Ads API, etc.

[0390] Legal Knowledge Base

[0391] A legal knowledge base is a database that comprehensively stores legal information and provides relevant information in response to queries. It is used by the server to retrieve appropriate legal information for a user's question by issuing a query based on keywords extracted from a natural language processing engine.

[0392] Advertising Database

[0393] The advertisement database stores relevant advertisements based on the user's question and sentiment, and the server selects appropriate advertisements from this database and embeds them into the generated answer.

[0394] Emotion Recognition Engine

[0395] The emotion recognition engine is used to analyze emotions from the user's text and voice, thereby personalizing responses to match the user's emotional state.

[0396] Operation flow

[0397] As an example, here is the prompt text when a user asks, "Can I get a refund for an item I purchased from your virtual store?"

[0398] Prompt Sentence Examples

[0399] A user asks: "Can I get a refund for an item I purchased from your virtual store?"

[0400] NLP analysis results:

[0401] Keywords: [Virtual store, purchase, product, refund, possible]

[0402] Emotion recognition:

[0403] User sentiment: [Anxiety]

[0404] Query the legal knowledge base:

[0405] SELECT FROM legal_knowledge_base WHERE keywords IN ('virtual store', 'purchase', 'refund') AND sentiment = 'anxiety'

[0406] Answer generation:

[0407] Refunds for items purchased from virtual stores are subject to the store's refund policy. Generally, refunds may be granted for unused items within 14 days of purchase. Please check the store's terms and conditions for details.

[0408] Ad Selection:

[0409] For a safer shopping experience, use our app to check refund policies before you buy. Download it here.

[0410] In this way, the present invention is a system that takes into consideration the user's feelings and provides specific and appropriate legal advice, thereby contributing to alleviating the user's anxieties and doubts.

[0411] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0412] Step 1:

[0413] The user enters a legal question into the terminal and presses the send button, which sends the entered question text (input: user's question text, output: data sent to the server).

[0414] Step 2:

[0415] The device sends the user's question to the server, which receives the data and passes it to a natural language processing engine (input: user's question text, output: data received by the server).

[0416] Step 3:

[0417] The server uses a natural language processing engine to analyze the received question and extract key keywords. Specifically, it uses the Google Cloud NLP API to analyze the question text and extract important keywords and phrases (input: question text received by the server, output: extracted keywords).

[0418] Step 4:

[0419] The server uses an emotion recognition engine to analyze the emotion in the user's question text. Specifically, it uses IBM Watson Tone Analyzer to determine the emotional state of the question text (anger, anxiety, joy, etc.) (input: question text, output: recognized emotional state).

[0420] Step 5:

[0421] The server generates a query to a legal knowledge base based on the extracted keywords and the recognized sentiment. The generated SQL query is sent to the legal knowledge base (e.g., LexisNexis API) to retrieve the relevant information (input: keywords and sentiment state, output: legal information query results).

[0422] Step 6:

[0423] The server generates a specific answer text to the user's question based on the obtained legal information, and adjusts the answer to include the most appropriate advice for the user by taking into account emotional information (input: legal information from the query result, output: generated answer text).

[0424] Step 7:

[0425] The server selects relevant ads from an advertising database based on the generated answer text. Specifically, it uses the Google Ads API or similar to search for and retrieve ads that match the user's emotions and question content (input: generated answer text and emotional state, output: selected ads).

[0426] Step 8:

[0427] The server combines the generated answer text and the associated advertisement to construct the final response data to send to the user (input: generated answer text and selected advertisement; output: final response data).

[0428] Step 9:

[0429] The server sends the constructed final response data to the user's terminal (input: final response data, output: data to be sent to the user's terminal).

[0430] Step 10:

[0431] The user's device receives the response data from the server and displays a notification to the user using the notification function (input: response data from the server, output: notification to the user).

[0432] Step 11:

[0433] The user taps the notification on their device, opens the application, and checks the answer and advertisement content (input: notification data, output: displayed answer and advertisement).

[0434] 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.

[0435] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0436] 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.

[0437] [Second embodiment]

[0438] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0439] 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.

[0440] 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).

[0441] 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.

[0442] 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.

[0443] 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).

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0449] 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."

[0450] The present invention is a system that receives a legal question from a user's terminal, analyzes the question, retrieves information from an appropriate legal knowledge base to generate an answer, and provides the answer to the user. Specific embodiments of this system are described below.

[0451] Overall system configuration

[0452] The system mainly consists of the following components:

[0453] 1. User's device (smartphone, PC, tablet, etc.)

[0454] 2. Server (accepts questions, analyzes them, queries the database, generates answers, and sends them)

[0455] 3. Legal knowledge base (database containing legal information)

[0456] 4. Advertising database (stores relevant advertisements)

[0457] Program processing

[0458] The following provides a detailed natural language explanation of the operation of the system.

[0459] Posting and Submitting Questions

[0460] The user launches the application using their device and enters a legal question, which is then transmitted from the user's device to the server.

[0461] Receiving and parsing questions

[0462] When the server receives a user's question, it uses a natural language processing (NLP) engine to analyze the question, extracting key keywords and phrases from the query and formulating a query against a legal knowledge base.

[0463] Querying the Knowledge Base

[0464] The server then submits the generated query to a legal knowledge base to search for relevant information. The information returned from the legal knowledge base includes legal guidelines, statutes, and precedents that are directly related to the query.

[0465] Generate answers

[0466] The server uses the information it receives to generate a clear answer to the user's question, including legal reasons and specific advice.

[0467] Ad selection and placement

[0468] Advertisements relevant to the generated answer are selected from an advertisement database. For example, for a question about copyright, an advertisement for a copyright management service is selected and appropriately embedded in the answer text.

[0469] Submit your answer

[0470] The server sends data containing the final answer and the selected advertisement to the user's device. The user's device receives this data and uses a notification function to notify the user that the answer has arrived. The user can open the application and view the answer and advertisement.

[0471] Specific examples

[0472] Example 1: YouTube music question

[0473] A user types the question "Is it illegal to use music from YouTube in my videos?" into their device and sends it. The server receives the question and extracts keywords such as "YouTube," "music," "video," and "illegal." It then searches a legal knowledge base based on these keywords, obtains information such as "YouTube music requires permission from the copyright holder," and generates an answer. It then selects an advertisement for a copyright management service and sends it to the user's device along with the answer. The user can review the answer and take further action if necessary.

[0474] Example 2: Question about the legality of downloads

[0475] A user types "Is it illegal to download movies from this site?" into their device and sends the question. The server receives the question and extracts keywords such as "site," "movie," "download," and "illegal." Based on these keywords, the server searches a legal knowledge base to obtain information such as "Under copyright law, downloading movies without permission is illegal," and generates an answer. An advertisement for a relevant copyright protection tool is selected and sent to the user's device along with the answer. The user can review the answer and take appropriate action.

[0476] In this way, the system of the present invention provides users with prompt and accurate information regarding legal questions they may have, helping them to act with confidence.

[0477] The processing flow will be explained below.

[0478] Step 1:

[0479] The device launches the application, and the user enters the legal question. The user taps the "Submit" button to submit the question.

[0480] Step 2:

[0481] The device sends the user's question to the server, including the question text, user ID, timestamp, and other metadata.

[0482] Step 3:

[0483] The server receives the request data from the terminal, checks the consistency of the data format, and verifies that the data has been received correctly.

[0484] Step 4:

[0485] The server passes the received question to a natural language processing (NLP) engine for analysis, which analyzes the question and extracts key keywords and related phrases.

[0486] Step 5:

[0487] The server generates a query to the legal knowledge base based on the extracted keywords, and uses the generated query to query the legal knowledge base.

[0488] Step 6:

[0489] The legal knowledge base responds to queries with relevant information, which the server receives and uses to answer the user's questions.

[0490] Step 7:

[0491] Based on the information obtained, the server generates a specific response text, including any necessary legal justification or advice.

[0492] Step 8:

[0493] The server selects advertisements relevant to the answer from an advertisement database and embeds the selected advertisements appropriately in the answer text.

[0494] Step 9:

[0495] The server constructs response data containing the final answer and advertisement, and sends the response data to the user's device.

[0496] Step 10:

[0497] The device receives the response data from the server and uses the notification function to notify the user that a response has been received.

[0498] Step 11:

[0499] Users tap the notification on their device to open the application, view the answer and the advertisement, and then take appropriate action.

[0500] Example 1

[0501] 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."

[0502] In today's world, systems that provide fast and accurate answers to legal problems and questions faced by users are not yet fully developed. This makes it difficult for users to obtain reliable answers to their legal questions and requires them to go to the trouble of searching for information themselves. Furthermore, there is a need for systems that provide relevant advertising information and provide useful support to users.

[0503] 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.

[0504] In this invention, the server includes a means for receiving legal questions from a user's device, a means for analyzing the received questions and extracting key keywords, and a means for querying a legal knowledge base based on the extracted keywords to retrieve relevant information. This allows users to easily enter legal questions and obtain answers quickly and accurately. The server also includes a means for generating clear answers using a generative AI model, a means for embedding advertisements related to the generated answers based on relevance, and a means for notifying the user of the receipt of the answers using a notification function on the user's device. This allows users to simultaneously obtain necessary legal information as well as information on related services and products, realizing a comprehensive support system.

[0505] "User device" refers to electronic devices such as smartphones, computers, and tablets.

[0506] "Legal Questions" refers to questions or inquiries about the law.

[0507] "Means for receiving" refers to the function of receiving data from the user's terminal.

[0508] "Means of analyzing and extracting key keywords" refers to the process of using text analysis technology to identify important keywords from the question content.

[0509] "Legal knowledge base" refers to a database containing information related to the law.

[0510] "Means of submitting a query and retrieving relevant information" refers to the ability to send a specific query to a database and retrieve relevant information.

[0511] "Means of generating an answer based on retrieved information" refers to the process of using information obtained from a database to create an answer to a question.

[0512] "Means for selecting and embedding advertisements relevant to the generated answer" refers to a function for selecting advertisements relevant to the answer content and appropriately placing them within the answer text.

[0513] "Means for sending answers to the user's terminal" refers to the function of sending the generated answers and advertisements to the user's terminal.

[0514] "Generative AI model" refers to a technology that uses a large-scale artificial intelligence model to generate natural-language output for specific inputs.

[0515] "Means for notifying the receipt of a response using a notification function" refers to a function for notifying the user's terminal that a new response has arrived.

[0516] The present invention is a system that receives a legal question from a user terminal, analyzes the question, obtains information from an appropriate legal knowledge base to generate an answer, and provides the answer to the user. The following specific embodiments for implementing this system will be described.

[0517] The overall system configuration includes the following elements:

[0518] 1. User's device (smartphone, PC, tablet, etc.)

[0519] 2. Server (accepts questions, analyzes them, queries the database, generates answers, and sends them)

[0520] 3. Legal knowledge base (database containing legal information)

[0521] 4. Advertising database (stores relevant advertisements)

[0522] First, a user launches the application on their device and enters a legal question, which is then sent from the user's device to the server. Specifically, the user enters a question such as, "Is it illegal to use music from YouTube in my videos?"

[0523] When the server receives the user's question, it analyzes the content of the question using a natural language processing (NLP) engine. Examples of software used include Python's NLTK and spaCy. Based on the key keywords and phrases extracted through this analysis, a query is created against the legal knowledge base. For example, keywords such as "YouTube," "music," "video," and "illegal" are extracted.

[0524] The server then submits the generated query to a legal knowledge base to search for relevant information, which returns information such as legal guidelines, statutes, and case law that is directly related to the question, using software such as Elasticsearch and SQL databases.

[0525] The server then uses the information it has acquired to generate a clear answer to the user's question, using a generative AI model (e.g., GPT-3). An example prompt might look like this:

[0526] "What is the best response in this legal situation?"

[0527] Based on the information obtained and the prompt, the generative AI model generates an answer such as "YouTube requires permission from the copyright holder to use music."

[0528] Advertisements related to the generated answer are selected from an advertisement database. Based on the question and answer, highly relevant advertisements are selected and pasted into the answer. For example, an advertisement for a "copyright management service" is selected.

[0529] Finally, the server sends the final answer and the selected ad to the user's device using a communication protocol such as HTTPS.

[0530] The user's device displays the received data within the application and uses a notification function to notify the user that a response has arrived. The user can then open the application to view the response and advertisement.

[0531] This allows users to obtain fast and accurate information on legal questions, as well as information on related services and products, providing comprehensive support.

[0532] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0533] Step 1:

[0534] A user launches the application using their device and enters a legal question. This question is sent from the user's device to the server. The input is a textual question. Specifically, the user enters a question such as "Is it illegal to use music from YouTube in my video?" and presses the submit button. The output is the question data sent to the server.

[0535] Step 2:

[0536] The server receives the user's question and analyzes the question using a natural language processing (NLP) engine. The user's question data is obtained as input. To process the data, the NLP engine (e.g., spaCy) analyzes the text and extracts key keywords such as "YouTube," "music," "video," and "illegal." Specifically, the server passes the question received to the NLP engine, which then performs keyword extraction. A list of extracted keywords is generated as output.

[0537] Step 3:

[0538] The server queries the legal knowledge base based on the extracted keywords. A keyword list is obtained as input. As data calculation, a query statement is generated based on the keywords and submitted to Elasticsearch or an SQL database. Specifically, the server generates a query using the keywords "YouTube," "music," "video," and "illegal" and queries the database. As output, the legal knowledge base returns the relevant legal information.

[0539] Step 4:

[0540] The server generates an answer to the user's question based on the legal information it has acquired. The input is legal information acquired from a database. Data processing involves inputting the information into a generative AI model (e.g., GPT-3) along with a prompt, which generates an answer in natural language. An example of a prompt is "What is the best response in this legal situation?" Specific operations involve inputting the legal information acquired by the server into the AI ​​model, which then generates an answer. The output is an answer written in natural language.

[0541] Step 5:

[0542] Advertisements relevant to the generated answer are selected from an advertisement database and embedded in the answer text. The generated answer sentence is obtained as input. Data calculation involves searching for advertisements that are highly relevant based on the answer content and inserting them in the appropriate position. In specific operations, the server selects relevant advertisements based on the answer sentence from the database and embeds the advertisements in the answer text. The output is the final answer sentence with the advertisements embedded.

[0543] Step 6:

[0544] The server sends the completed answer and advertisement to the user's device. The final answer is obtained as input. Data processing involves generating a data packet for transmission and sending it using a secure communication protocol (e.g., HTTPS). Specifically, the server sends data including the answer and advertisement to the user's device. The user's device receives the data as output.

[0545] Step 7:

[0546] The user's device displays the received answer and advertisement and notifies the user. The input is the data sent from the server. The data is processed by converting the answer and advertisement into a display format and displaying them on the device's UI. The specific operation is that the device notifies the user that "a new answer is available," and the user opens the application to check the answer and advertisement. The output is a state in which the user can check the answer and advertisement.

[0547] (Application example 1)

[0548] 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."

[0549] Physical stores lack the means to quickly and accurately respond to legal issues faced by staff and owners. Questions about laws such as the Consumer Protection Act and the Labor Standards Act often arise in daily operations, making it difficult to obtain appropriate answers on the spot. This raises concerns about business delays and increased legal risks.

[0550] 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.

[0551] In this invention, the server includes means for receiving legal questions from a user's device, means for analyzing the received questions and extracting key keywords, means for querying a legal knowledge base based on the extracted keywords and retrieving relevant information, means for selecting and embedding advertisements related to the generated answers, means for enabling store staff to ask legal questions using smartphones, means for providing legal information related to the Consumer Protection Act and the Labor Standards Act, and means for transmitting answers to the user's device, thereby enabling stores to quickly and accurately respond to legal issues.

[0552] "User device" refers to an information device used to input and send legal questions, including smartphones, PCs, tablets, etc.

