system

Abstract

We provide the system. [Solution] A means of receiving information via a communication means, Based on the aforementioned information, a means for searching for relevant information using natural language processing, A means for generating proposals based on the aforementioned related information using generative artificial intelligence, Means for transmitting the above proposal via communication means, A means of processing transactions through electronic payment services, A system that includes a means to select a payment method and complete a transaction.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When many ordinary citizens need legal advice, they face the situation that it is difficult to access legal experts. This problem is particularly caused by the fact that legal advice is expensive or the expertise is limited, and they try to solve problems by their own judgment. Under such circumstances, there is a need for a method to obtain legal advice quickly and at a low cost.

Means for Solving the Problems

[0005] To address the aforementioned challenges, the present invention provides a system that receives legal inquiry information via a communication device and retrieves relevant legal information from a database using natural language processing. Furthermore, it generates solutions based on the acquired legal information using generative artificial intelligence and transmits these solutions again via the communication device. This allows users to receive legal advice repeatedly and at low cost at any time, and to easily complete payments through mobile payment services. Such a system can significantly reduce the barriers to accessing legal consultations.

[0006] "Communication equipment" refers to devices used to send and receive information between users and servers, and specifically refers to mobile devices and computers.

[0007] "Legal inquiry information" refers to text data in which users enter questions or consultations based on legal principles.

[0008] "Natural language processing" refers to the technology that allows computers to understand and analyze human language, and in particular, the technology that extracts meaning from text.

[0009] A "database" refers to a collection of information in which legal documents, case law information, and other similar data are systematically stored.

[0010] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to analyze data and generate new information and solutions.

[0011] A "solution" refers to specific actions or advice derived in response to a particular legal inquiry.

[0012] "Mobile payment services" refer to services that allow payments to be processed online using mobile devices such as smartphones. [Brief explanation of the drawing]

[0013] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

[0016] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention provides a system that allows users to easily consult about legal questions and problems. The system mainly consists of three components: a server, a terminal, and a user.

[0035] The user first launches a social networking app on their device, enters their legal question in text format, and sends it. This message is then packaged by the device and sent to the server via the internet.

[0036] The server analyzes received messages using natural language processing (NLP) techniques, extracting relevant legal keywords and context from their content. This allows for a detailed understanding of the user's intent. Next, the server accesses a database to search for information such as laws, precedents, and guidelines related to the extracted keywords.

[0037] Based on the information obtained through the search, the server utilizes artificial intelligence to generate solutions tailored to the user's situation. These generated solutions are not mere generalities, but specific advice tailored to the user's inquiry.

[0038] The generated solutions are then sent back to the device via SNS by the server. Users can review the advice displayed on their device, ask further questions if necessary, and receive advice repeatedly.

[0039] Furthermore, the payment process has been simplified, allowing users to easily pay for the legal advice provided using mobile payment services. This enables users to receive legal assistance quickly and easily.

[0040] For example, if a user wants to know "how to deal with problems that arise regarding the renewal of a lease agreement," the system will provide appropriate solutions based on the latest relevant laws and precedents through the server. This allows users to quickly address problems, saving them time and effort.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user opens a social networking app on their device, types a text message requesting legal advice, and sends it. The device then sends this message to the server.

[0044] Step 2:

[0045] The server receives messages via the SNS protocol. The received messages are then input into the natural language processing (NLP) module.

[0046] Step 3:

[0047] The server uses an NLP module to analyze the message, extracting legally relevant keywords and context.

[0048] Step 4:

[0049] The server searches the database based on the extracted keywords and retrieves relevant legal documents, precedents, and guidelines.

[0050] Step 5:

[0051] The server uses artificial intelligence based on acquired legal information to generate solutions related to the user's questions.

[0052] Step 6:

[0053] The server formats the generated solution as a text message and sends it to the terminal according to the SNS protocol.

[0054] Step 7:

[0055] The device receives messages from the server and displays them within the SNS app. Users can review the provided solutions, enter any further questions, and repeat the process from step 1.

[0056] Step 8:

[0057] If the user is satisfied with the consultation, they will use their device to activate the mobile payment service and follow the instructions to complete the payment. The server will then verify the payment information and complete the transaction.

[0058] (Example 1)

[0059] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0060] There is a need for a system that can respond quickly and accurately to legal inquiries. However, existing legal inquiry response systems have struggled to provide detailed solutions tailored to users' specific problems, and the complex payment procedures involved have sometimes compromised user convenience. Therefore, a system is needed that accurately understands user needs, provides specific solutions quickly based on those needs, and has a simple payment process.

[0061] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0062] In this invention, the server includes means for receiving legal question information via a communication device, means for analyzing the legal question information using natural language processing technology and extracting legal keywords, means for searching for relevant legal information from data storage based on the legal keywords, means for generating solutions based on the relevant legal information using generative machine learning, means for transmitting the solutions via the communication device, and means for processing transactions using an electronic payment service. This enables the rapid provision of detailed solutions tailored to the user's specific questions, making it easy for users to receive legal assistance.

[0063] "Communication equipment" is a general term for hardware or software used to send and receive data, and it plays a role in connecting users and servers.

[0064] "Legal inquiry information" refers to text data, audio data, and other information entered by users seeking legal advice or information.