[0553] "Means for receiving" refers to the function of receiving legal questions sent from the user's terminal on the server.

[0554] "Means for analyzing and extracting key keywords" refers to the function of analyzing received legal questions using a natural language processing engine and identifying important keywords.

[0555] "Means of querying a legal knowledge base and retrieving relevant information" refers to the function of querying a legal knowledge base based on extracted keywords and searching for and retrieving relevant legal information.

[0556] "Means for generating answers" refers to the functionality for generating answers to users' questions based on the acquired legal information.

[0557] "Means for selecting and embedding relevant advertisements" refers to a function for selecting from an advertisement database the advertisement most relevant to the generated answer and appropriately placing it within the answer.

[0558] "Means enabling store staff to use their smartphones to ask legal questions" refers to functionality that enables store staff to use their smartphones to submit legal questions to the system.

[0559] "Means of providing legal information on the Consumer Protection Act and the Labor Standards Act" refers to the function of providing information on the Consumer Protection Act and the Labor Standards Act that is related to the content of the question.

[0560] "Means for sending answers to the user's terminal" refers to a function for sending the generated answers to the user's terminal and allowing the user to check the answers.

[0561] MODE FOR CARRYING OUT THE INVENTION

[0562] The present invention is a system in which users input legal questions, and the server analyzes and generates answers. The system aims to provide store staff with fast and accurate information on legal issues they face in the field. The present invention is implemented in the following configuration.

[0563] Overall system configuration

[0564] The system mainly consists of the following components:

[0565] 1. User's device: An information device used to input and submit legal inquiries, including smartphones, PCs, tablets, etc.

[0566] 2. Server: Accepts questions, analyzes them, queries the database, generates answers, and sends them.

[0567] 3. Legal Knowledge Base: A database containing information about the law.

[0568] 4. Advertisement database: stores relevant advertisements.

[0569] Program processing

[0570] Posting and Submitting Questions

[0571] The user launches the application using their device and enters a legal question, which is then sent from the user's device to the server.

[0572] Receiving and parsing questions

[0573] Once the server receives the user's question, it analyzes it using a natural language processing (NLP) engine, such as SpaCy or NLTK, and uses the key keywords and phrases extracted from the analysis to formulate a query against a legal knowledge base.

[0574] Querying the Knowledge Base

[0575] The server then submits the generated query to a legal knowledge base, which stores a wide range of legal information, including consumer protection laws, labor standards laws, and copyright laws, and returns information such as specific legal guidelines, provisions, and precedents.

[0576] Generate answers

[0577] The server uses the information it receives to generate a clear answer to the user's question, including legal reasons and specific advice.

[0578] Ad selection and placement

[0579] Advertisements relevant to the generated answer are selected from an advertisement database and appropriately embedded in the answer text, for example, an advertisement for a complaint management service is selected for a consumer protection question.

[0580] Submit your answer

[0581] The server sends data containing the final answer and the selected advertisement to the user's device. The user's device receives this data and uses a notification function to notify the user that the answer has arrived. The user can open the application and view the answer and advertisement.

[0582] Specific examples

[0583] Specific examples are shown below.

[0584] Example 1: When a store staff member asks on their smartphone, "A consumer has requested a return. How should I handle this?" The server extracts keywords such as "return," "consumer," and "response" and retrieves information on the applicable consumer protection law. It then provides appropriate guidelines and simultaneously displays an advertisement for a complaint management service.

[0585] Example prompt sentence:

[0586] "What are the Consumer Protection Act guidelines regarding returns?"

[0587] This system will enable legal issues to be dealt with quickly and accurately even in physical stores.

[0588] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0589] Step 1:

[0590] The terminal provides an interface for users to input legal questions. The input content is a specific legal question, such as "A consumer has requested a return. How should I respond?" When the user inputs a question and presses the send button, the terminal sends the question data to the server.

[0591] Step 2:

[0592] The server receives legal question data sent from the user's device. The input includes the user's question text data. The server inputs this received data into a natural language processing (NLP) engine (e.g., SpaCy, NLTK) to analyze the question content. Specific processing includes sentence tokenization, semantic analysis, and keyword extraction. The output is a list of analyzed keywords.

[0593] Step 3:

[0594] The server generates a query to the legal knowledge base based on the keyword list extracted by the analysis. The keyword list obtained in step 2 is used as input. As a specific example of query generation, an SQL query to search for related legal information is constructed using keywords such as "consumer," "return," and "response." The generated query is obtained as output.

[0595] Step 4:

[0596] The server submits the generated query to a legal knowledge base to retrieve the relevant legal information. The generated query is used as input. A database engine (e.g., MySQL, PostgreSQL) is used to execute the query and search for relevant legal provisions and guidelines. The retrieved legal information is the output.

[0597] Step 5:

[0598] The server generates an answer to the user's question based on the acquired legal information. The legal information acquired in step 4 is used as input. Specifically, the server constructs an answer text to the user's question based on the legal information. The generated answer text is obtained as output.

[0599] Step 6:

[0600] The server selects advertisements from an advertisement database that are relevant to the generated answer and embeds them appropriately in the answer text. Keywords related to the generated answer text are used as input. A database query is used to search the advertisement database to select relevant advertisements, such as an advertisement for a "claim management service." The output is the answer text with the embedded advertisement.

[0601] Step 7:

[0602] The server sends data including the final answer and the selected advertisement to the user's device. The answer text with the embedded advertisement obtained in step 6 is used as input. The server sends this to the user's device, and the device uses a notification function to notify the user that the answer has arrived. The user can open the application and check the answer and advertisement. The output is the answer data that is displayed on the user's device.

[0603] The above are the specific processing steps of the system that realizes the application example.

[0604] 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.

[0605] The present invention is a system that receives legal questions from a user's device, analyzes the questions, retrieves information from an appropriate legal knowledge base to generate answers, and further combines it with an emotion engine that recognizes the user's emotions to provide more personalized answers and advertisements. Specific embodiments of this system are described below.

[0606] Overall system configuration

[0607] The system mainly consists of the following components:

[0608] 1. User's device (smartphone, PC, tablet, etc.)

[0609] 2. Server (accepts questions, analyzes them, recognizes emotions, queries the database, generates answers, and sends them)

[0610] 3. Legal knowledge base (database containing legal information)

[0611] 4. Advertising database (stores relevant advertisements)

[0612] 5. Emotion engine (recognizes user emotions and reflects them in analysis)

[0613] Program processing

[0614] The following provides a detailed natural language explanation of the operation of the system.

[0615] Posting and Submitting Questions

[0616] The user launches the application using their device and enters a legal question, which is then transmitted from the user's device to the server.

[0617] Receiving and parsing questions

[0618] The device sends the user's question to the server, which then uses a natural language processing (NLP) engine to analyze the question and extract key keywords and phrases.

[0619] Emotion recognition

[0620] The server uses an emotion engine to recognize emotions from the user's input. This emotion information is then associated with the analysis results of the question. The emotion engine determines emotions such as anger, sadness, joy, and surprise based on the user's vocabulary and sentence structure.

[0621] Querying the Knowledge Base

[0622] The server generates a query to the legal knowledge base based on the extracted keywords and the recognized sentiment, and uses the generated query to query the legal knowledge base.

[0623] Obtaining information and generating answers

[0624] The legal knowledge base responds to queries with relevant information. The server receives the information and generates a specific answer text tailored to the user's question and sentiment. The answer includes the necessary legal reasoning and advice.

[0625] Emotion-based ad selection

[0626] The server selects relevant ads from an advertising database based on the user's emotions, and embeds them in the answer depending on the user's emotions. For example, if the user is angry, an ad that conveys a more calming or reassuring feeling will be selected.

[0627] Submit your answer

[0628] The server constructs response data containing the final answer and advertisement, which is then sent to the user's device.

[0629] Receiving and notifying responses

[0630] The device receives the response data from the server and uses the notification function to notify the user that a response has been received.

[0631] Show Answers

[0632] Users tap the notification on their device, open the application, and view the response and advertisement content, which they can then take appropriate action based on.

[0633] Specific examples

[0634] Example 1: YouTube music question

[0635] The user types "Is it illegal to use music from YouTube in my videos?" into their device and sends the question. The server receives this question and extracts keywords such as "YouTube," "music," "video," and "illegal." At the same time, the emotion engine recognizes that the user is feeling anxious. Based on these keywords and emotion information, the server searches a legal knowledge base, obtains the information that "using music from YouTube requires permission from the copyright holder," and generates an answer. An advertisement for a copyright management service that gives a sense of security in line with the emotion is selected and sent to the user's device along with the answer. The user can check the answer and take the next step with peace of mind.

[0636] Example 2: Question about the legality of downloads

[0637] The user types "Is it illegal to download movies from this site?" into their device and sends the question. The server receives this question and extracts keywords such as "site," "movie," "download," and "illegal." At the same time, the emotion engine recognizes that the user has doubts. Based on these keywords and emotion information, the server searches the legal knowledge base to obtain the information that "Under copyright law, downloading movies without permission is illegal," and generates an answer. An advertisement for a related copyright protection tool is selected and sent to the user's device along with the answer. The user can check the answer and take appropriate action.

[0638] In this way, the system of the present invention can provide prompt and accurate information to answer legal questions while taking into consideration the user's feelings, allowing the user to receive answers that take their feelings into consideration and act with peace of mind.

[0639] The processing flow will be explained below.

[0640] Step 1:

[0641] The device launches the application, and the user enters the legal question. The user taps the "Submit" button to submit the question.

[0642] Step 2:

[0643] The device sends the user's question to the server, including the question text, user ID, timestamp, and other metadata.

[0644] Step 3:

[0645] The server receives the request data from the terminal. The server checks the consistency of the data format and verifies that the data has been received correctly.

[0646] Step 4:

[0647] The server passes the received question to a natural language processing (NLP) engine, which analyzes the question and extracts key keywords and phrases.

[0648] Step 5:

[0649] The server uses an emotion engine to recognize emotions from the user's input, and associates the recognized emotions with the analysis results of the question.

[0650] Step 6:

[0651] The server generates queries to a legal knowledge base based on the extracted keywords and the recognized sentiments.

[0652] Step 7:

[0653] The server queries the generated query against a legal knowledge base to retrieve relevant information.

[0654] Step 8:

[0655] Based on the information acquired, the server generates a specific answer text based on the user's question and the perceived sentiment, including any necessary legal justification or advice.

[0656] Step 9:

[0657] The server selects advertisements from an advertisement database that are relevant to the answer, and the relevant advertisements are selected based on the recognized sentiment.

[0658] Step 10:

[0659] The server appropriately embeds the selected advertisement into the answer text to construct the final response data.

[0660] Step 11:

[0661] The server sends the response data (answers and advertisements) to the user's terminal.

[0662] Step 12:

[0663] The device receives the response data from the server and uses the notification function to notify the user that a response has been received.

[0664] Step 13:

[0665] Users tap the notification on their device, open the application, and view the response and advertisement content, allowing them to take appropriate action.

[0666] Example 2

[0667] 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."

[0668] Conventional legal question-answering systems were unable to consider the user's emotions when providing appropriate answers to legal questions entered by the user, making it difficult to provide answers that give the user a sense of satisfaction and security.In addition, the selection of advertisements was not based on the user's emotions, making it difficult to improve the quality of the user experience.

[0669] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0670] In this invention, the server includes means for receiving a legal question from a user's terminal, means for analyzing the received question and extracting key keywords and the user's emotions, means for generating a query to a legal knowledge base based on the extracted keywords and emotions and acquiring relevant information, means for generating an answer based on the acquired information and emotions, means for selecting and embedding an advertisement based on the emotion associated with the generated answer, means for transmitting the answer and advertisement to the user's terminal, and means for notifying the user's terminal of the receipt of the answer using a notification function, thereby enabling the user to receive personalized answers and advertisements according to their emotions.

[0671] "User device" refers to a device such as a smartphone, computer, or tablet that a user uses to enter a question.

[0672] A "Legal Question" is text information entered by a user about a legal question or issue.

[0673] "Analysis" is the process of analyzing the user-entered question and extracting key keywords and sentiments.

[0674] "Keywords" are important words and phrases extracted to understand the content of a question and generate an appropriate answer.

[0675] "Emotion" refers to emotional information that can be read from the user's input, and includes, for example, anxiety, doubt, anger, joy, etc.

[0676] A "legal knowledge base" is a database that stores legal information and provides relevant information in response to queries.

[0677] A "query" is a search inquiry against the legal knowledge base, generated based on the extracted keywords and sentiment.

[0678] "Answer generation" is the process of creating specific answer texts to users' questions based on the acquired legal and emotional information.

[0679] "Advertising" refers to promotional information for related products and services that are provided based on the user's interests and emotions.

[0680] "Sending" refers to the act of sending responses and advertisements as data from the server to the user's terminal.

[0681] The "notification function" is an application function that notifies the user's device that a new answer has arrived.

[0682] The present invention is a system that receives legal questions from a user's device, analyzes the questions, retrieves information from an appropriate legal knowledge base to generate answers, and further combines it with an emotion engine that recognizes the user's emotions to provide more personalized answers and advertisements. Specific embodiments of this system are described below.

[0683] Hardware and software used

[0684] This system is implemented using the following hardware and software:

[0685] User devices: smartphones, computers, tablets, etc.

[0686] Server: A high-performance computer that accepts questions, analyzes them, recognizes emotions, queries the database, generates answers, and sends them.

[0687] Legal Knowledge Base: A database containing legal information (MySQL, MongoDB, etc.)

[0688] Advertisement database: stores relevant advertisements

[0689] Natural Language Processing (NLP) engines: spaCy, Google Cloud NLP API, etc.

[0690] Sentiment engine: Microsoft Azure's Text Analytics API, IBM Watson's Tone Analyzer, etc.

[0691] Specific operation of the system

[0692] Posting and Submitting Questions

[0693] The user launches the application using their device and enters a legal question, which is then transmitted from the user's device to the server.

[0694] Receiving and parsing questions

[0695] The device sends the user's question to the server, which then uses a natural language processing (NLP) engine to analyze the question and extract key keywords and phrases.

[0696] Emotion recognition

[0697] The server uses an emotion engine to recognize emotions from the user's input. This emotion information is then associated with the analysis results of the question. The emotion engine determines emotions such as anger, sadness, joy, and surprise based on the user's vocabulary and sentence structure.

[0698] Querying the Knowledge Base

[0699] The server generates a query to the legal knowledge base based on the extracted keywords and the recognized sentiment, and uses the generated query to query the legal knowledge base.

[0700] Obtaining information and generating answers

[0701] The legal knowledge base responds to queries with relevant information. The server receives the information and generates a specific answer text tailored to the user's question and sentiment. The answer includes the necessary legal reasoning and advice.

[0702] Emotion-based ad selection

[0703] The server selects relevant ads from an advertising database based on the user's emotions, and embeds them in the answer depending on the user's emotions. For example, if the user is angry, an ad that conveys a more calming or reassuring feeling will be selected.

[0704] Submit your answer

[0705] The server constructs response data containing the final answer and advertisement, which is then sent to the user's device.

[0706] Receiving and notifying responses

[0707] The device receives the response data from the server and uses the notification function to notify the user that a response has been received.

[0708] Show Answers

[0709] Users tap the notification on their device, open the application, and view the response and advertisement content, which they can then take appropriate action based on.

[0710] Specific examples

[0711] Example 1: YouTube music question

[0712] The user types "Is it illegal to use music from YouTube in my videos?" into their device and sends the question. The server receives this question and extracts keywords such as "YouTube," "music," "video," and "illegal." At the same time, the emotion engine recognizes that the user is feeling anxious. Based on these keywords and emotion information, the server searches a legal knowledge base, obtains the information that "using music from YouTube requires permission from the copyright holder," and generates an answer. An advertisement for a copyright management service that gives a sense of security in line with the emotion is selected and sent to the user's device along with the answer. The user can check the answer and take the next step with peace of mind.