[0065] "Natural language processing technology" is a technology that enables machines to understand and analyze human language, and it is used to extract legal keywords.

[0066] "Legal keywords" are legal terms and phrases, and are an important element in understanding the intent of the user's question.

[0067] "Data storage" refers to a digital medium or system for storing data, specifically information on laws and precedents.

[0068] "Relevant legal information" refers to data on laws, precedents, or guidelines that are relevant to the user's question.

[0069] Generative machine learning is a type of artificial intelligence technology that generates new data and answers through pattern learning.

[0070] "Solution" refers to a response that includes specific advice and instructions provided regarding a user's legal issues.

[0071] An "electronic payment service" is a system for conducting monetary transactions over the internet and has the function of processing payments from users.

[0072] This invention is a system that provides quick and accurate solutions to legal questions. The system mainly consists of three components: a server, a terminal, and a user.

[0073] User

[0074] Users launch a social networking app on their devices and enter legal questions. The entered questions are sent in text format. For example, a specific question might be, "What legal options are available if my rent renewal is refused?"

[0075] terminal

[0076] The terminal packages the text data entered by the user and securely transmits it to the server over the internet. This process uses a secure communication protocol (e.g., HTTPS) to encrypt the data.

[0077] server

[0078] The server decodes the received data and analyzes the question using natural language processing techniques. A generative AI model (e.g., GPT-4®) is used for the analysis, extracting legal keywords and context from the text. This analysis allows for a detailed understanding of the user's intent behind the question.

[0079] The server then accesses dedicated data storage and searches for legal information (laws, precedents, guidelines) related to the extracted keywords. Based on the search results, the server applies generative machine learning to generate specific solutions to provide to the user. These generated solutions are not merely general legal knowledge, but are tailored to the user's question.

[0080] The generated solution is then sent back to the user's device via the SNS app by the server. The user can review the advice displayed on their device and contact the server again if they have any further questions.

[0081] Furthermore, users can easily pay for the legal advice provided using the electronic payment function. This system enables users to obtain legal advice quickly, significantly reducing the time and effort required to deal with legal issues.

[0082] As a concrete example, an example of a prompt is as follows: "Please provide legal advice regarding the refusal to renew rent. I need a detailed guide tailored to my specific case." This prompt enables the generated AI model to provide the user with accurate legal advice.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] User

[0086] The user launches a social networking app on their device to enter a legal question. The specific question entered is a legal issue, such as "What legal options are available if my rent renewal is refused?" The entered data is saved directly on the device. The input data consists of the user's legal questions and concerns in text format.

[0087] Step 2:

[0088] terminal

[0089] The terminal packages the text data entered by the user. This process includes encryption and data formatting. The packaged data is then sent to the server via the internet. The input is the user's question data, and the output is the encrypted packaged data.

[0090] Step 3:

[0091] server

[0092] The server receives packaged data sent from the terminal. First, the data is decrypted and returned to its original text format. Next, natural language processing is performed using a generative AI model to extract legal keywords and context. This allows for a detailed understanding of the user's question intent. The input is encrypted packaged data, and the output is legal keywords and contextual information.

[0093] Step 4:

[0094] server

[0095] The server uses the extracted legal keywords to search its internal database for relevant legal information. The database contains the latest laws, precedents, and guidelines. The search results are output as a set of information relevant to the user's question. The input is legal keywords, and the output is the relevant legal information.

[0096] Step 5:

[0097] server

[0098] The server uses a generative machine learning model to generate specific solutions based on the acquired legal information. A prompt such as "Please provide legal advice regarding the refusal to renew rent" is used as the generation prompt. This process generates solutions tailored to the user's specific problem. The input is relevant legal information, and the output is a specific legal solution.

[0099] Step 6:

[0100] server

[0101] The server repackages the generated solution and reliably sends it to the terminal. All data is re-encrypted before transmission. The input is the solution data, and the output is the encrypted transmission data.

[0102] Step 7:

[0103] User

[0104] The user receives the solution on their device and reviews its contents. If they have further questions, they can inquire again. This allows the user to receive ongoing legal support. The input is the received solution, and the output is the user's feedback or additional questions.

[0105] (Application Example 1)

[0106] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0107] There is a need for a system that can respond quickly and specifically to legal questions and problems. Existing systems have a cumbersome legal consultation process, and payment after the consultation is cumbersome. Furthermore, there is a need for a service that offers the flexibility to allow users to receive advice multiple times.

[0108] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0109] In this invention, the server includes means for receiving information via communication means, means for searching for relevant information using natural language processing, means for generating suggestions based on the relevant information using generative artificial intelligence, and means for selecting a payment method by selection means and completing the transaction. This allows users to smoothly conduct legal consultations and complete payments with a single tap. Furthermore, since advice can be received multiple times, continuous legal support is available.

[0110] "Communication means" refers to devices or technologies for sending and receiving information with other devices or systems.

[0111] "Natural language processing" is a technology that uses computers to analyze and understand human language.

[0112] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to generate new information and suggestions based on input data.

[0113] "Relevant information" refers to legal or other knowledge necessary for resolving the issue, derived from the information received.

[0114] A "proposal" is a solution or advice presented for a specific problem.

[0115] An "electronic payment service" is a technology or platform that enables online payments.

[0116] A "selection mechanism" is a system that allows users to choose their preferred option from multiple choices.

[0117] "Completing a transaction" means ending a series of procedures related to the purchase of goods or the provision of services.

[0118] The system for implementing this invention mainly consists of a server, a terminal, and a user. First, the user uses the terminal to input legal questions in text format from a specific social networking application. The terminal packages this information and sends it to the server via the internet.

[0119] The server analyzes the received information using natural language processing techniques. Libraries used for this analysis include, for example, SpaCy and NLTK. After analysis, it extracts relevant legal information and searches for necessary laws and precedents using a database. The server also uses generative artificial intelligence techniques to generate specific solutions tailored to the user's situation from the collected information. Here, OpenAI's generative model (e.g., GPT) is used.

[0120] The generated solutions are then sent back to the user's device via social media. The user can review the solutions displayed on their device and ask further questions if necessary. Furthermore, if payment is required, they can easily complete the transaction using their preferred electronic payment service from the options displayed on their device. Examples of payment services that can be used include Apple Pay and Google Pay®.

[0121] For example, if a user wants to know "how to deal with problems that arise regarding the renewal of a lease agreement," the server will provide a solution based on the latest relevant laws and precedents, and after the procedure is completed, the service fee can be paid immediately with Apple Pay.

[0122] Examples of prompts for a generative AI model include the following:

[0123] "Question from a user: What are some solutions for problems with lease renewals?"

[0124] The model generates the following: Please provide legal solutions related to this question.

[0125] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0126] Step 1:

[0127] Users launch a social networking app on their devices and enter legal questions in text format. In this scenario, user input consists of specific legal questions, which are then stored as digital data on the device.

[0128] Step 2:

[0129] The terminal sends the entered question as a data packet to the server over the internet. In this step, the entered text data is packetized and routed to the specified address on the server.

[0130] Step 3:

[0131] The server interprets the received data and extracts legal keywords and context using natural language processing techniques. The input is the user's raw text, and the output is the identified keywords and contextual information. The techniques used here include, for example, SpaCy and NLTK.

[0132] Step 4:

[0133] The server searches the database for relevant legal information based on keywords. This process involves querying the database and extracting relevant laws and precedents. The input is keywords, and the output is relevant legal information.

[0134] Step 5:

[0135] The server uses generative artificial intelligence technology to generate solutions from extracted legal information. Here, a generative AI model (e.g., GPT) generates advice specific to the question and provides it as output.

[0136] Step 6:

[0137] The server sends the generated solutions to the user's device via the SNS app. The input here is the text data of the generated solutions, which is converted into a format that can be displayed on the device.

[0138] Step 7:

[0139] Users can view solutions displayed on their device and enter additional questions as needed. They can also immediately pay any fees incurred for legal consultation using the displayed payment options. Input is the user's selection, and output is a confirmation message upon completion of the transaction. Electronic payment services (such as Apple Pay and Google Pay) are used here.

[0140] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0141] This invention provides a system that allows users to easily ask legal questions and seek advice, while also understanding the user's emotions and providing solutions tailored to those emotions. This system includes three components: a server, a terminal, and the user, and further incorporates an emotion engine.

[0142] Users launch a social networking app on their device and enter their legal questions in text format. The message is sent from the device to the server. Here, the text entered by the user may include not only legal information but also emotional nuances.

[0143] When the server receives a message, it first uses natural language processing (NLP) to analyze legal keywords. This helps it understand the user's intent and the content of their inquiry. Next, it uses an emotion engine to identify the user's emotional state from the message. This information is used in subsequent processes to generate solutions.

[0144] Based on keywords analyzed by the server, it accesses a database to search for relevant legal information and precedents. Based on these search results, the generating artificial intelligence generates solutions. Here, the user's emotional state, as recognized by the emotion engine, is taken into consideration. For example, if the user is feeling anxious, the server adjusts its solution to provide a greater sense of reassurance.

[0145] The generated solutions are formatted as text messages and sent back to the device. The user can review the advice displayed on the device and enter any further questions they may have.

[0146] Furthermore, if the user is satisfied with the advice provided, they can easily make a payment using their device. Once the transaction is completed using the mobile payment service, the server verifies the information and closes the transaction.

[0147] For example, if a user is struggling to reach an agreement with their spouse during divorce mediation, the emotion engine can be used to provide empathetic advice to alleviate the user's stress and anxiety. By providing emotionally sensitive legal support in this way, a more personalized experience can be offered to the user.

[0148] The following describes the processing flow.

[0149] Step 1:

[0150] Users launch a social networking app on their device, type a message requesting legal advice, and send it. The sent message may reflect not only legal questions but also the user's emotions.

[0151] Step 2:

[0152] The terminal sends messages from the user to the server. These messages reach the server via the communication network.

[0153] Step 3:

[0154] The server analyzes the received message, using natural language processing (NLP) techniques to extract legal keywords and context. This allows the server to understand the content of the consultation.

[0155] Step 4:

[0156] The server uses an emotion engine to analyze the user's emotions within a message. The emotion engine identifies emotional states such as joy, anxiety, and anger.

[0157] Step 5:

[0158] The server uses the extracted keywords to search the database for relevant legal information and case law. Based on these results, it prepares to create legal solutions.