[0713] Example 2: Question about the legality of downloads

[0714] The user types "Is it illegal to download movies from this site?" into their device and sends the question. The server receives this question and extracts keywords such as "site," "movie," "download," and "illegal." At the same time, the emotion engine recognizes that the user has doubts. Based on these keywords and emotion information, the server searches the legal knowledge base to obtain the information that "Under copyright law, downloading movies without permission is illegal," and generates an answer. An advertisement for a related copyright protection tool is selected and sent to the user's device along with the answer. The user can check the answer and take appropriate action.

[0715] Prompt Sentence Examples

[0716] For example, a prompt for a generative AI model might look like this:

[0717] "Analyze the legal question entered by the user and generate relevant answers and sentiment-based ads, but also provide the sentiment the user is feeling about the question."

[0718] This system allows users to receive fast, accurate, and emotionally sensitive legal information, allowing them to take their next steps with confidence.

[0719] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0720] Step 1:

[0721] The user uses the terminal to launch the application and enter the legal questions.

[0722] What it does: A user launches the application and types a question into the question field, for example, "Is it illegal to use music from YouTube in my videos?"

[0723] Input: Legal question text.

[0724] Output: The question data sent from the device to the server.

[0725] Step 2:

[0726] The terminal transmits the entered question to the server.

[0727] Specific operation: The device sends the question text to the server as an HTTP POST request.

[0728] Input: The question text entered on the terminal.

[0729] Output: The HTTP request sent to the server.

[0730] Step 3:

[0731] The server analyzes the received question using a natural language processing (NLP) engine and extracts key keywords.

[0732] Specific operation: The server uses an NLP engine (e.g., spaCy) to extract the main keywords: "YouTube," "music," "video," and "illegal."

[0733] Input: The question text in the HTTP request.

[0734] Output: A list of extracted main keywords.

[0735] Step 4:

[0736] The server uses an emotion engine to recognize emotions from user input.

[0737] Specific operation: The server runs an emotion engine (for example, Microsoft Azure's Text Analytics API) and determines the user's emotion as "anxiety."

[0738] Input: Question text.

[0739] Output: Recognized emotion information.

[0740] Step 5:

[0741] The server generates a query to a legal knowledge base based on the extracted keywords and sentiment information.

[0742] What it does: The server generates a MySQL database query to search the legal knowledge base for the keywords "YouTube," "music," "video," and "illegal."

[0743] Input: Extracted keywords and sentiment information.

[0744] Output: A query against the legal knowledge base.

[0745] Step 6:

[0746] It retrieves information from a legal knowledge base in response to a query and generates a specific answer text.

[0747] Specific operation: The legal knowledge base returns the information that "YouTube requires permission from the copyright holder to use music," and the server creates an answer text based on this information.

[0748] Input: The generated query.

[0749] Output: The specific answer text.

[0750] Step 7:

[0751] The server selects an appropriate advertisement from an advertisement database according to the emotion information.

[0752] Specific operation: The server recognizes that the emotion is "anxiety" and selects an advertisement for a copyright management service that gives a sense of security.

[0753] Input: Recognized emotion information.

[0754] Output: The selected ad.

[0755] Step 8:

[0756] The server sends the final answer and advertisement to the user's terminal.

[0757] Specific operation: The server combines the selected advertisement and answer and sends it to the user's device as an HTTP response.

[0758] Input: Answer text and selected ad.

[0759] Output: The HTTP response sent to the user's device.

[0760] Step 9:

[0761] The user's terminal receives the response data from the server and notifies the user.

[0762] Specific behavior: The device displays a push notification to inform the user that a response has been received.

[0763] Input: HTTP response data from the server.

[0764] Output: A push notification to the user.

[0765] Step 10:

[0766] Users tap the notification to open the application and view the answers and advertisements.

[0767] What happens: The user sees the answer "YouTube music usage requires permission from the copyright holder" within the application, along with an advertisement for a related copyright management service.

[0768] Input: Answers and ad data displayed by user actions.

[0769] Output: The answer and advertisement that the user sees.

[0770] (Application example 2)

[0771] 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."

[0772] Current virtual store legal consultation systems are unable to provide answers that take users' emotions into account and are unable to meet users' needs for personalized advice. As a result, users are unable to receive appropriate advice and find it difficult to resolve their legal questions and concerns. Furthermore, they are unable to select relevant advertisements based on users' emotions, which reduces the quality of the user experience.

[0773] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a legal question from a user's terminal, means for analyzing the received question and extracting key keywords, means for querying a legal knowledge base based on the extracted keywords and obtaining relevant information, means for generating an answer based on the obtained information, means for recognizing the user's emotions, means for personalizing the answer based on the recognized emotions, means for selecting and embedding advertisements related to the generated answer, and means for transmitting the answer to the user's terminal. This makes it possible to provide personalized legal advice that takes user emotions into consideration and to appropriately select relevant advertisements.

[0774] "User device" refers to an electronic device, such as a smartphone, tablet, head-mounted display, or personal computer, that a user uses to input and receive questions.

[0775] A "legal question" is text or voice input that a user makes into a device to request information or advice about a legal matter.

[0776] "Analysis" is the process of categorizing incoming legal questions using natural language processing techniques and extracting key keywords and phrases.

[0777] "Keywords" are words or phrases that are particularly important in a legal question and form the basis for querying a legal knowledge base.

[0778] A "legal knowledge base" is a database that systematically collects legal information and provides relevant legal information in response to a query.

[0779] A "query" refers to an inquiry made to a legal knowledge base to obtain information.

[0780] "Retrieving information" means querying a legal knowledge base to retrieve relevant law or advice and gathering it to answer the user's question.

[0781] "Generating an answer" means creating a specific and appropriate response to a user's question based on the legal information obtained.

[0782] "Emotion recognition" means using natural language processing and artificial intelligence technology to determine a user's emotional state from their questions and voice.

[0783] "Personalizing" means tailoring responses to the user's emotions and specific circumstances to provide individually optimized answers.

[0784] "Selecting ads" means selecting relevant ads from an ad database based on the user's emotions and questions.

[0785] "Embedding advertisements in answers" means placing selected advertisements in appropriate positions within the generated legal answer text.

[0786] "Sending an answer" refers to the process of sending the generated answer and related advertisement to the user's terminal using telecommunications means.

[0787] The present invention provides a legal consultation assistant system that allows users to obtain immediate and personalized answers to legal questions in a virtual store. A detailed description of specific embodiments of the system is provided below.

[0788] Overall system configuration

[0789] The system consists of the following components:

[0790] 1. User's Device

[0791] 2. Server

[0792] 3. Legal Knowledge Base

[0793] 4. Advertising Database

[0794] 5. Emotion Recognition Engine

[0795] User's device

[0796] The user's device is an electronic device such as a smartphone, tablet, or head-mounted display. An application is installed on the device that allows the user to enter legal questions. The application includes a text box for entering questions and voice recognition capabilities.

[0797] server

[0798] The server receives legal questions sent from the user's device, analyzes them, recognizes emotions, generates queries to a legal knowledge base to obtain information, and finally performs a series of processes to generate personalized answers. The specific technologies used are as follows:

[0799] Natural language processing engine: Analyzes user questions and extracts key keywords. This uses technologies such as Google Cloud NLP API.

[0800] Emotion Recognition Engine: Analyzes emotions from the content of the user's question using technologies such as IBM Watson Tone Analyzer.

[0801] Legal knowledge base: Collects legal information and provides the required information based on queries. This can be done using APIs such as LexisNexis.

[0802] Advertisement database: Selects ads based on user sentiment and questions. This is done using Google Ads API, etc.

[0803] Legal Knowledge Base

[0804] A legal knowledge base is a database that comprehensively stores legal information and provides relevant information in response to queries. It is used by the server to retrieve appropriate legal information for a user's question by issuing a query based on keywords extracted from a natural language processing engine.

[0805] Advertising Database

[0806] The advertisement database stores relevant advertisements based on the user's question and sentiment, and the server selects appropriate advertisements from this database and embeds them into the generated answer.

[0807] Emotion Recognition Engine

[0808] The emotion recognition engine is used to analyze emotions from the user's text and voice, thereby personalizing responses to match the user's emotional state.

[0809] Operation flow

[0810] As an example, here is the prompt text when a user asks, "Can I get a refund for an item I purchased from your virtual store?"

[0811] Prompt Sentence Examples

[0812] A user asks: "Can I get a refund for an item I purchased from your virtual store?"

[0813] NLP analysis results:

[0814] Keywords: [Virtual store, purchase, product, refund, possible]

[0815] Emotion recognition:

[0816] User sentiment: [Anxiety]

[0817] Query the legal knowledge base:

[0818] SELECT FROM legal_knowledge_base WHERE keywords IN ('virtual store', 'purchase', 'refund') AND sentiment = 'anxiety'

[0819] Answer generation:

[0820] Refunds for items purchased from virtual stores are subject to the store's refund policy. Generally, refunds may be granted for unused items within 14 days of purchase. Please check the store's terms and conditions for details.

[0821] Ad Selection:

[0822] For a safer shopping experience, use our app to check refund policies before you buy. Download it here.

[0823] In this way, the present invention is a system that takes into consideration the user's feelings and provides specific and appropriate legal advice, thereby contributing to alleviating the user's anxieties and doubts.

[0824] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0825] Step 1:

[0826] The user enters a legal question into the terminal and presses the send button, which sends the entered question text (input: user's question text, output: data sent to the server).

[0827] Step 2:

[0828] The device sends the user's question to the server, which receives the data and passes it to a natural language processing engine (input: user's question text, output: data received by the server).

[0829] Step 3:

[0830] The server uses a natural language processing engine to analyze the received question and extract key keywords. Specifically, it uses the Google Cloud NLP API to analyze the question text and extract important keywords and phrases (input: question text received by the server, output: extracted keywords).

[0831] Step 4:

[0832] The server uses an emotion recognition engine to analyze the emotion in the user's question text. Specifically, it uses IBM Watson Tone Analyzer to determine the emotional state of the question text (anger, anxiety, joy, etc.) (input: question text, output: recognized emotional state).

[0833] Step 5:

[0834] The server generates a query to a legal knowledge base based on the extracted keywords and the recognized sentiment. The generated SQL query is sent to the legal knowledge base (e.g., LexisNexis API) to retrieve the relevant information (input: keywords and sentiment state, output: legal information query results).

[0835] Step 6:

[0836] The server generates a specific answer text to the user's question based on the obtained legal information, and adjusts the answer to include the most appropriate advice for the user by taking into account emotional information (input: legal information from the query result, output: generated answer text).

[0837] Step 7:

[0838] The server selects relevant ads from an advertising database based on the generated answer text. Specifically, it uses the Google Ads API or similar to search for and retrieve ads that match the user's emotions and question content (input: generated answer text and emotional state, output: selected ads).

[0839] Step 8:

[0840] The server combines the generated answer text and the associated advertisement to construct the final response data to send to the user (input: generated answer text and selected advertisement; output: final response data).

[0841] Step 9:

[0842] The server sends the constructed final response data to the user's terminal (input: final response data, output: data to be sent to the user's terminal).

[0843] Step 10:

[0844] The user's device receives the response data from the server and displays a notification to the user using the notification function (input: response data from the server, output: notification to the user).

[0845] Step 11:

[0846] The user taps the notification on their device, opens the application, and checks the answer and advertisement content (input: notification data, output: displayed answer and advertisement).

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

[0848] 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.

[0849] 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.

[0850] [Third embodiment]

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

[0852] 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.

[0853] 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).

[0854] 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.

[0855] 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.

[0856] 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).

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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."

[0863] The present invention is a system that receives a legal question from a user's terminal, analyzes the question, retrieves information from an appropriate legal knowledge base to generate an answer, and provides the answer to the user. Specific embodiments of this system are described below.

[0864] Overall system configuration

[0865] The system mainly consists of the following components:

[0866] 1. User's device (smartphone, PC, tablet, etc.)

[0867] 2. Server (accepts questions, analyzes them, queries the database, generates answers, and sends them)

[0868] 3. Legal knowledge base (database containing legal information)

[0869] 4. Advertising database (stores relevant advertisements)

[0870] Program processing

[0871] The following provides a detailed natural language explanation of the operation of the system.

[0872] Posting and Submitting Questions

[0873] The user launches the application using their device and enters a legal question, which is then transmitted from the user's device to the server.

[0874] Receiving and parsing questions

[0875] When the server receives a user's question, it uses a natural language processing (NLP) engine to analyze the question, extracting key keywords and phrases from the query and formulating a query against a legal knowledge base.

[0876] Querying the Knowledge Base

[0877] The server then submits the generated query to a legal knowledge base to search for relevant information. The information returned from the legal knowledge base includes legal guidelines, statutes, and precedents that are directly related to the query.

[0878] Generate answers

[0879] The server uses the information it receives to generate a clear answer to the user's question, including legal reasons and specific advice.

[0880] Ad selection and placement

[0881] Advertisements relevant to the generated answer are selected from an advertisement database. For example, for a question about copyright, an advertisement for a copyright management service is selected and appropriately embedded in the answer text.

[0882] Submit your answer

[0883] The server sends data containing the final answer and the selected advertisement to the user's device. The user's device receives this data and uses a notification function to notify the user that the answer has arrived. The user can open the application and view the answer and advertisement.

[0884] Specific examples

[0885] Example 1: YouTube music question

[0886] A user types the question "Is it illegal to use music from YouTube in my videos?" into their device and sends it. The server receives the question and extracts keywords such as "YouTube," "music," "video," and "illegal." It then searches a legal knowledge base based on these keywords, obtains information such as "YouTube music requires permission from the copyright holder," and generates an answer. It then selects an advertisement for a copyright management service and sends it to the user's device along with the answer. The user can review the answer and take further action if necessary.

[0887] Example 2: Question about the legality of downloads

[0888] A user types "Is it illegal to download movies from this site?" into their device and sends the question. The server receives the question and extracts keywords such as "site," "movie," "download," and "illegal." Based on these keywords, the server searches a legal knowledge base to obtain information such as "Under copyright law, downloading movies without permission is illegal," and generates an answer. An advertisement for a relevant copyright protection tool is selected and sent to the user's device along with the answer. The user can review the answer and take appropriate action.

[0889] In this way, the system of the present invention provides users with prompt and accurate information regarding legal questions they may have, helping them to act with confidence.

[0890] The processing flow will be explained below.

[0891] Step 1:

[0892] The device launches the application, and the user enters the legal question. The user taps the "Submit" button to submit the question.

[0893] Step 2:

[0894] The device sends the user's question to the server, including the question text, user ID, timestamp, and other metadata.

[0895] Step 3:

[0896] The server receives the request data from the terminal, checks the consistency of the data format, and verifies that the data has been received correctly.

[0897] Step 4:

[0898] The server passes the received question to a natural language processing (NLP) engine for analysis, which analyzes the question and extracts key keywords and related phrases.

[0899] Step 5:

[0900] The server generates a query to the legal knowledge base based on the extracted keywords, and uses the generated query to query the legal knowledge base.

[0901] Step 6:

[0902] The legal knowledge base responds to queries with relevant information, which the server receives and uses to answer the user's questions.

[0903] Step 7:

[0904] Based on the information obtained, the server generates a specific response text, including any necessary legal justification or advice.

[0905] Step 8:

[0906] The server selects advertisements relevant to the answer from an advertisement database and embeds the selected advertisements appropriately in the answer text.

[0907] Step 9:

[0908] The server constructs response data containing the final answer and advertisement, and sends the response data to the user's device.

[0909] Step 10:

[0910] The device receives the response data from the server and uses the notification function to notify the user that a response has been received.

[0911] Step 11:

[0912] Users tap the notification on their device to open the application, view the answer and the advertisement, and then take appropriate action.

[0913] Example 1

[0914] 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."