[0159] Step 6:

[0160] The server utilizes a generative artificial intelligence model to generate solutions that take into account the user's emotions, as recognized by the emotion engine. This provides legal advice that is appropriate to the user's emotions.

[0161] Step 7:

[0162] The server formats the generated solution as a text message and sends it to the user's terminal to inform them of the solution.

[0163] Step 8:

[0164] The device receives messages from the server and displays them within the SNS app. Users can review the advice provided, enter new messages if they have further questions, and repeat the process from step 1.

[0165] Step 9:

[0166] If the user is satisfied with the advice, they will use their device to initiate the mobile payment service and process the payment according to the instructions. Once the payment is complete, the server will verify the information and complete the transaction.

[0167] (Example 2)

[0168] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0169] In modern society, there is a need for a system that can respond quickly and appropriately to legal questions and consultations from individuals. However, many current systems do not take into account the user's feelings and have difficulty providing personalized legal advice. Furthermore, generating legal solutions requires specialized knowledge, making it difficult for the average user to access. This invention aims to solve these problems and provide a user-friendly and accessible legal consultation system.

[0170] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0171] In this invention, the server includes means for receiving legal inquiry information via communication equipment, means for analyzing legal terminology using natural language processing, and means for performing sentiment analysis to identify emotional states. This enables users to receive personalized legal support that takes their emotional state into consideration quickly and efficiently.

[0172] "Communication equipment" refers to devices and infrastructure used for sending and receiving data.

[0173] "Legal inquiry information" refers to data that includes legal questions and consultations.

[0174] "Natural language processing" refers to the technology that enables computers to understand and analyze human language.

[0175] "Legal terminology" refers to specific keywords or phrases related to laws and precedents.

[0176] "Emotional analysis" refers to the process of identifying a user's emotional state from text data or audio data.

[0177] An "information aggregation structure" refers to a data structure that systematically organizes large amounts of data, enabling the efficient retrieval of necessary information.

[0178] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to generate new information and content based on given data and instructions.

[0179] A "solution" refers to information that describes specific measures and advice regarding the user's legal issues.

[0180] An "electronic payment system" refers to the technology and platform used to complete payments in a digital format.

[0181] This invention provides personalized solutions that take into account the user's emotions when they ask legal questions or seek advice. Specific embodiments are shown below.

[0182] Users launch communication software using their mobile devices or computers and input their legal inquiries in text format. This text is expected to include not only specific legal information but also context that reflects the user's emotions. For example, a sentence like, "I am anxious about divorce and child custody issues."

[0183] The terminal sends this entered text to the server via the internet. It is recommended to use a secure communication protocol for this transmission.

[0184] The server first processes the received text message using a natural language processing engine to analyze legal keywords. The analyzed data provides crucial information for understanding the user's inquiry.

[0185] Next, the server uses an emotion engine to identify the user's emotional state. This process allows it to determine whether the user is feeling anxious or stressed, or conversely, at ease.

[0186] Subsequently, the server uses the database system to search for relevant legal information and case precedents based on the analyzed keywords. By using query languages ​​such as SQL, data that matches the specified conditions can be retrieved quickly.

[0187] Based on the searched information, the server uses a generative AI model to generate legal solutions. During this process, the user's emotional state, as determined by sentiment analysis, is reflected, ensuring that advice is provided that takes the user's feelings into consideration.

[0188] The generated solution is sent back to the device, where the user can view it on the device's display. If the user wishes to add further questions, they can enter new text and send it again.

[0189] For example, if a user enters a question such as, "I want to know how legal advice can alleviate my anxiety," the system will identify the user's anxiety and return reassuring solutions to address it. An example of a prompt in this case would be, "Please provide advice for a user who is having difficulty reaching an agreement in divorce mediation. The user is feeling anxious."

[0190] This allows users to receive legal support that reflects their own feelings, enabling them to deal with legal issues with greater peace of mind.

[0191] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0192] Step 1:

[0193] The user enters legal questions into the terminal. The input is in text format and includes specific legal questions and consultations. An example of input might be, "What should I do if my rent payments are consistently behind?" The terminal temporarily stores the entered text data for use in subsequent processes.

[0194] Step 2:

[0195] The terminal sends the entered text data to the server. A secure communication protocol (e.g., HTTPS) is used for transmission to protect personal information. The transmitted data arrives at the server and is prepared for the next processing step.

[0196] Step 3:

[0197] The server passes the received text data to a natural language processing engine for analysis. The input here is text sent by the user, and the output extracts legal keywords and the user's intent. Specifically, morphological analysis and keyword extraction are performed.

[0198] Step 4:

[0199] The server uses an emotion analysis engine to identify the emotions contained in the message. The input is the user's text data, and the output is the user's emotional state (e.g., anxiety, relief, anger). This step involves emotion scoring and emotion classification of words.

[0200] Step 5:

[0201] The server uses the analyzed keywords to search the database for relevant legal information and case precedents. A database query is executed, with the analyzed keywords as input and the corresponding legal information as output. Specifically, information retrieval is performed using SQL.

[0202] Step 6:

[0203] The server generates legal solutions using a generative AI model based on search results and sentiment analysis data. The input is relevant legal information and user sentiment data, and the output is a solution tailored to the user's problem. In this process, the generative AI model constructs new solutions based on the data it has learned.