[0915] In today's world, systems that provide fast and accurate answers to legal problems and questions faced by users are not yet fully developed. This makes it difficult for users to obtain reliable answers to their legal questions and requires them to go to the trouble of searching for information themselves. Furthermore, there is a need for systems that provide relevant advertising information and provide useful support to users.

[0916] 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.

[0917] In this invention, the server includes a means for receiving legal questions from a user's device, a means for analyzing the received questions and extracting key keywords, and a means for querying a legal knowledge base based on the extracted keywords to retrieve relevant information. This allows users to easily enter legal questions and obtain answers quickly and accurately. The server also includes a means for generating clear answers using a generative AI model, a means for embedding advertisements related to the generated answers based on relevance, and a means for notifying the user of the receipt of the answers using a notification function on the user's device. This allows users to simultaneously obtain necessary legal information as well as information on related services and products, realizing a comprehensive support system.

[0918] "User device" refers to electronic devices such as smartphones, computers, and tablets.

[0919] "Legal Questions" refers to questions or inquiries about the law.

[0920] "Means for receiving" refers to the function of receiving data from the user's terminal.

[0921] "Means of analyzing and extracting key keywords" refers to the process of using text analysis technology to identify important keywords from the question content.

[0922] "Legal knowledge base" refers to a database containing information related to the law.

[0923] "Means of submitting a query and retrieving relevant information" refers to the ability to send a specific query to a database and retrieve relevant information.

[0924] "Means of generating an answer based on retrieved information" refers to the process of using information obtained from a database to create an answer to a question.

[0925] "Means for selecting and embedding advertisements relevant to the generated answer" refers to a function for selecting advertisements relevant to the answer content and appropriately placing them within the answer text.

[0926] "Means for sending answers to the user's terminal" refers to the function of sending the generated answers and advertisements to the user's terminal.

[0927] "Generative AI model" refers to a technology that uses a large-scale artificial intelligence model to generate natural-language output for specific inputs.

[0928] "Means for notifying the receipt of a response using a notification function" refers to a function for notifying the user's terminal that a new response has arrived.

[0929] The present invention is a system that receives a legal question from a user terminal, analyzes the question, obtains information from an appropriate legal knowledge base to generate an answer, and provides the answer to the user. The following specific embodiments for implementing this system will be described.

[0930] The overall system configuration includes the following elements:

[0931] 1. User's device (smartphone, PC, tablet, etc.)

[0932] 2. Server (accepts questions, analyzes them, queries the database, generates answers, and sends them)

[0933] 3. Legal knowledge base (database containing legal information)

[0934] 4. Advertising database (stores relevant advertisements)

[0935] First, a user launches the application on their device and enters a legal question, which is then sent from the user's device to the server. Specifically, the user enters a question such as, "Is it illegal to use music from YouTube in my videos?"

[0936] When the server receives the user's question, it analyzes the content of the question using a natural language processing (NLP) engine. Examples of software used include Python's NLTK and spaCy. Based on the key keywords and phrases extracted through this analysis, a query is created against the legal knowledge base. For example, keywords such as "YouTube," "music," "video," and "illegal" are extracted.

[0937] The server then submits the generated query to a legal knowledge base to search for relevant information, which returns information such as legal guidelines, statutes, and case law that is directly related to the question, using software such as Elasticsearch and SQL databases.

[0938] The server then uses the information it has acquired to generate a clear answer to the user's question, using a generative AI model (e.g., GPT-3). An example prompt might look like this:

[0939] "What is the best response in this legal situation?"

[0940] Based on the information obtained and the prompt, the generative AI model generates an answer such as "YouTube requires permission from the copyright holder to use music."

[0941] Advertisements related to the generated answer are selected from an advertisement database. Based on the question and answer, highly relevant advertisements are selected and pasted into the answer. For example, an advertisement for a "copyright management service" is selected.

[0942] Finally, the server sends the final answer and the selected ad to the user's device using a communication protocol such as HTTPS.

[0943] The user's device displays the received data within the application and uses a notification function to notify the user that a response has arrived. The user can then open the application to view the response and advertisement.

[0944] This allows users to obtain fast and accurate information on legal questions, as well as information on related services and products, providing comprehensive support.

[0945] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0946] Step 1:

[0947] A user launches the application using their device and enters a legal question. This question is sent from the user's device to the server. The input is a textual question. Specifically, the user enters a question such as "Is it illegal to use music from YouTube in my video?" and presses the submit button. The output is the question data sent to the server.

[0948] Step 2:

[0949] The server receives the user's question and analyzes the question using a natural language processing (NLP) engine. The user's question data is obtained as input. To process the data, the NLP engine (e.g., spaCy) analyzes the text and extracts key keywords such as "YouTube," "music," "video," and "illegal." Specifically, the server passes the question received to the NLP engine, which then performs keyword extraction. A list of extracted keywords is generated as output.

[0950] Step 3:

[0951] The server queries the legal knowledge base based on the extracted keywords. A keyword list is obtained as input. As data calculation, a query statement is generated based on the keywords and submitted to Elasticsearch or an SQL database. Specifically, the server generates a query using the keywords "YouTube," "music," "video," and "illegal" and queries the database. As output, the legal knowledge base returns the relevant legal information.

[0952] Step 4:

[0953] The server generates an answer to the user's question based on the legal information it has acquired. The input is legal information acquired from a database. Data processing involves inputting the information into a generative AI model (e.g., GPT-3) along with a prompt, which generates an answer in natural language. An example of a prompt is "What is the best response in this legal situation?" Specific operations involve inputting the legal information acquired by the server into the AI ​​model, which then generates an answer. The output is an answer written in natural language.

[0954] Step 5:

[0955] Advertisements relevant to the generated answer are selected from an advertisement database and embedded in the answer text. The generated answer sentence is obtained as input. Data calculation involves searching for advertisements that are highly relevant based on the answer content and inserting them in the appropriate position. In specific operations, the server selects relevant advertisements based on the answer sentence from the database and embeds the advertisements in the answer text. The output is the final answer sentence with the advertisements embedded.

[0956] Step 6:

[0957] The server sends the completed answer and advertisement to the user's device. The final answer is obtained as input. Data processing involves generating a data packet for transmission and sending it using a secure communication protocol (e.g., HTTPS). Specifically, the server sends data including the answer and advertisement to the user's device. The user's device receives the data as output.

[0958] Step 7:

[0959] The user's device displays the received answer and advertisement and notifies the user. The input is the data sent from the server. The data is processed by converting the answer and advertisement into a display format and displaying them on the device's UI. The specific operation is that the device notifies the user that "a new answer is available," and the user opens the application to check the answer and advertisement. The output is a state in which the user can check the answer and advertisement.

[0960] (Application example 1)

[0961] 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."

[0962] Physical stores lack the means to quickly and accurately respond to legal issues faced by staff and owners. Questions about laws such as the Consumer Protection Act and the Labor Standards Act often arise in daily operations, making it difficult to obtain appropriate answers on the spot. This raises concerns about business delays and increased legal risks.

[0963] 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.

[0964] In this invention, the server includes means for receiving legal questions from a user's device, means for analyzing the received questions and extracting key keywords, means for querying a legal knowledge base based on the extracted keywords and retrieving relevant information, means for selecting and embedding advertisements related to the generated answers, means for enabling store staff to ask legal questions using smartphones, means for providing legal information related to the Consumer Protection Act and the Labor Standards Act, and means for transmitting answers to the user's device, thereby enabling stores to quickly and accurately respond to legal issues.

[0965] "User device" refers to an information device used to input and send legal questions, including smartphones, PCs, tablets, etc.

[0966] "Means for receiving" refers to the function of receiving legal questions sent from the user's terminal on the server.

[0967] "Means for analyzing and extracting key keywords" refers to the function of analyzing received legal questions using a natural language processing engine and identifying important keywords.

[0968] "Means of querying a legal knowledge base and retrieving relevant information" refers to the function of querying a legal knowledge base based on extracted keywords and searching for and retrieving relevant legal information.

[0969] "Means for generating answers" refers to the functionality for generating answers to users' questions based on the acquired legal information.

[0970] "Means for selecting and embedding relevant advertisements" refers to a function for selecting from an advertisement database the advertisement most relevant to the generated answer and appropriately placing it within the answer.

[0971] "Means enabling store staff to use their smartphones to ask legal questions" refers to functionality that enables store staff to use their smartphones to submit legal questions to the system.

[0972] "Means of providing legal information on the Consumer Protection Act and the Labor Standards Act" refers to the function of providing information on the Consumer Protection Act and the Labor Standards Act that is related to the content of the question.

[0973] "Means for sending answers to the user's terminal" refers to a function for sending the generated answers to the user's terminal and allowing the user to check the answers.

[0974] MODE FOR CARRYING OUT THE INVENTION

[0975] The present invention is a system in which users input legal questions, and the server analyzes and generates answers. The system aims to provide store staff with fast and accurate information on legal issues they face in the field. The present invention is implemented in the following configuration.

[0976] Overall system configuration

[0977] The system mainly consists of the following components:

[0978] 1. User's device: An information device used to input and submit legal inquiries, including smartphones, PCs, tablets, etc.

[0979] 2. Server: Accepts questions, analyzes them, queries the database, generates answers, and sends them.

[0980] 3. Legal Knowledge Base: A database containing information about the law.

[0981] 4. Advertisement database: stores relevant advertisements.

[0982] Program processing

[0983] Posting and Submitting Questions

[0984] The user launches the application using their device and enters a legal question, which is then sent from the user's device to the server.

[0985] Receiving and parsing questions

[0986] Once the server receives the user's question, it analyzes it using a natural language processing (NLP) engine, such as SpaCy or NLTK, and uses the key keywords and phrases extracted from the analysis to formulate a query against a legal knowledge base.

[0987] Querying the Knowledge Base

[0988] The server then submits the generated query to a legal knowledge base, which stores a wide range of legal information, including consumer protection laws, labor standards laws, and copyright laws, and returns information such as specific legal guidelines, provisions, and precedents.

[0989] Generate answers

[0990] The server uses the information it receives to generate a clear answer to the user's question, including legal reasons and specific advice.

[0991] Ad selection and placement

[0992] Advertisements relevant to the generated answer are selected from an advertisement database and appropriately embedded in the answer text, for example, an advertisement for a complaint management service is selected for a consumer protection question.

[0993] Submit your answer

[0994] The server sends data containing the final answer and the selected advertisement to the user's device. The user's device receives this data and uses a notification function to notify the user that the answer has arrived. The user can open the application and view the answer and advertisement.

[0995] Specific examples

[0996] Specific examples are shown below.

[0997] Example 1: When a store staff member asks on their smartphone, "A consumer has requested a return. How should I handle this?" The server extracts keywords such as "return," "consumer," and "response" and retrieves information on the applicable consumer protection law. It then provides appropriate guidelines and simultaneously displays an advertisement for a complaint management service.

[0998] Example prompt sentence:

[0999] "What are the Consumer Protection Act guidelines regarding returns?"

[1000] This system will enable legal issues to be dealt with quickly and accurately even in physical stores.

[1001] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1002] Step 1:

[1003] The terminal provides an interface for users to input legal questions. The input content is a specific legal question, such as "A consumer has requested a return. How should I respond?" When the user inputs a question and presses the send button, the terminal sends the question data to the server.

[1004] Step 2:

[1005] The server receives legal question data sent from the user's device. The input includes the user's question text data. The server inputs this received data into a natural language processing (NLP) engine (e.g., SpaCy, NLTK) to analyze the question content. Specific processing includes sentence tokenization, semantic analysis, and keyword extraction. The output is a list of analyzed keywords.

[1006] Step 3:

[1007] The server generates a query to the legal knowledge base based on the keyword list extracted by the analysis. The keyword list obtained in step 2 is used as input. As a specific example of query generation, an SQL query to search for related legal information is constructed using keywords such as "consumer," "return," and "response." The generated query is obtained as output.

[1008] Step 4:

[1009] The server submits the generated query to a legal knowledge base to retrieve the relevant legal information. The generated query is used as input. A database engine (e.g., MySQL, PostgreSQL) is used to execute the query and search for relevant legal provisions and guidelines. The retrieved legal information is the output.

[1010] Step 5:

[1011] The server generates an answer to the user's question based on the acquired legal information. The legal information acquired in step 4 is used as input. Specifically, the server constructs an answer text to the user's question based on the legal information. The generated answer text is obtained as output.

[1012] Step 6:

[1013] The server selects advertisements from an advertisement database that are relevant to the generated answer and embeds them appropriately in the answer text. Keywords related to the generated answer text are used as input. A database query is used to search the advertisement database to select relevant advertisements, such as an advertisement for a "claim management service." The output is the answer text with the embedded advertisement.

[1014] Step 7:

[1015] The server sends data including the final answer and the selected advertisement to the user's device. The answer text with the embedded advertisement obtained in step 6 is used as input. The server sends this to the user's device, and the device uses a notification function to notify the user that the answer has arrived. The user can open the application and check the answer and advertisement. The output is the answer data that is displayed on the user's device.

[1016] The above are the specific processing steps of the system that realizes the application example.

[1017] 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.

[1018] The present invention is a system that receives legal questions from a user's device, analyzes the questions, retrieves information from an appropriate legal knowledge base to generate answers, and further combines it with an emotion engine that recognizes the user's emotions to provide more personalized answers and advertisements. Specific embodiments of this system are described below.

[1019] Overall system configuration

[1020] The system mainly consists of the following components:

[1021] 1. User's device (smartphone, PC, tablet, etc.)

[1022] 2. Server (accepts questions, analyzes them, recognizes emotions, queries the database, generates answers, and sends them)

[1023] 3. Legal knowledge base (database containing legal information)

[1024] 4. Advertising database (stores relevant advertisements)

[1025] 5. Emotion engine (recognizes user emotions and reflects them in analysis)

[1026] Program processing

[1027] The following provides a detailed natural language explanation of the operation of the system.

[1028] Posting and Submitting Questions

[1029] The user launches the application using their device and enters a legal question, which is then transmitted from the user's device to the server.

[1030] Receiving and parsing questions

[1031] The device sends the user's question to the server, which then uses a natural language processing (NLP) engine to analyze the question and extract key keywords and phrases.

[1032] Emotion recognition

[1033] The server uses an emotion engine to recognize emotions from the user's input. This emotion information is then associated with the analysis results of the question. The emotion engine determines emotions such as anger, sadness, joy, and surprise based on the user's vocabulary and sentence structure.

[1034] Querying the Knowledge Base

[1035] The server generates a query to the legal knowledge base based on the extracted keywords and the recognized sentiment, and uses the generated query to query the legal knowledge base.

[1036] Obtaining information and generating answers

[1037] The legal knowledge base responds to queries with relevant information. The server receives the information and generates a specific answer text tailored to the user's question and sentiment. The answer includes the necessary legal reasoning and advice.

[1038] Emotion-based ad selection

[1039] The server selects relevant ads from an advertising database based on the user's emotions, and embeds them in the answer depending on the user's emotions. For example, if the user is angry, an ad that conveys a more calming or reassuring feeling will be selected.

[1040] Submit your answer

[1041] The server constructs response data containing the final answer and advertisement, which is then sent to the user's device.

[1042] Receiving and notifying responses

[1043] The device receives the response data from the server and uses the notification function to notify the user that a response has been received.

[1044] Show Answers

[1045] Users tap the notification on their device, open the application, and view the response and advertisement content, which they can then take appropriate action based on.

[1046] Specific examples

[1047] Example 1: YouTube music question

[1048] The user types "Is it illegal to use music from YouTube in my videos?" into their device and sends the question. The server receives this question and extracts keywords such as "YouTube," "music," "video," and "illegal." At the same time, the emotion engine recognizes that the user is feeling anxious. Based on these keywords and emotion information, the server searches a legal knowledge base, obtains the information that "using music from YouTube requires permission from the copyright holder," and generates an answer. An advertisement for a copyright management service that gives a sense of security in line with the emotion is selected and sent to the user's device along with the answer. The user can check the answer and take the next step with peace of mind.