[0204] Step 7:

[0205] The server sends the generated solution to the terminal. The terminal receives it and displays it to the user in an appropriate format. A secure communication protocol is used again for transmission, and the solution is output as text. The user can review this and ask for further assistance if necessary.

[0206] Step 8:

[0207] If the user is satisfied with the advice provided, they will make a payment using the electronic payment system via the terminal. The input is the user's payment information, and the output is a notification that the payment has been completed. In this step, the transaction is processed through the payment gateway.

[0208] (Application Example 2)

[0209] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0210] A challenge exists in legal consultations where users are left feeling anxious and stressed because appropriate solutions are not provided that take their emotional state into consideration. Furthermore, the cumbersome payment process for related services after legal consultations can negatively impact the user experience. There is a need for a system that addresses these issues and provides emotionally sensitive legal consultations and smooth payment processes.

[0211] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0212] In this invention, the server includes means for receiving legal inquiry information and sentiment information via communication equipment; means for retrieving relevant legal information and sentiment information from a data store using natural language processing based on the legal inquiry information and sentiment information; means for generating solutions based on the relevant legal information and sentiment information using generative artificial intelligence; means for processing transactions through electronic transaction services; and means for providing a sentiment-sensitive user interface. This enables users to receive legal consultation in a sentiment-sensitive manner and to smoothly settle related services after the consultation.

[0213] "Communication equipment" refers to devices used by users to transmit legal inquiry information and emotional information.

[0214] "Legal inquiry information" refers to information that includes questions or consultations from users regarding legal matters.

[0215] "Emotional information" refers to information that indicates the user's emotional state, and is an important element when providing legal advice.

[0216] "Natural language processing" is a technology that enables computers to understand and process human language.

[0217] A "data store" is a recording medium that stores relevant legal and emotional information.

[0218] "Generative artificial intelligence" is a program that has the ability to generate new information and solutions based on data.

[0219] "Electronic transaction services" are services that allow for online transactions and payments.

[0220] "User interface" refers to the screens and means of operation that users use to interact with a system.

[0221] In order to implement this invention, it is necessary to effectively integrate communication equipment, servers, data stores, emotion engines, generative artificial intelligence, and electronic trading services.

[0222] System Configuration

[0223] Users use communication devices such as smartphones and personal computers to send legal questions and information along with emotional information. The device provides an intuitive user interface, designed to allow users to easily input information.

[0224] Processing flow

[0225] The server analyzes legal inquiry and sentiment information received from communication devices. Using Python's NLTK library and Transformers API, it performs natural language processing to identify legal keywords and intent. It also uses Microsoft's Azure Text Analytics and other tools to perform sentiment recognition and evaluate the user's emotional state. Based on this evaluation, it retrieves relevant legal information from a data store and generates solutions using generative artificial intelligence such as OpenAI's GPT-3. These generated solutions are then delivered to the user through a user interface.

[0226] Electronic payment

[0227] Furthermore, after providing solutions, the use of electronic transaction services such as Stripe and Square allows for smooth payment processing of related service fees and product purchases.

[0228] Specific example

[0229] For example, if a user requests "legal advice about purchasing an apartment" and emotional information detects anxiety, the system will generate detailed advice to alleviate concerns and present options such as hiring a lawyer or purchasing relevant books. Furthermore, payment services can be used to easily complete payments for related services.

[0230] As an example of a prompt, you can instruct the generating AI in the following format: "User question: What are the key points to check in a contract when purchasing an apartment? Emotional state: Anxious. Please generate careful and helpful advice."

[0231] In this way, a system is realized that provides legal consultations that take emotions into consideration and smooth settlements.

[0232] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0233] Step 1:

[0234] The terminal captures legal questions and emotions entered by the user. The input includes the user's text information and emotional nuances. After capturing this information, it is sent to the server via a communication device. The output is the legal question and emotional data, which is sent to the server.

[0235] Step 2:

[0236] The server analyzes received legal inquiry information and sentiment information. It takes text data and sentiment information as input, and uses Python's NLTK to extract keywords from the text. Next, it uses Microsoft Azure Text Analytics to analyze the sentiment and obtain the results. The output includes a list of analyzed keywords and the sentiment analysis results.

[0237] Step 3:

[0238] The server searches the data store for relevant legal information based on the analyzed keywords. It takes a list of keywords as input and executes SQL queries to retrieve the relevant legal information. The output is a set of relevant legal information.

[0239] Step 4:

[0240] The server generates solutions using a generative artificial intelligence model. It takes relevant legal information and sentiment analysis results as input, and sends prompts to the generative AI model (e.g., OpenAI's GPT-3) to generate solutions. The output is a specific, sentiment-sensitive solution.

[0241] Step 5:

[0242] The terminal displays the generated solutions to the user. The input is the solution text from the server, and the information is displayed to the user through a user interface in an easy-to-understand format. The output provides the user with the opportunity to review the solutions and choose further actions.

[0243] Step 6:

[0244] The user selects the necessary related services and makes payments through the electronic transaction service via the terminal. Inputs include the selected service information and payment information, and the transaction is completed using APIs such as Stripe or Square. Outputs include payment confirmation messages provided to both the user and the system.

[0245] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0246] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0247] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0248] [Second Embodiment]

[0249] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0250] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0251] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0252] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0253] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0254] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0255] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0256] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0257] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0258] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0259] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0260] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0261] This invention provides a system that allows users to easily consult about legal questions and problems. The system mainly consists of three components: a server, a terminal, and a user.

[0262] The user first launches a social networking app on their device, enters their legal question in text format, and sends it. This message is then packaged by the device and sent to the server via the internet.

[0263] The server analyzes received messages using natural language processing (NLP) techniques, extracting relevant legal keywords and context from their content. This allows for a detailed understanding of the user's intent. Next, the server accesses a database to search for information such as laws, precedents, and guidelines related to the extracted keywords.

[0264] Based on the information obtained through the search, the server utilizes artificial intelligence to generate solutions tailored to the user's situation. These generated solutions are not mere generalities, but specific advice tailored to the user's inquiry.

[0265] The generated solutions are then sent back to the device via SNS by the server. Users can review the advice displayed on their device, ask further questions if necessary, and receive advice repeatedly.

[0266] Furthermore, the payment process has been simplified, allowing users to easily pay for the legal advice provided using mobile payment services. This enables users to receive legal assistance quickly and easily.

[0267] For example, if a user wants to know "how to deal with problems that arise regarding the renewal of a lease agreement," the system will provide appropriate solutions based on the latest relevant laws and precedents through the server. This allows users to quickly address problems, saving them time and effort.

[0268] The following describes the processing flow.

[0269] Step 1:

[0270] The user opens a social networking app on their device, types a text message requesting legal advice, and sends it. The device then sends this message to the server.

[0271] Step 2:

[0272] The server receives messages through the SNS protocol. The received messages are input into the natural language processing (NLP) module.

[0273] Step 3:

[0274] The server uses the NLP module to analyze the messages. Here, legal-related keywords and context are extracted.

[0275] Step 4:

[0276] The server searches the database based on the extracted keywords and obtains relevant legal documents, case laws, and guidelines.

[0277] Step 5:

[0278] The server uses an artificial intelligence generated based on the obtained legal information to generate a solution related to the user's question.

[0279] Step 6:

[0280] The server formalizes the generated solution as a text message and sends it to the terminal according to the SNS protocol.

[0281] Step 7:

[0282] The terminal receives the message from the server and displays it within the SNS application. The user can view the provided solution, and if there are further questions, they can enter them anew and repeat the process from Step 1.

[0283] Step 8:

[0284] When the user is satisfied with the consultation, they use the terminal to activate the mobile payment service and perform the payment process according to the instructions. The server verifies this payment information and completes the transaction.

[0285] (Example 1)

[0286] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0287] There is a need for a system that can respond quickly and accurately to legal questions. However, conventional systems for handling legal inquiries have difficulty providing detailed solutions tailored to the specific problems of users, and also involve complex settlement procedures, which may impair the convenience of users. Therefore, there is a need for a system that can accurately understand the needs of users, quickly provide specific solutions based on them, and have a simple settlement procedure.

[0288] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0289] In this invention, the server includes means for receiving legal question information via a communication device, means for analyzing the legal question information using natural language processing technology to extract legal keywords, means for searching for relevant legal information from a data storage based on the legal keywords, means for generating a solution based on the relevant legal information using generative machine learning, means for transmitting the solution via a communication device, and means for processing transactions using an electronic payment service. Thereby, it becomes possible to quickly provide a detailed solution suitable for the specific questions of users, and for users to easily receive legal assistance.

[0290] The "communication device" is a general term for hardware or software for transmitting and receiving data, and has the role of connecting the user and the server.

[0291] The "legal question information" refers to text data, voice data, etc. input by the user to seek legal advice or information.

[0292] "Natural language processing technology" is a technology that enables machines to understand and analyze human language, and it is used to extract legal keywords.

[0293] "Legal keywords" are legal terms and phrases, and are an important element in understanding the intent behind a user's question.

[0294] "Data storage" refers to a digital medium or system for storing data, specifically information on laws and precedents.

[0295] "Relevant legal information" refers to data on laws, precedents, or guidelines that are relevant to the user's question.

[0296] Generative machine learning is a type of artificial intelligence technology that generates new data and answers through pattern learning.

[0297] "Solution" refers to a response that includes specific advice and instructions provided regarding a user's legal issues.

[0298] An "electronic payment service" is a system for conducting monetary transactions over the internet and has the function of processing payments from users.

[0299] This invention is a system that provides quick and accurate solutions to legal questions. The system mainly consists of three components: a server, a terminal, and a user.

[0300] User

[0301] Users launch a social networking app on their devices and enter legal questions. The entered questions are sent in text format. For example, a specific question might be, "What legal options are available if my rent renewal is refused?"

[0302] terminal

[0303] The terminal packages the text data input by the user and securely sends it to the server via the Internet. For this process, a secure communication protocol (e.g., HTTPS) for encrypting the data is used.

[0304] Server

[0305] The server decrypts the received data and analyzes the question using natural language processing technology. For the analysis, a generative AI model (e.g., GPT-4) is used to extract legal keywords and context from the text. Through this analysis, the intention of the user's question is grasped in detail.