[1049] Example 2: Question about the legality of downloads

[1050] The user types "Is it illegal to download movies from this site?" into their device and sends the question. The server receives this question and extracts keywords such as "site," "movie," "download," and "illegal." At the same time, the emotion engine recognizes that the user has doubts. Based on these keywords and emotion information, the server searches the legal knowledge base to obtain the information that "Under copyright law, downloading movies without permission is illegal," and generates an answer. An advertisement for a related copyright protection tool is selected and sent to the user's device along with the answer. The user can check the answer and take appropriate action.

[1051] In this way, the system of the present invention can provide prompt and accurate information to answer legal questions while taking into consideration the user's feelings, allowing the user to receive answers that take their feelings into consideration and act with peace of mind.

[1052] The processing flow will be explained below.

[1053] Step 1:

[1054] The device launches the application, and the user enters the legal question. The user taps the "Submit" button to submit the question.

[1055] Step 2:

[1056] The device sends the user's question to the server, including the question text, user ID, timestamp, and other metadata.

[1057] Step 3:

[1058] The server receives the request data from the terminal. The server checks the consistency of the data format and verifies that the data has been received correctly.

[1059] Step 4:

[1060] The server passes the received question to a natural language processing (NLP) engine, which analyzes the question and extracts key keywords and phrases.

[1061] Step 5:

[1062] The server uses an emotion engine to recognize emotions from the user's input, and associates the recognized emotions with the analysis results of the question.

[1063] Step 6:

[1064] The server generates queries to a legal knowledge base based on the extracted keywords and the recognized sentiments.

[1065] Step 7:

[1066] The server queries the generated query against a legal knowledge base to retrieve relevant information.

[1067] Step 8:

[1068] Based on the information acquired, the server generates a specific answer text based on the user's question and the perceived sentiment, including any necessary legal justification or advice.

[1069] Step 9:

[1070] The server selects advertisements from an advertisement database that are relevant to the answer, and the relevant advertisements are selected based on the recognized sentiment.

[1071] Step 10:

[1072] The server appropriately embeds the selected advertisement into the answer text to construct the final response data.

[1073] Step 11:

[1074] The server sends the response data (answers and advertisements) to the user's terminal.

[1075] Step 12:

[1076] The device receives the response data from the server and uses the notification function to notify the user that a response has been received.

[1077] Step 13:

[1078] Users tap the notification on their device, open the application, and view the response and advertisement content, allowing them to take appropriate action.

[1079] Example 2

[1080] 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."

[1081] Conventional legal question-answering systems were unable to consider the user's emotions when providing appropriate answers to legal questions entered by the user, making it difficult to provide answers that give the user a sense of satisfaction and security.In addition, the selection of advertisements was not based on the user's emotions, making it difficult to improve the quality of the user experience.

[1082] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1083] In this invention, the server includes means for receiving a legal question from a user's terminal, means for analyzing the received question and extracting key keywords and the user's emotions, means for generating a query to a legal knowledge base based on the extracted keywords and emotions and acquiring relevant information, means for generating an answer based on the acquired information and emotions, means for selecting and embedding an advertisement based on the emotion associated with the generated answer, means for transmitting the answer and advertisement to the user's terminal, and means for notifying the user's terminal of the receipt of the answer using a notification function, thereby enabling the user to receive personalized answers and advertisements according to their emotions.

[1084] "User device" refers to a device such as a smartphone, computer, or tablet that a user uses to enter a question.

[1085] A "Legal Question" is text information entered by a user about a legal question or issue.

[1086] "Analysis" is the process of analyzing the user-entered question and extracting key keywords and sentiments.

[1087] "Keywords" are important words and phrases extracted to understand the content of a question and generate an appropriate answer.

[1088] "Emotion" refers to emotional information that can be read from the user's input, and includes, for example, anxiety, doubt, anger, joy, etc.

[1089] A "legal knowledge base" is a database that stores legal information and provides relevant information in response to queries.

[1090] A "query" is a search inquiry against the legal knowledge base, generated based on the extracted keywords and sentiment.

[1091] "Answer generation" is the process of creating specific answer texts to users' questions based on the acquired legal and emotional information.

[1092] "Advertising" refers to promotional information for related products and services that are provided based on the user's interests and emotions.

[1093] "Sending" refers to the act of sending responses and advertisements as data from the server to the user's terminal.

[1094] The "notification function" is an application function that notifies the user's device that a new answer has arrived.

[1095] The present invention is a system that receives legal questions from a user's device, analyzes the questions, retrieves information from an appropriate legal knowledge base to generate answers, and further combines it with an emotion engine that recognizes the user's emotions to provide more personalized answers and advertisements. Specific embodiments of this system are described below.

[1096] Hardware and software used

[1097] This system is implemented using the following hardware and software:

[1098] User devices: smartphones, computers, tablets, etc.

[1099] Server: A high-performance computer that accepts questions, analyzes them, recognizes emotions, queries the database, generates answers, and sends them.

[1100] Legal Knowledge Base: A database containing legal information (MySQL, MongoDB, etc.)

[1101] Advertisement database: stores relevant advertisements

[1102] Natural Language Processing (NLP) engines: spaCy, Google Cloud NLP API, etc.

[1103] Sentiment engine: Microsoft Azure's Text Analytics API, IBM Watson's Tone Analyzer, etc.

[1104] Specific operation of the system

[1105] Posting and Submitting Questions

[1106] The user launches the application using their device and enters a legal question, which is then transmitted from the user's device to the server.

[1107] Receiving and parsing questions

[1108] The device sends the user's question to the server, which then uses a natural language processing (NLP) engine to analyze the question and extract key keywords and phrases.

[1109] Emotion recognition

[1110] The server uses an emotion engine to recognize emotions from the user's input. This emotion information is then associated with the analysis results of the question. The emotion engine determines emotions such as anger, sadness, joy, and surprise based on the user's vocabulary and sentence structure.

[1111] Querying the Knowledge Base

[1112] The server generates a query to the legal knowledge base based on the extracted keywords and the recognized sentiment, and uses the generated query to query the legal knowledge base.

[1113] Obtaining information and generating answers

[1114] The legal knowledge base responds to queries with relevant information. The server receives the information and generates a specific answer text tailored to the user's question and sentiment. The answer includes the necessary legal reasoning and advice.

[1115] Emotion-based ad selection

[1116] The server selects relevant ads from an advertising database based on the user's emotions, and embeds them in the answer depending on the user's emotions. For example, if the user is angry, an ad that conveys a more calming or reassuring feeling will be selected.

[1117] Submit your answer

[1118] The server constructs response data containing the final answer and advertisement, which is then sent to the user's device.

[1119] Receiving and notifying responses

[1120] The device receives the response data from the server and uses the notification function to notify the user that a response has been received.

[1121] Show Answers

[1122] Users tap the notification on their device, open the application, and view the response and advertisement content, which they can then take appropriate action based on.

[1123] Specific examples

[1124] Example 1: YouTube music question

[1125] The user types "Is it illegal to use music from YouTube in my videos?" into their device and sends the question. The server receives this question and extracts keywords such as "YouTube," "music," "video," and "illegal." At the same time, the emotion engine recognizes that the user is feeling anxious. Based on these keywords and emotion information, the server searches a legal knowledge base, obtains the information that "using music from YouTube requires permission from the copyright holder," and generates an answer. An advertisement for a copyright management service that gives a sense of security in line with the emotion is selected and sent to the user's device along with the answer. The user can check the answer and take the next step with peace of mind.

[1126] Example 2: Question about the legality of downloads

[1127] The user types "Is it illegal to download movies from this site?" into their device and sends the question. The server receives this question and extracts keywords such as "site," "movie," "download," and "illegal." At the same time, the emotion engine recognizes that the user has doubts. Based on these keywords and emotion information, the server searches the legal knowledge base to obtain the information that "Under copyright law, downloading movies without permission is illegal," and generates an answer. An advertisement for a related copyright protection tool is selected and sent to the user's device along with the answer. The user can check the answer and take appropriate action.

[1128] Prompt Sentence Examples

[1129] For example, a prompt for a generative AI model might look like this:

[1130] "Analyze the legal question entered by the user and generate relevant answers and sentiment-based ads, but also provide the sentiment the user is feeling about the question."

[1131] This system allows users to receive fast, accurate, and emotionally sensitive legal information, allowing them to take their next steps with confidence.

[1132] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1133] Step 1:

[1134] The user uses the terminal to launch the application and enter the legal questions.

[1135] What it does: A user launches the application and types a question into the question field, for example, "Is it illegal to use music from YouTube in my videos?"

[1136] Input: Legal question text.

[1137] Output: The question data sent from the device to the server.

[1138] Step 2:

[1139] The terminal transmits the entered question to the server.

[1140] Specific operation: The device sends the question text to the server as an HTTP POST request.

[1141] Input: The question text entered on the terminal.

[1142] Output: The HTTP request sent to the server.

[1143] Step 3:

[1144] The server analyzes the received question using a natural language processing (NLP) engine and extracts key keywords.

[1145] Specific operation: The server uses an NLP engine (e.g., spaCy) to extract the main keywords: "YouTube," "music," "video," and "illegal."

[1146] Input: The question text in the HTTP request.

[1147] Output: A list of extracted main keywords.

[1148] Step 4:

[1149] The server uses an emotion engine to recognize emotions from user input.

[1150] Specific operation: The server runs an emotion engine (for example, Microsoft Azure's Text Analytics API) and determines the user's emotion as "anxiety."

[1151] Input: Question text.

[1152] Output: Recognized emotion information.

[1153] Step 5:

[1154] The server generates a query to a legal knowledge base based on the extracted keywords and sentiment information.

[1155] What it does: The server generates a MySQL database query to search the legal knowledge base for the keywords "YouTube," "music," "video," and "illegal."

[1156] Input: Extracted keywords and sentiment information.

[1157] Output: A query against the legal knowledge base.

[1158] Step 6:

[1159] It retrieves information from a legal knowledge base in response to a query and generates a specific answer text.

[1160] Specific operation: The legal knowledge base returns the information that "YouTube requires permission from the copyright holder to use music," and the server creates an answer text based on this information.

[1161] Input: The generated query.

[1162] Output: The specific answer text.

[1163] Step 7:

[1164] The server selects an appropriate advertisement from an advertisement database according to the emotion information.

[1165] Specific operation: The server recognizes that the emotion is "anxiety" and selects an advertisement for a copyright management service that gives a sense of security.

[1166] Input: Recognized emotion information.

[1167] Output: The selected ad.

[1168] Step 8:

[1169] The server sends the final answer and advertisement to the user's terminal.

[1170] Specific operation: The server combines the selected advertisement and answer and sends it to the user's device as an HTTP response.

[1171] Input: Answer text and selected ad.

[1172] Output: The HTTP response sent to the user's device.

[1173] Step 9:

[1174] The user's terminal receives the response data from the server and notifies the user.

[1175] Specific behavior: The device displays a push notification to inform the user that a response has been received.

[1176] Input: HTTP response data from the server.

[1177] Output: A push notification to the user.

[1178] Step 10:

[1179] Users tap the notification to open the application and view the answers and advertisements.

[1180] What happens: The user sees the answer "YouTube music usage requires permission from the copyright holder" within the application, along with an advertisement for a related copyright management service.

[1181] Input: Answers and ad data displayed by user actions.

[1182] Output: The answer and advertisement that the user sees.

[1183] (Application example 2)

[1184] 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."

[1185] Current virtual store legal consultation systems are unable to provide answers that take users' emotions into account and are unable to meet users' needs for personalized advice. As a result, users are unable to receive appropriate advice and find it difficult to resolve their legal questions and concerns. Furthermore, they are unable to select relevant advertisements based on users' emotions, which reduces the quality of the user experience.

[1186] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a legal question from a user's terminal, means for analyzing the received question and extracting key keywords, means for querying a legal knowledge base based on the extracted keywords and obtaining relevant information, means for generating an answer based on the obtained information, means for recognizing the user's emotions, means for personalizing the answer based on the recognized emotions, means for selecting and embedding advertisements related to the generated answer, and means for transmitting the answer to the user's terminal. This makes it possible to provide personalized legal advice that takes user emotions into consideration and to appropriately select relevant advertisements.

[1187] "User device" refers to an electronic device, such as a smartphone, tablet, head-mounted display, or personal computer, that a user uses to input and receive questions.

[1188] A "legal question" is text or voice input that a user makes into a device to request information or advice about a legal matter.

[1189] "Analysis" is the process of categorizing incoming legal questions using natural language processing techniques and extracting key keywords and phrases.

[1190] "Keywords" are words or phrases that are particularly important in a legal question and form the basis for querying a legal knowledge base.

[1191] A "legal knowledge base" is a database that systematically collects legal information and provides relevant legal information in response to a query.

[1192] A "query" refers to an inquiry made to a legal knowledge base to obtain information.

[1193] "Retrieving information" means querying a legal knowledge base to retrieve relevant law or advice and gathering it to answer the user's question.

[1194] "Generating an answer" means creating a specific and appropriate response to a user's question based on the legal information obtained.

[1195] "Emotion recognition" means using natural language processing and artificial intelligence technology to determine a user's emotional state from their questions and voice.

[1196] "Personalizing" means tailoring responses to the user's emotions and specific circumstances to provide individually optimized answers.

[1197] "Selecting ads" means selecting relevant ads from an ad database based on the user's emotions and questions.

[1198] "Embedding advertisements in answers" means placing selected advertisements in appropriate positions within the generated legal answer text.

[1199] "Sending an answer" refers to the process of sending the generated answer and related advertisement to the user's terminal using telecommunications means.

[1200] The present invention provides a legal consultation assistant system that allows users to obtain immediate and personalized answers to legal questions in a virtual store. A detailed description of specific embodiments of the system is provided below.

[1201] Overall system configuration

[1202] The system consists of the following components:

[1203] 1. User's Device

[1204] 2. Server

[1205] 3. Legal Knowledge Base

[1206] 4. Advertising Database

[1207] 5. Emotion Recognition Engine

[1208] User's device

[1209] The user's device is an electronic device such as a smartphone, tablet, or head-mounted display. An application is installed on the device that allows the user to enter legal questions. The application includes a text box for entering questions and voice recognition capabilities.

[1210] server

[1211] The server receives legal questions sent from the user's device, analyzes them, recognizes emotions, generates queries to a legal knowledge base to obtain information, and finally performs a series of processes to generate personalized answers. The specific technologies used are as follows:

[1212] Natural language processing engine: Analyzes user questions and extracts key keywords. This uses technologies such as Google Cloud NLP API.

[1213] Emotion Recognition Engine: Analyzes emotions from the content of the user's question using technologies such as IBM Watson Tone Analyzer.

[1214] Legal knowledge base: Collects legal information and provides the required information based on queries. This can be done using APIs such as LexisNexis.

[1215] Advertisement database: Selects ads based on user sentiment and questions. This is done using Google Ads API, etc.

[1216] Legal Knowledge Base

[1217] A legal knowledge base is a database that comprehensively stores legal information and provides relevant information in response to queries. It is used by the server to retrieve appropriate legal information for a user's question by issuing a query based on keywords extracted from a natural language processing engine.

[1218] Advertising Database

[1219] The advertisement database stores relevant advertisements based on the user's question and sentiment, and the server selects appropriate advertisements from this database and embeds them into the generated answer.

[1220] Emotion Recognition Engine

[1221] The emotion recognition engine is used to analyze emotions from the user's text and voice, thereby personalizing responses to match the user's emotional state.

[1222] Operation flow

[1223] As an example, here is the prompt text when a user asks, "Can I get a refund for an item I purchased from your virtual store?"