[0306] The server then accesses a dedicated data storage and searches for legal information (laws, case precedents, guidelines) related to the extracted keywords. Based on the search results, the server applies generative machine learning to generate specific solutions to be provided to the user. This generated solution is not just general legal knowledge but is tailored to the user's question.

[0307] The generated solution is sent by the server back to the user's terminal via the SNS app again. The user can check the advice displayed on the terminal and make further inquiries to the server if there are additional questions.

[0308] Furthermore, the user can easily make a payment for the provided legal advice using an electronic payment function. This system enables the user to obtain legal advice quickly and significantly reduces the time and effort required to deal with legal issues.

[0309] As a specific example, an example of a prompt sentence is as follows. "Please provide legal advice regarding the refusal to renew the rent. Detailed guidance tailored to the customer's specific case is required." With this prompt, the generative AI model can provide accurate legal advice to the user.

[0310] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0311] Step 1:

[0312] User

[0313] The user launches a social networking app on their device to enter a legal question. The specific question entered is a legal issue, such as "What legal options are available if my rent renewal is refused?" The entered data is saved directly on the device. The input data consists of the user's legal questions and concerns in text format.

[0314] Step 2:

[0315] terminal

[0316] The terminal packages the text data entered by the user. This process includes encryption and data formatting. The packaged data is then sent to the server via the internet. The input is the user's question data, and the output is the encrypted packaged data.

[0317] Step 3:

[0318] server

[0319] The server receives packaged data sent from the terminal. First, the data is decrypted and returned to its original text format. Next, natural language processing is performed using a generative AI model to extract legal keywords and context. This allows for a detailed understanding of the user's question intent. The input is encrypted packaged data, and the output is legal keywords and contextual information.

[0320] Step 4:

[0321] server

[0322] The server uses the extracted legal keywords to search its internal database for relevant legal information. The database contains the latest laws, precedents, and guidelines. The search results are output as a set of information relevant to the user's question. The input is legal keywords, and the output is the relevant legal information.

[0323] Step 5:

[0324] server

[0325] The server uses a generative machine learning model to generate specific solutions based on the acquired legal information. A prompt such as "Please provide legal advice regarding the refusal to renew rent" is used as the generation prompt. This process generates solutions tailored to the user's specific problem. The input is relevant legal information, and the output is a specific legal solution.

[0326] Step 6:

[0327] server

[0328] The server repackages the generated solution and reliably sends it to the terminal. All data is re-encrypted before transmission. The input is the solution data, and the output is the encrypted transmission data.

[0329] Step 7:

[0330] User

[0331] The user receives the solution on their device and reviews its contents. If they have further questions, they can inquire again. This allows the user to receive ongoing legal support. The input is the received solution, and the output is the user's feedback or additional questions.

[0332] (Application Example 1)

[0333] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0334] There is a need for a system that can respond quickly and specifically to legal questions and problems. Existing systems have a cumbersome legal consultation process, and payment after the consultation is cumbersome. Furthermore, there is a need for a service that offers the flexibility to allow users to receive advice multiple times.

[0335] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0336] In this invention, the server includes means for receiving information via communication means, means for searching for relevant information using natural language processing, means for generating suggestions based on the relevant information using generative artificial intelligence, and means for selecting a payment method by selection means and completing the transaction. This allows users to smoothly conduct legal consultations and complete payments with a single tap. Furthermore, since advice can be received multiple times, continuous legal support is available.

[0337] "Communication means" refers to devices or technologies for sending and receiving information with other devices or systems.

[0338] "Natural language processing" is a technology that uses computers to analyze and understand human language.

[0339] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to generate new information and suggestions based on input data.

[0340] "Relevant information" refers to legal or other knowledge necessary for resolving the issue, derived from the information received.

[0341] A "proposal" is a solution or advice presented for a specific problem.

[0342] An "electronic payment service" is a technology or platform that enables online payments.

[0343] A "selection mechanism" is a system that allows users to choose their preferred option from multiple choices.

[0344] "Completing a transaction" means ending a series of procedures related to the purchase of goods or the provision of services.

[0345] The system for implementing this invention mainly consists of a server, a terminal, and a user. First, the user uses the terminal to input legal questions in text format from a specific social networking application. The terminal packages this information and sends it to the server via the internet.

[0346] The server analyzes the received information using natural language processing techniques. Libraries used for this analysis include, for example, SpaCy and NLTK. After analysis, it extracts relevant legal information and searches for necessary laws and precedents using a database. The server also uses generative artificial intelligence techniques to generate specific solutions tailored to the user's situation from the collected information. Here, OpenAI's generative models (e.g., GPT) are used.

[0347] The generated solutions are then sent back to the user's device via social media. The user can review the solutions displayed on their device and ask further questions if necessary. Furthermore, if payment is required, they can easily complete the transaction using their preferred electronic payment service from the options displayed on their device. Examples of payment services used include Apple Pay and Google Pay.

[0348] For example, if a user wants to know "how to deal with problems that arise regarding the renewal of a lease agreement," the server will provide a solution based on the latest relevant laws and precedents, and after the procedure is completed, the service fee can be paid immediately with Apple Pay.

[0349] Examples of prompts for a generative AI model include the following:

[0350] "Question from a user: What are some solutions for problems with lease renewals?"