[1224] Prompt Sentence Examples

[1225] A user asks: "Can I get a refund for an item I purchased from your virtual store?"

[1226] NLP analysis results:

[1227] Keywords: [Virtual store, purchase, product, refund, possible]

[1228] Emotion recognition:

[1229] User sentiment: [Anxiety]

[1230] Query the legal knowledge base:

[1231] SELECT FROM legal_knowledge_base WHERE keywords IN ('virtual store', 'purchase', 'refund') AND sentiment = 'anxiety'

[1232] Answer generation:

[1233] Refunds for items purchased from virtual stores are subject to the store's refund policy. Generally, refunds may be granted for unused items within 14 days of purchase. Please check the store's terms and conditions for details.

[1234] Ad Selection:

[1235] For a safer shopping experience, use our app to check refund policies before you buy. Download it here.

[1236] In this way, the present invention is a system that takes into consideration the user's feelings and provides specific and appropriate legal advice, thereby contributing to alleviating the user's anxieties and doubts.

[1237] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1238] Step 1:

[1239] The user enters a legal question into the terminal and presses the send button, which sends the entered question text (input: user's question text, output: data sent to the server).

[1240] Step 2:

[1241] The device sends the user's question to the server, which receives the data and passes it to a natural language processing engine (input: user's question text, output: data received by the server).

[1242] Step 3:

[1243] The server uses a natural language processing engine to analyze the received question and extract key keywords. Specifically, it uses the Google Cloud NLP API to analyze the question text and extract important keywords and phrases (input: question text received by the server, output: extracted keywords).

[1244] Step 4:

[1245] The server uses an emotion recognition engine to analyze the emotion in the user's question text. Specifically, it uses IBM Watson Tone Analyzer to determine the emotional state of the question text (anger, anxiety, joy, etc.) (input: question text, output: recognized emotional state).

[1246] Step 5:

[1247] The server generates a query to a legal knowledge base based on the extracted keywords and the recognized sentiment. The generated SQL query is sent to the legal knowledge base (e.g., LexisNexis API) to retrieve the relevant information (input: keywords and sentiment state, output: legal information query results).

[1248] Step 6:

[1249] The server generates a specific answer text to the user's question based on the obtained legal information, and adjusts the answer to include the most appropriate advice for the user by taking into account emotional information (input: legal information from the query result, output: generated answer text).

[1250] Step 7:

[1251] The server selects relevant ads from an advertising database based on the generated answer text. Specifically, it uses the Google Ads API or similar to search for and retrieve ads that match the user's emotions and question content (input: generated answer text and emotional state, output: selected ads).

[1252] Step 8:

[1253] The server combines the generated answer text and the associated advertisement to construct the final response data to send to the user (input: generated answer text and selected advertisement; output: final response data).

[1254] Step 9:

[1255] The server sends the constructed final response data to the user's terminal (input: final response data, output: data to be sent to the user's terminal).

[1256] Step 10:

[1257] The user's device receives the response data from the server and displays a notification to the user using the notification function (input: response data from the server, output: notification to the user).

[1258] Step 11:

[1259] The user taps the notification on their device, opens the application, and checks the answer and advertisement content (input: notification data, output: displayed answer and advertisement).

[1260] 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.

[1261] 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.

[1262] 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.

[1263] [Fourth embodiment]

[1264] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1265] 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.

[1266] 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).

[1267] 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.

[1268] 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.

[1269] 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).

[1270] 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.

[1271] 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.

[1272] 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.

[1273] 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.

[1274] 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.

[1275] 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.

[1276] 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."

[1277] The present invention is a system that receives a legal question from a user's terminal, analyzes the question, retrieves information from an appropriate legal knowledge base to generate an answer, and provides the answer to the user. Specific embodiments of this system are described below.

[1278] Overall system configuration

[1279] The system mainly consists of the following components:

[1280] 1. User's device (smartphone, PC, tablet, etc.)

[1281] 2. Server (accepts questions, analyzes them, queries the database, generates answers, and sends them)

[1282] 3. Legal knowledge base (database containing legal information)

[1283] 4. Advertising database (stores relevant advertisements)

[1284] Program processing

[1285] The following provides a detailed natural language explanation of the operation of the system.

[1286] Posting and Submitting Questions

[1287] The user launches the application using their device and enters a legal question, which is then transmitted from the user's device to the server.

[1288] Receiving and parsing questions

[1289] When the server receives a user's question, it uses a natural language processing (NLP) engine to analyze the question, extracting key keywords and phrases from the query and formulating a query against a legal knowledge base.

[1290] Querying the Knowledge Base

[1291] The server then submits the generated query to a legal knowledge base to search for relevant information. The information returned from the legal knowledge base includes legal guidelines, statutes, and precedents that are directly related to the query.

[1292] Generate answers

[1293] The server uses the information it receives to generate a clear answer to the user's question, including legal reasons and specific advice.

[1294] Ad selection and placement

[1295] Advertisements relevant to the generated answer are selected from an advertisement database. For example, for a question about copyright, an advertisement for a copyright management service is selected and appropriately embedded in the answer text.

[1296] Submit your answer

[1297] The server sends data containing the final answer and the selected advertisement to the user's device. The user's device receives this data and uses a notification function to notify the user that the answer has arrived. The user can open the application and view the answer and advertisement.

[1298] Specific examples

[1299] Example 1: YouTube music question

[1300] A user types the question "Is it illegal to use music from YouTube in my videos?" into their device and sends it. The server receives the question and extracts keywords such as "YouTube," "music," "video," and "illegal." It then searches a legal knowledge base based on these keywords, obtains information such as "YouTube music requires permission from the copyright holder," and generates an answer. It then selects an advertisement for a copyright management service and sends it to the user's device along with the answer. The user can review the answer and take further action if necessary.

[1301] Example 2: Question about the legality of downloads

[1302] A user types "Is it illegal to download movies from this site?" into their device and sends the question. The server receives the question and extracts keywords such as "site," "movie," "download," and "illegal." Based on these keywords, the server searches a legal knowledge base to obtain information such as "Under copyright law, downloading movies without permission is illegal," and generates an answer. An advertisement for a relevant copyright protection tool is selected and sent to the user's device along with the answer. The user can review the answer and take appropriate action.

[1303] In this way, the system of the present invention provides users with prompt and accurate information regarding legal questions they may have, helping them to act with confidence.

[1304] The processing flow will be explained below.

[1305] Step 1:

[1306] The device launches the application, and the user enters the legal question. The user taps the "Submit" button to submit the question.

[1307] Step 2:

[1308] The device sends the user's question to the server, including the question text, user ID, timestamp, and other metadata.

[1309] Step 3:

[1310] The server receives the request data from the terminal, checks the consistency of the data format, and verifies that the data has been received correctly.

[1311] Step 4:

[1312] The server passes the received question to a natural language processing (NLP) engine for analysis, which analyzes the question and extracts key keywords and related phrases.

[1313] Step 5:

[1314] The server generates a query to the legal knowledge base based on the extracted keywords, and uses the generated query to query the legal knowledge base.

[1315] Step 6:

[1316] The legal knowledge base responds to queries with relevant information, which the server receives and uses to answer the user's questions.

[1317] Step 7:

[1318] Based on the information obtained, the server generates a specific response text, including any necessary legal justification or advice.

[1319] Step 8:

[1320] The server selects advertisements relevant to the answer from an advertisement database and embeds the selected advertisements appropriately in the answer text.

[1321] Step 9:

[1322] The server constructs response data containing the final answer and advertisement, and sends the response data to the user's device.

[1323] Step 10:

[1324] The device receives the response data from the server and uses the notification function to notify the user that a response has been received.

[1325] Step 11:

[1326] Users tap the notification on their device to open the application, view the answer and the advertisement, and then take appropriate action.

[1327] Example 1

[1328] 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."

[1329] In today's world, systems that provide fast and accurate answers to legal problems and questions faced by users are not yet fully developed. This makes it difficult for users to obtain reliable answers to their legal questions and requires them to go to the trouble of searching for information themselves. Furthermore, there is a need for systems that provide relevant advertising information and provide useful support to users.

[1330] 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.

[1331] In this invention, the server includes a means for receiving legal questions from a user's device, a means for analyzing the received questions and extracting key keywords, and a means for querying a legal knowledge base based on the extracted keywords to retrieve relevant information. This allows users to easily enter legal questions and obtain answers quickly and accurately. The server also includes a means for generating clear answers using a generative AI model, a means for embedding advertisements related to the generated answers based on relevance, and a means for notifying the user of the receipt of the answers using a notification function on the user's device. This allows users to simultaneously obtain necessary legal information as well as information on related services and products, realizing a comprehensive support system.

[1332] "User device" refers to electronic devices such as smartphones, computers, and tablets.

[1333] "Legal Questions" refers to questions or inquiries about the law.

[1334] "Means for receiving" refers to the function of receiving data from the user's terminal.

[1335] "Means of analyzing and extracting key keywords" refers to the process of using text analysis technology to identify important keywords from the question content.

[1336] "Legal knowledge base" refers to a database containing information related to the law.

[1337] "Means of submitting a query and retrieving relevant information" refers to the ability to send a specific query to a database and retrieve relevant information.

[1338] "Means of generating an answer based on retrieved information" refers to the process of using information obtained from a database to create an answer to a question.

[1339] "Means for selecting and embedding advertisements relevant to the generated answer" refers to a function for selecting advertisements relevant to the answer content and appropriately placing them within the answer text.

[1340] "Means for sending answers to the user's terminal" refers to the function of sending the generated answers and advertisements to the user's terminal.

[1341] "Generative AI model" refers to a technology that uses a large-scale artificial intelligence model to generate natural-language output for specific inputs.

[1342] "Means for notifying the receipt of a response using a notification function" refers to a function for notifying the user's terminal that a new response has arrived.

[1343] The present invention is a system that receives a legal question from a user terminal, analyzes the question, obtains information from an appropriate legal knowledge base to generate an answer, and provides the answer to the user. The following specific embodiments for implementing this system will be described.

[1344] The overall system configuration includes the following elements:

[1345] 1. User's device (smartphone, PC, tablet, etc.)

[1346] 2. Server (accepts questions, analyzes them, queries the database, generates answers, and sends them)

[1347] 3. Legal knowledge base (database containing legal information)

[1348] 4. Advertising database (stores relevant advertisements)

[1349] First, a user launches the application on their device and enters a legal question, which is then sent from the user's device to the server. Specifically, the user enters a question such as, "Is it illegal to use music from YouTube in my videos?"

[1350] When the server receives the user's question, it analyzes the content of the question using a natural language processing (NLP) engine. Examples of software used include Python's NLTK and spaCy. Based on the key keywords and phrases extracted through this analysis, a query is created against the legal knowledge base. For example, keywords such as "YouTube," "music," "video," and "illegal" are extracted.

[1351] The server then submits the generated query to a legal knowledge base to search for relevant information, which returns information such as legal guidelines, statutes, and case law that is directly related to the question, using software such as Elasticsearch and SQL databases.

[1352] The server then uses the information it has acquired to generate a clear answer to the user's question, using a generative AI model (e.g., GPT-3). An example prompt might look like this:

[1353] "What is the best response in this legal situation?"

[1354] Based on the information obtained and the prompt, the generative AI model generates an answer such as "YouTube requires permission from the copyright holder to use music."

[1355] Advertisements related to the generated answer are selected from an advertisement database. Based on the question and answer, highly relevant advertisements are selected and pasted into the answer. For example, an advertisement for a "copyright management service" is selected.

[1356] Finally, the server sends the final answer and the selected ad to the user's device using a communication protocol such as HTTPS.

[1357] The user's device displays the received data within the application and uses a notification function to notify the user that a response has arrived. The user can then open the application to view the response and advertisement.

[1358] This allows users to obtain fast and accurate information on legal questions, as well as information on related services and products, providing comprehensive support.

[1359] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1360] Step 1:

[1361] A user launches the application using their device and enters a legal question. This question is sent from the user's device to the server. The input is a textual question. Specifically, the user enters a question such as "Is it illegal to use music from YouTube in my video?" and presses the submit button. The output is the question data sent to the server.

[1362] Step 2:

[1363] The server receives the user's question and analyzes the question using a natural language processing (NLP) engine. The user's question data is obtained as input. To process the data, the NLP engine (e.g., spaCy) analyzes the text and extracts key keywords such as "YouTube," "music," "video," and "illegal." Specifically, the server passes the question received to the NLP engine, which then performs keyword extraction. A list of extracted keywords is generated as output.

[1364] Step 3:

[1365] The server queries the legal knowledge base based on the extracted keywords. A keyword list is obtained as input. As data calculation, a query statement is generated based on the keywords and submitted to Elasticsearch or an SQL database. Specifically, the server generates a query using the keywords "YouTube," "music," "video," and "illegal" and queries the database. As output, the legal knowledge base returns the relevant legal information.

[1366] Step 4:

[1367] The server generates an answer to the user's question based on the legal information it has acquired. The input is legal information acquired from a database. Data processing involves inputting the information into a generative AI model (e.g., GPT-3) along with a prompt, which generates an answer in natural language. An example of a prompt is "What is the best response in this legal situation?" Specific operations involve inputting the legal information acquired by the server into the AI ​​model, which then generates an answer. The output is an answer written in natural language.

[1368] Step 5:

[1369] Advertisements relevant to the generated answer are selected from an advertisement database and embedded in the answer text. The generated answer sentence is obtained as input. Data calculation involves searching for advertisements that are highly relevant based on the answer content and inserting them in the appropriate position. In specific operations, the server selects relevant advertisements based on the answer sentence from the database and embeds the advertisements in the answer text. The output is the final answer sentence with the advertisements embedded.

[1370] Step 6:

[1371] The server sends the completed answer and advertisement to the user's device. The final answer is obtained as input. Data processing involves generating a data packet for transmission and sending it using a secure communication protocol (e.g., HTTPS). Specifically, the server sends data including the answer and advertisement to the user's device. The user's device receives the data as output.

[1372] Step 7:

[1373] The user's device displays the received answer and advertisement and notifies the user. The input is the data sent from the server. The data is processed by converting the answer and advertisement into a display format and displaying them on the device's UI. The specific operation is that the device notifies the user that "a new answer is available," and the user opens the application to check the answer and advertisement. The output is a state in which the user can check the answer and advertisement.

[1374] (Application example 1)

[1375] 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."

[1376] Physical stores lack the means to quickly and accurately respond to legal issues faced by staff and owners. Questions about laws such as the Consumer Protection Act and the Labor Standards Act often arise in daily operations, making it difficult to obtain appropriate answers on the spot. This raises concerns about business delays and increased legal risks.

[1377] 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.

[1378] In this invention, the server includes means for receiving legal questions from a user's device, means for analyzing the received questions and extracting key keywords, means for querying a legal knowledge base based on the extracted keywords and retrieving relevant information, means for selecting and embedding advertisements related to the generated answers, means for enabling store staff to ask legal questions using smartphones, means for providing legal information related to the Consumer Protection Act and the Labor Standards Act, and means for transmitting answers to the user's device, thereby enabling stores to quickly and accurately respond to legal issues.

[1379] "User device" refers to an information device used to input and send legal questions, including smartphones, PCs, tablets, etc.

[1380] "Means for receiving" refers to the function of receiving legal questions sent from the user's terminal on the server.

[1381] "Means for analyzing and extracting key keywords" refers to the function of analyzing received legal questions using a natural language processing engine and identifying important keywords.

[1382] "Means of querying a legal knowledge base and retrieving relevant information" refers to the function of querying a legal knowledge base based on extracted keywords and searching for and retrieving relevant legal information.

[1383] "Means for generating answers" refers to the functionality for generating answers to users' questions based on the acquired legal information.

[1384] "Means for selecting and embedding relevant advertisements" refers to a function for selecting from an advertisement database the advertisement most relevant to the generated answer and appropriately placing it within the answer.

[1385] "Means enabling store staff to use their smartphones to ask legal questions" refers to functionality that enables store staff to use their smartphones to submit legal questions to the system.

[1386] "Means of providing legal information on the Consumer Protection Act and the Labor Standards Act" refers to the function of providing information on the Consumer Protection Act and the Labor Standards Act that is related to the content of the question.

[1387] "Means for sending answers to the user's terminal" refers to a function for sending the generated answers to the user's terminal and allowing the user to check the answers.

[1388] MODE FOR CARRYING OUT THE INVENTION

[1389] The present invention is a system in which users input legal questions, and the server analyzes and generates answers. The system aims to provide store staff with fast and accurate information on legal issues they face in the field. The present invention is implemented in the following configuration.

[1390] Overall system configuration

[1391] The system mainly consists of the following components:

[1392] 1. User's device: An information device used to input and submit legal inquiries, including smartphones, PCs, tablets, etc.

[1393] 2. Server: Accepts questions, analyzes them, queries the database, generates answers, and sends them.

[1394] 3. Legal Knowledge Base: A database containing information about the law.

[1395] 4. Advertisement database: stores relevant advertisements.

[1396] Program processing

[1397] Posting and Submitting Questions

[1398] The user launches the application using their device and enters a legal question, which is then sent from the user's device to the server.

[1399] Receiving and parsing questions

[1400] Once the server receives the user's question, it analyzes it using a natural language processing (NLP) engine, such as SpaCy or NLTK, and uses the key keywords and phrases extracted from the analysis to formulate a query against a legal knowledge base.

[1401] Querying the Knowledge Base

[1402] The server then submits the generated query to a legal knowledge base, which stores a wide range of legal information, including consumer protection laws, labor standards laws, and copyright laws, and returns information such as specific legal guidelines, provisions, and precedents.

[1403] Generate answers

[1404] The server uses the information it receives to generate a clear answer to the user's question, including legal reasons and specific advice.

[1405] Ad selection and placement

[1406] Advertisements relevant to the generated answer are selected from an advertisement database and appropriately embedded in the answer text, for example, an advertisement for a complaint management service is selected for a consumer protection question.

[1407] Submit your answer

[1408] The server sends data containing the final answer and the selected advertisement to the user's device. The user's device receives this data and uses a notification function to notify the user that the answer has arrived. The user can open the application and view the answer and advertisement.

[1409] Specific examples

[1410] Specific examples are shown below.

[1411] Example 1: When a store staff member asks on their smartphone, "A consumer has requested a return. How should I handle this?" The server extracts keywords such as "return," "consumer," and "response" and retrieves information on the applicable consumer protection law. It then provides appropriate guidelines and simultaneously displays an advertisement for a complaint management service.

[1412] Example prompt sentence:

[1413] "What are the Consumer Protection Act guidelines regarding returns?"

[1414] This system will enable legal issues to be dealt with quickly and accurately even in physical stores.

[1415] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1416] Step 1:

[1417] The terminal provides an interface for users to input legal questions. The input content is a specific legal question, such as "A consumer has requested a return. How should I respond?" When the user inputs a question and presses the send button, the terminal sends the question data to the server.

[1418] Step 2:

[1419] The server receives legal question data sent from the user's device. The input includes the user's question text data. The server inputs this received data into a natural language processing (NLP) engine (e.g., SpaCy, NLTK) to analyze the question content. Specific processing includes sentence tokenization, semantic analysis, and keyword extraction. The output is a list of analyzed keywords.

[1420] Step 3:

[1421] The server generates a query to the legal knowledge base based on the keyword list extracted by the analysis. The keyword list obtained in step 2 is used as input. As a specific example of query generation, an SQL query to search for related legal information is constructed using keywords such as "consumer," "return," and "response." The generated query is obtained as output.

[1422] Step 4:

[1423] The server submits the generated query to a legal knowledge base to retrieve the relevant legal information. The generated query is used as input. A database engine (e.g., MySQL, PostgreSQL) is used to execute the query and search for relevant legal provisions and guidelines. The retrieved legal information is the output.

[1424] Step 5:

[1425] The server generates an answer to the user's question based on the acquired legal information. The legal information acquired in step 4 is used as input. Specifically, the server constructs an answer text to the user's question based on the legal information. The generated answer text is obtained as output.

[1426] Step 6:

[1427] The server selects advertisements from an advertisement database that are relevant to the generated answer and embeds them appropriately in the answer text. Keywords related to the generated answer text are used as input. A database query is used to search the advertisement database to select relevant advertisements, such as an advertisement for a "claim management service." The output is the answer text with the embedded advertisement.

[1428] Step 7:

[1429] The server sends data including the final answer and the selected advertisement to the user's device. The answer text with the embedded advertisement obtained in step 6 is used as input. The server sends this to the user's device, and the device uses a notification function to notify the user that the answer has arrived. The user can open the application and check the answer and advertisement. The output is the answer data that is displayed on the user's device.

[1430] The above are the specific processing steps of the system that realizes the application example.

[1431] 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.

[1432] The present invention is a system that receives legal questions from a user's device, analyzes the questions, retrieves information from an appropriate legal knowledge base to generate answers, and further combines it with an emotion engine that recognizes the user's emotions to provide more personalized answers and advertisements. Specific embodiments of this system are described below.

[1433] Overall system configuration

[1434] The system mainly consists of the following components:

[1435] 1. User's device (smartphone, PC, tablet, etc.)

[1436] 2. Server (accepts questions, analyzes them, recognizes emotions, queries the database, generates answers, and sends them)

[1437] 3. Legal knowledge base (database containing legal information)

[1438] 4. Advertising database (stores relevant advertisements)

[1439] 5. Emotion engine (recognizes user emotions and reflects them in analysis)

[1440] Program processing

[1441] The following provides a detailed natural language explanation of the operation of the system.

[1442] Posting and Submitting Questions

[1443] The user launches the application using their device and enters a legal question, which is then transmitted from the user's device to the server.

[1444] Receiving and parsing questions

[1445] The device sends the user's question to the server, which then uses a natural language processing (NLP) engine to analyze the question and extract key keywords and phrases.

[1446] Emotion recognition

[1447] The server uses an emotion engine to recognize emotions from the user's input. This emotion information is then associated with the analysis results of the question. The emotion engine determines emotions such as anger, sadness, joy, and surprise based on the user's vocabulary and sentence structure.

[1448] Querying the Knowledge Base

[1449] The server generates a query to the legal knowledge base based on the extracted keywords and the recognized sentiment, and uses the generated query to query the legal knowledge base.

[1450] Obtaining information and generating answers

[1451] The legal knowledge base responds to queries with relevant information. The server receives the information and generates a specific answer text tailored to the user's question and sentiment. The answer includes the necessary legal reasoning and advice.

[1452] Emotion-based ad selection

[1453] The server selects relevant ads from an advertising database based on the user's emotions, and embeds them in the answer depending on the user's emotions. For example, if the user is angry, an ad that conveys a more calming or reassuring feeling will be selected.

[1454] Submit your answer

[1455] The server constructs response data containing the final answer and advertisement, which is then sent to the user's device.

[1456] Receiving and notifying responses

[1457] The device receives the response data from the server and uses the notification function to notify the user that a response has been received.

[1458] Show Answers

[1459] Users tap the notification on their device, open the application, and view the response and advertisement content, which they can then take appropriate action based on.

[1460] Specific examples

[1461] Example 1: YouTube music question

[1462] The user types "Is it illegal to use music from YouTube in my videos?" into their device and sends the question. The server receives this question and extracts keywords such as "YouTube," "music," "video," and "illegal." At the same time, the emotion engine recognizes that the user is feeling anxious. Based on these keywords and emotion information, the server searches a legal knowledge base, obtains the information that "using music from YouTube requires permission from the copyright holder," and generates an answer. An advertisement for a copyright management service that gives a sense of security in line with the emotion is selected and sent to the user's device along with the answer. The user can check the answer and take the next step with peace of mind.

[1463] Example 2: Question about the legality of downloads

[1464] The user types "Is it illegal to download movies from this site?" into their device and sends the question. The server receives this question and extracts keywords such as "site," "movie," "download," and "illegal." At the same time, the emotion engine recognizes that the user has doubts. Based on these keywords and emotion information, the server searches the legal knowledge base to obtain the information that "Under copyright law, downloading movies without permission is illegal," and generates an answer. An advertisement for a related copyright protection tool is selected and sent to the user's device along with the answer. The user can check the answer and take appropriate action.

[1465] In this way, the system of the present invention can provide prompt and accurate information to answer legal questions while taking into consideration the user's feelings, allowing the user to receive answers that take their feelings into consideration and act with peace of mind.

[1466] The processing flow will be explained below.

[1467] Step 1:

[1468] The device launches the application, and the user enters the legal question. The user taps the "Submit" button to submit the question.

[1469] Step 2:

[1470] The device sends the user's question to the server, including the question text, user ID, timestamp, and other metadata.

[1471] Step 3:

[1472] The server receives the request data from the terminal. The server checks the consistency of the data format and verifies that the data has been received correctly.

[1473] Step 4:

[1474] The server passes the received question to a natural language processing (NLP) engine, which analyzes the question and extracts key keywords and phrases.

[1475] Step 5:

[1476] The server uses an emotion engine to recognize emotions from the user's input, and associates the recognized emotions with the analysis results of the question.

[1477] Step 6:

[1478] The server generates queries to a legal knowledge base based on the extracted keywords and the recognized sentiments.

[1479] Step 7:

[1480] The server queries the generated query against a legal knowledge base to retrieve relevant information.

[1481] Step 8:

[1482] Based on the information acquired, the server generates a specific answer text based on the user's question and the perceived sentiment, including any necessary legal justification or advice.

[1483] Step 9:

[1484] The server selects advertisements from an advertisement database that are relevant to the answer, and the relevant advertisements are selected based on the recognized sentiment.

[1485] Step 10:

[1486] The server appropriately embeds the selected advertisement into the answer text to construct the final response data.

[1487] Step 11:

[1488] The server sends the response data (answers and advertisements) to the user's terminal.

[1489] Step 12:

[1490] The device receives the response data from the server and uses the notification function to notify the user that a response has been received.

[1491] Step 13:

[1492] Users tap the notification on their device, open the application, and view the response and advertisement content, allowing them to take appropriate action.

[1493] Example 2

[1494] 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."

[1495] Conventional legal question-answering systems were unable to consider the user's emotions when providing appropriate answers to legal questions entered by the user, making it difficult to provide answers that give the user a sense of satisfaction and security.In addition, the selection of advertisements was not based on the user's emotions, making it difficult to improve the quality of the user experience.

[1496] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1497] In this invention, the server includes means for receiving a legal question from a user's terminal, means for analyzing the received question and extracting key keywords and the user's emotions, means for generating a query to a legal knowledge base based on the extracted keywords and emotions and acquiring relevant information, means for generating an answer based on the acquired information and emotions, means for selecting and embedding an advertisement based on the emotion associated with the generated answer, means for transmitting the answer and advertisement to the user's terminal, and means for notifying the user's terminal of the receipt of the answer using a notification function, thereby enabling the user to receive personalized answers and advertisements according to their emotions.

[1498] "User device" refers to a device such as a smartphone, computer, or tablet that a user uses to enter a question.

[1499] A "Legal Question" is text information entered by a user about a legal question or issue.

[1500] "Analysis" is the process of analyzing the user-entered question and extracting key keywords and sentiments.

[1501] "Keywords" are important words and phrases extracted to understand the content of a question and generate an appropriate answer.

[1502] "Emotion" refers to emotional information that can be read from the user's input, and includes, for example, anxiety, doubt, anger, joy, etc.

[1503] A "legal knowledge base" is a database that stores legal information and provides relevant information in response to queries.

[1504] A "query" is a search inquiry against the legal knowledge base, generated based on the extracted keywords and sentiment.

[1505] "Answer generation" is the process of creating specific answer texts to users' questions based on the acquired legal and emotional information.

[1506] "Advertising" refers to promotional information for related products and services that are provided based on the user's interests and emotions.

[1507] "Sending" refers to the act of sending responses and advertisements as data from the server to the user's terminal.

[1508] The "notification function" is an application function that notifies the user's device that a new answer has arrived.

[1509] The present invention is a system that receives legal questions from a user's device, analyzes the questions, retrieves information from an appropriate legal knowledge base to generate answers, and further combines it with an emotion engine that recognizes the user's emotions to provide more personalized answers and advertisements. Specific embodiments of this system are described below.

[1510] Hardware and software used

[1511] This system is implemented using the following hardware and software:

[1512] User devices: smartphones, computers, tablets, etc.

[1513] Server: A high-performance computer that accepts questions, analyzes them, recognizes emotions, queries the database, generates answers, and sends them.

[1514] Legal Knowledge Base: A database containing legal information (MySQL, MongoDB, etc.)

[1515] Advertisement database: stores relevant advertisements

[1516] Natural Language Processing (NLP) engines: spaCy, Google Cloud NLP API, etc.

[1517] Sentiment engine: Microsoft Azure's Text Analytics API, IBM Watson's Tone Analyzer, etc.

[1518] Specific operation of the system

[1519] Posting and Submitting Questions

[1520] The user launches the application using their device and enters a legal question, which is then transmitted from the user's device to the server.

[1521] Receiving and parsing questions

[1522] The device sends the user's question to the server, which then uses a natural language processing (NLP) engine to analyze the question and extract key keywords and phrases.

[1523] Emotion recognition

[1524] The server uses an emotion engine to recognize emotions from the user's input. This emotion information is then associated with the analysis results of the question. The emotion engine determines emotions such as anger, sadness, joy, and surprise based on the user's vocabulary and sentence structure.

[1525] Querying the Knowledge Base

[1526] The server generates a query to the legal knowledge base based on the extracted keywords and the recognized sentiment, and uses the generated query to query the legal knowledge base.

[1527] Obtaining information and generating answers

[1528] The legal knowledge base responds to queries with relevant information. The server receives the information and generates a specific answer text tailored to the user's question and sentiment. The answer includes the necessary legal reasoning and advice.

[1529] Emotion-based ad selection

[1530] The server selects relevant ads from an advertising database based on the user's emotions, and embeds them in the answer depending on the user's emotions. For example, if the user is angry, an ad that conveys a more calming or reassuring feeling will be selected.

[1531] Submit your answer

[1532] The server constructs response data containing the final answer and advertisement, which is then sent to the user's device.

[1533] Receiving and notifying responses

[1534] The device receives the response data from the server and uses the notification function to notify the user that a response has been received.

[1535] Show Answers

[1536] Users tap the notification on their device, open the application, and view the response and advertisement content, which they can then take appropriate action based on.

[1537] Specific examples

[1538] Example 1: YouTube music question

[1539] The user types "Is it illegal to use music from YouTube in my videos?" into their device and sends the question. The server receives this question and extracts keywords such as "YouTube," "music," "video," and "illegal." At the same time, the emotion engine recognizes that the user is feeling anxious. Based on these keywords and emotion information, the server searches a legal knowledge base, obtains the information that "using music from YouTube requires permission from the copyright holder," and generates an answer. An advertisement for a copyright management service that gives a sense of security in line with the emotion is selected and sent to the user's device along with the answer. The user can check the answer and take the next step with peace of mind.

[1540] Example 2: Question about the legality of downloads

[1541] The user types "Is it illegal to download movies from this site?" into their device and sends the question. The server receives this question and extracts keywords such as "site," "movie," "download," and "illegal." At the same time, the emotion engine recognizes that the user has doubts. Based on these keywords and emotion information, the server searches the legal knowledge base to obtain the information that "Under copyright law, downloading movies without permission is illegal," and generates an answer. An advertisement for a related copyright protection tool is selected and sent to the user's device along with the answer. The user can check the answer and take appropriate action.

[1542] Prompt Sentence Examples

[1543] For example, a prompt for a generative AI model might look like this:

[1544] "Analyze the legal question entered by the user and generate relevant answers and sentiment-based ads, but also provide the sentiment the user is feeling about the question."

[1545] This system allows users to receive fast, accurate, and emotionally sensitive legal information, allowing them to take their next steps with confidence.

[1546] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1547] Step 1:

[1548] The user uses the terminal to launch the application and enter the legal questions.

[1549] What it does: A user launches the application and types a question into the question field, for example, "Is it illegal to use music from YouTube in my videos?"

[1550] Input: Legal question text.

[1551] Output: The question data sent from the device to the server.

[1552] Step 2:

[1553] The terminal transmits the entered question to the server.

[1554] Specific operation: The device sends the question text to the server as an HTTP POST request.

[1555] Input: The question text entered on the terminal.

[1556] Output: The HTTP request sent to the server.

[1557] Step 3:

[1558] The server analyzes the received question using a natural language processing (NLP) engine and extracts key keywords.

[1559] Specific operation: The server uses an NLP engine (e.g., spaCy) to extract the main keywords: "YouTube," "music," "video," and "illegal."

[1560] Input: The question text in the HTTP request.

[1561] Output: A list of extracted main keywords.

[1562] Step 4:

[1563] The server uses an emotion engine to recognize emotions from user input.

[1564] Specific operation: The server runs an emotion engine (for example, Microsoft Azure's Text Analytics API) and determines the user's emotion as "anxiety."

[1565] Input: Question text.

[1566] Output: Recognized emotion information.

[1567] Step 5:

[1568] The server generates a query to a legal knowledge base based on the extracted keywords and sentiment information.

[1569] What it does: The server generates a MySQL database query to search the legal knowledge base for the keywords "YouTube," "music," "video," and "illegal."

[1570] Input: Extracted keywords and sentiment information.

[1571] Output: A query against the legal knowledge base.

[1572] Step 6:

[1573] It retrieves information from a legal knowledge base in response to a query and generates a specific answer text.

[1574] Specific operation: The legal knowledge base returns the information that "YouTube requires permission from the copyright holder to use music," and the server creates an answer text based on this information.

[1575] Input: The generated query.

[1576] Output: The specific answer text.

[1577] Step 7:

[1578] The server selects an appropriate advertisement from an advertisement database according to the emotion information.

[1579] Specific operation: The server recognizes that the emotion is "anxiety" and selects an advertisement for a copyright management service that gives a sense of security.

[1580] Input: Recognized emotion information.

[1581] Output: The selected ad.

[1582] Step 8:

[1583] The server sends the final answer and advertisement to the user's terminal.

[1584] Specific operation: The server combines the selected advertisement and answer and sends it to the user's device as an HTTP response.

[1585] Input: Answer text and selected ad.

[1586] Output: The HTTP response sent to the user's device.

[1587] Step 9:

[1588] The user's terminal receives the response data from the server and notifies the user.

[1589] Specific behavior: The device displays a push notification to inform the user that a response has been received.

[1590] Input: HTTP response data from the server.

[1591] Output: A push notification to the user.

[1592] Step 10:

[1593] Users tap the notification to open the application and view the answers and advertisements.

[1594] What happens: The user sees the answer "YouTube music usage requires permission from the copyright holder" within the application, along with an advertisement for a related copyright management service.

[1595] Input: Answers and ad data displayed by user actions.

[1596] Output: The answer and advertisement that the user sees.

[1597] (Application example 2)

[1598] 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."

[1599] Current virtual store legal consultation systems are unable to provide answers that take users' emotions into account and are unable to meet users' needs for personalized advice. As a result, users are unable to receive appropriate advice and find it difficult to resolve their legal questions and concerns. Furthermore, they are unable to select relevant advertisements based on users' emotions, which reduces the quality of the user experience.

[1600] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a legal question from a user's terminal, means for analyzing the received question and extracting key keywords, means for querying a legal knowledge base based on the extracted keywords and obtaining relevant information, means for generating an answer based on the obtained information, means for recognizing the user's emotions, means for personalizing the answer based on the recognized emotions, means for selecting and embedding advertisements related to the generated answer, and means for transmitting the answer to the user's terminal. This makes it possible to provide personalized legal advice that takes user emotions into consideration and to appropriately select relevant advertisements.

[1601] "User device" refers to an electronic device, such as a smartphone, tablet, head-mounted display, or personal computer, that a user uses to input and receive questions.

[1602] A "legal question" is text or voice input that a user makes into a device to request information or advice about a legal matter.

[1603] "Analysis" is the process of categorizing incoming legal questions using natural language processing techniques and extracting key keywords and phrases.

[1604] "Keywords" are words or phrases that are particularly important in a legal question and form the basis for querying a legal knowledge base.

[1605] A "legal knowledge base" is a database that systematically collects legal information and provides relevant legal information in response to a query.

[1606] A "query" refers to an inquiry made to a legal knowledge base to obtain information.

[1607] "Retrieving information" means querying a legal knowledge base to retrieve relevant law or advice and gathering it to answer the user's question.

[1608] "Generating an answer" means creating a specific and appropriate response to a user's question based on the legal information obtained.

[1609] "Emotion recognition" means using natural language processing and artificial intelligence technology to determine a user's emotional state from their questions and voice.

[1610] "Personalizing" means tailoring responses to the user's emotions and specific circumstances to provide individually optimized answers.

[1611] "Selecting ads" means selecting relevant ads from an ad database based on the user's emotions and questions.

[1612] "Embedding advertisements in answers" means placing selected advertisements in appropriate positions within the generated legal answer text.

[1613] "Sending an answer" refers to the process of sending the generated answer and related advertisement to the user's terminal using telecommunications means.

[1614] The present invention provides a legal consultation assistant system that allows users to obtain immediate and personalized answers to legal questions in a virtual store. A detailed description of specific embodiments of the system is provided below.

[1615] Overall system configuration

[1616] The system consists of the following components:

[1617] 1. User's Device

[1618] 2. Server

[1619] 3. Legal Knowledge Base

[1620] 4. Advertising Database

[1621] 5. Emotion Recognition Engine

[1622] User's device

[1623] The user's device is an electronic device such as a smartphone, tablet, or head-mounted display. An application is installed on the device that allows the user to enter legal questions. The application includes a text box for entering questions and voice recognition capabilities.

[1624] server

[1625] The server receives legal questions sent from the user's device, analyzes them, recognizes emotions, generates queries to a legal knowledge base to obtain information, and finally performs a series of processes to generate personalized answers. The specific technologies used are as follows:

[1626] Natural language processing engine: Analyzes user questions and extracts key keywords. This uses technologies such as Google Cloud NLP API.

[1627] Emotion Recognition Engine: Analyzes emotions from the content of the user's question using technologies such as IBM Watson Tone Analyzer.

[1628] Legal knowledge base: Collects legal information and provides the required information based on queries. This can be done using APIs such as LexisNexis.

[1629] Advertisement database: Selects ads based on user sentiment and questions. This is done using Google Ads API, etc.

[1630] Legal Knowledge Base

[1631] A legal knowledge base is a database that comprehensively stores legal information and provides relevant information in response to queries. It is used by the server to retrieve appropriate legal information for a user's question by issuing a query based on keywords extracted from a natural language processing engine.

[1632] Advertising Database

[1633] The advertisement database stores relevant advertisements based on the user's question and sentiment, and the server selects appropriate advertisements from this database and embeds them into the generated answer.

[1634] Emotion Recognition Engine

[1635] The emotion recognition engine is used to analyze emotions from the user's text and voice, thereby personalizing responses to match the user's emotional state.

[1636] Operation flow

[1637] As an example, here is the prompt text when a user asks, "Can I get a refund for an item I purchased from your virtual store?"

[1638] Prompt Sentence Examples

[1639] A user asks: "Can I get a refund for an item I purchased from your virtual store?"

[1640] NLP analysis results:

[1641] Keywords: [Virtual store, purchase, product, refund, possible]

[1642] Emotion recognition:

[1643] User sentiment: [Anxiety]

[1644] Query the legal knowledge base:

[1645] SELECT FROM legal_knowledge_base WHERE keywords IN ('virtual store', 'purchase', 'refund') AND sentiment = 'anxiety'

[1646] Answer generation:

[1647] Refunds for items purchased from virtual stores are subject to the store's refund policy. Generally, refunds may be granted for unused items within 14 days of purchase. Please check the store's terms and conditions for details.

[1648] Ad Selection:

[1649] For a safer shopping experience, use our app to check refund policies before you buy. Download it here.

[1650] In this way, the present invention is a system that takes into consideration the user's feelings and provides specific and appropriate legal advice, thereby contributing to alleviating the user's anxieties and doubts.

[1651] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1652] Step 1:

[1653] The user enters a legal question into the terminal and presses the send button, which sends the entered question text (input: user's question text, output: data sent to the server).

[1654] Step 2:

[1655] The device sends the user's question to the server, which receives the data and passes it to a natural language processing engine (input: user's question text, output: data received by the server).

[1656] Step 3:

[1657] The server uses a natural language processing engine to analyze the received question and extract key keywords. Specifically, it uses the Google Cloud NLP API to analyze the question text and extract important keywords and phrases (input: question text received by the server, output: extracted keywords).

[1658] Step 4:

[1659] The server uses an emotion recognition engine to analyze the emotion in the user's question text. Specifically, it uses IBM Watson Tone Analyzer to determine the emotional state of the question text (anger, anxiety, joy, etc.) (input: question text, output: recognized emotional state).

[1660] Step 5:

[1661] The server generates a query to a legal knowledge base based on the extracted keywords and the recognized sentiment. The generated SQL query is sent to the legal knowledge base (e.g., LexisNexis API) to retrieve the relevant information (input: keywords and sentiment state, output: legal information query results).

[1662] Step 6:

[1663] The server generates a specific answer text to the user's question based on the obtained legal information, and adjusts the answer to include the most appropriate advice for the user by taking into account emotional information (input: legal information from the query result, output: generated answer text).

[1664] Step 7:

[1665] The server selects relevant ads from an advertising database based on the generated answer text. Specifically, it uses the Google Ads API or similar to search for and retrieve ads that match the user's emotions and question content (input: generated answer text and emotional state, output: selected ads).

[1666] Step 8:

[1667] The server combines the generated answer text and the associated advertisement to construct the final response data to send to the user (input: generated answer text and selected advertisement; output: final response data).

[1668] Step 9:

[1669] The server sends the constructed final response data to the user's terminal (input: final response data, output: data to be sent to the user's terminal).

[1670] Step 10:

[1671] The user's device receives the response data from the server and displays a notification to the user using the notification function (input: response data from the server, output: notification to the user).

[1672] Step 11:

[1673] The user taps the notification on their device, opens the application, and checks the answer and advertisement content (input: notification data, output: displayed answer and advertisement).

[1674] 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.

[1675] 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.

[1676] 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.

[1677] 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.

[1678] 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.

[1679] 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.

[1680] 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).

[1681] 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.

[1682] 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."

[1683] 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.

[1684] 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).

[1685] 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.

[1686] 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.

[1687] 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.

[1688] 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.

[1689] 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.

[1690] 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.

[1691] 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.

[1692] 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.

[1693] 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.

[1694] 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.

[1695] The following is further disclosed regarding the above embodiment.

[1696] (Claim 1)

[1697] a means of receiving legal inquiries from the user's device;

[1698] A means of analyzing received questions and extracting key keywords;

[1699] a means for querying a legal knowledge base based on the extracted keywords to retrieve relevant information;

[1700] a means for generating a response based on the acquired information;

[1701] means for selecting and embedding advertisements relevant to the generated answers;

[1702] The system includes a means for transmitting the answer to the user's terminal.

[1703] (Claim 2)

[1704] 10. The system of claim 1, wherein the question is analyzed by a natural language processing engine.

[1705] (Claim 3)

[1706] 2. The system of claim 1, wherein the system notifies the user of receipt of the response using a notification function on the user's terminal.

[1707] "Example 1"

[1708] (Claim 1)

[1709] a means of receiving legal inquiries from the user's device;

[1710] A means of analyzing received questions and extracting key keywords;

[1711] a means for querying a legal knowledge base based on the extracted keywords to retrieve relevant information;

[1712] a means for generating a response based on the acquired information;

[1713] means for selecting and embedding advertisements relevant to the generated answers;

[1714] a means for transmitting the response to the user's terminal;

[1715] a means for generating the generated answer using a generative AI model;

[1716] The system includes a means for notifying the user of receipt of a response using a notification function on the user's terminal.

[1717] (Claim 2)

[1718] 10. The system of claim 1, wherein the question is analyzed by a natural language processing engine.

[1719] (Claim 3)

[1720] 10. The system of claim 1, further comprising means for embedding advertisements related to the generated answers based on relevance.

[1721] "Application Example 1"

[1722] (Claim 1)

[1723] a means of receiving legal inquiries from the user's device;

[1724] A means of analyzing received questions and extracting key keywords;

[1725] a means for querying a legal knowledge base based on the extracted keywords to retrieve relevant information;

[1726] a means for generating a response based on the acquired information;

[1727] means for selecting and embedding advertisements relevant to the generated answers;

[1728] A means to enable store staff to ask legal questions using their smartphones;

[1729] A means of providing legal information on consumer protection laws and labor standards laws;

[1730] The system includes a means for transmitting the answer to the user's terminal.

[1731] (Claim 2)

[1732] 10. The system of claim 1, wherein the question is analyzed by a natural language processing engine.

[1733] (Claim 3)

[1734] 2. The system of claim 1, wherein the system notifies the user of receipt of the response using a notification function on the user's terminal.

[1735] "Example 2: Combining Emotion Engines"

[1736] (Claim 1)

[1737] a means of receiving legal inquiries from the user's device;

[1738] A means of analyzing received questions to extract key keywords and user sentiment;

[1739] a means for generating queries to a legal knowledge base based on the extracted keywords and sentiments to retrieve relevant information;

[1740] means for generating an answer based on the acquired information and sentiment;

[1741] means for selecting and embedding advertisements based on sentiment associated with the generated answers;

[1742] means for transmitting the answers and advertisements to the user's terminal;

[1743] The system includes a means for notifying the user of receipt of a response using a notification function on the user's terminal.

[1744] (Claim 2)

[1745] 10. The system of claim 1, wherein the questions and emotions are analyzed with a natural language processing engine and an emotion recognition engine.

[1746] (Claim 3)

[1747] 10. The system of claim 1, wherein the generated answers and advertisements are personalized based on user sentiment.

[1748] "Application example 2 when combining emotion engines"

[1749] (Claim 1)

[1750] a means of receiving legal inquiries from the user's device;

[1751] A means of analyzing received questions and extracting key keywords;

[1752] a means for querying a legal knowledge base based on the extracted keywords to retrieve relevant information;

[1753] a means for generating a response based on the acquired information;

[1754] a means of recognizing a user's emotions;

[1755] a means of personalizing responses based on perceived sentiment;

[1756] means for selecting and embedding advertisements relevant to the generated answers;

[1757] The system includes a means for transmitting the answer to the user's terminal.

[1758] (Claim 2)

[1759] 10. The system of claim 1, wherein the question is analyzed by a natural language processing engine.

[1760] (Claim 3)

[1761] 2. The system of claim 1, wherein the system notifies the user of receipt of the response using a notification function on the user's terminal. [Explanation of symbols]

[1762] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means of receiving legal inquiries from the user's device; A means of analyzing received questions and extracting key keywords; a means for querying a legal knowledge base based on the extracted keywords to retrieve relevant information; a means for generating a response based on the acquired information; means for selecting and embedding advertisements relevant to the generated answers; and means for transmitting the answer to the user's terminal.

2. 10. The system of claim 1, wherein the question is analyzed by a natural language processing engine.

3. 2. The system according to claim 1, wherein the system notifies the user of receipt of the reply using a notification function on the user's terminal.

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