[0351] The model generates the following: Please provide legal solutions related to this question.

[0352] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0353] Step 1:

[0354] Users launch a social networking app on their devices and enter legal questions in text format. In this scenario, user input consists of specific legal questions, which are then stored as digital data on the device.

[0355] Step 2:

[0356] The terminal sends the entered question as a data packet to the server over the internet. In this step, the entered text data is packetized and routed to the specified address on the server.

[0357] Step 3:

[0358] The server interprets the received data and extracts legal keywords and context using natural language processing techniques. The input is the user's raw text, and the output is the identified keywords and contextual information. The techniques used here include, for example, SpaCy and NLTK.

[0359] Step 4:

[0360] The server searches the database for relevant legal information based on keywords. This process involves querying the database and extracting relevant laws and precedents. The input is keywords, and the output is relevant legal information.

[0361] Step 5:

[0362] The server uses generative artificial intelligence technology to generate solutions from extracted legal information. Here, a generative AI model (e.g., GPT) generates advice specific to the question and provides it as output.

[0363] Step 6:

[0364] The server sends the generated solutions to the user's device via the SNS app. The input here is the text data of the generated solutions, which is converted into a format that can be displayed on the device.

[0365] Step 7:

[0366] Users can view solutions displayed on their device and enter additional questions as needed. They can also immediately pay any fees incurred for legal consultation using the displayed payment options. Input is the user's selection, and output is a confirmation message upon completion of the transaction. Electronic payment services (such as Apple Pay and Google Pay) are used in this process.

[0367] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0368] This invention provides a system that allows users to easily ask legal questions and seek advice, while also understanding the user's emotions and providing solutions tailored to those emotions. This system includes three components: a server, a terminal, and the user, and further incorporates an emotion engine.

[0369] Users launch a social networking app on their device and enter their legal questions in text format. The message is sent from the device to the server. Here, the text entered by the user may include not only legal information but also emotional nuances.

[0370] When the server receives a message, it first uses natural language processing (NLP) to analyze legal keywords. This helps it understand the user's intent and the content of their inquiry. Next, it uses an emotion engine to identify the user's emotional state from the message. This information is used in subsequent processes to generate solutions.

[0371] Based on keywords analyzed by the server, it accesses a database to search for relevant legal information and precedents. Based on these search results, the generating artificial intelligence generates solutions. Here, the user's emotional state, as recognized by the emotion engine, is taken into consideration. For example, if the user is feeling anxious, the server adjusts its solution to provide a greater sense of reassurance.

[0372] The generated solutions are formatted as text messages and sent back to the device. The user can review the advice displayed on the device and enter any further questions they may have.

[0373] Furthermore, if the user is satisfied with the advice provided, they can easily make a payment using their device. Once the transaction is completed using the mobile payment service, the server verifies the information and closes the transaction.

[0374] For example, if a user is struggling to reach an agreement with their spouse during divorce mediation, the emotion engine can be used to provide empathetic advice to alleviate the user's stress and anxiety. By providing emotionally sensitive legal support in this way, a more personalized experience can be offered to the user.

[0375] The following describes the processing flow.

[0376] Step 1:

[0377] Users launch a social networking app on their device, type a message requesting legal advice, and send it. The sent message may reflect not only legal questions but also the user's emotions.

[0378] Step 2:

[0379] The terminal sends messages from the user to the server. These messages reach the server via the communication network.

[0380] Step 3:

[0381] The server analyzes the received message, using natural language processing (NLP) techniques to extract legal keywords and context. This allows the server to understand the content of the consultation.

[0382] Step 4:

[0383] The server uses an emotion engine to analyze the user's emotions within a message. The emotion engine identifies emotional states such as joy, anxiety, and anger.

[0384] Step 5:

[0385] The server uses the extracted keywords to search the database for relevant legal information and case law. Based on these results, it prepares to create legal solutions.

[0386] Step 6:

[0387] The server utilizes a generative artificial intelligence model to generate solutions that take into account the user's emotions, as recognized by the emotion engine. This provides legal advice that is appropriate to the user's emotions.

[0388] Step 7:

[0389] The server formats the generated solution as a text message and sends it to the user's terminal to inform them of the solution.

[0390] Step 8:

[0391] The device receives messages from the server and displays them within the SNS app. Users can review the advice provided, enter new messages if they have further questions, and repeat the process from step 1.

[0392] Step 9:

[0393] If the user is satisfied with the advice, they will use their device to initiate the mobile payment service and process the payment according to the instructions. Once the payment is complete, the server will verify the information and complete the transaction.

[0394] (Example 2)

[0395] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0396] In modern society, there is a need for a system that can respond quickly and appropriately to legal questions and consultations from individuals. However, many current systems do not take into account the user's feelings and have difficulty providing personalized legal advice. Furthermore, generating legal solutions requires specialized knowledge, making it difficult for the average user to access. This invention aims to solve these problems and provide a user-friendly and accessible legal consultation system.

[0397] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0398] In this invention, the server includes means for receiving legal inquiry information via communication equipment, means for analyzing legal terminology using natural language processing, and means for performing sentiment analysis to identify emotional states. This enables users to receive personalized legal support that takes their emotional state into consideration quickly and efficiently.

[0399] "Communication equipment" refers to devices and infrastructure used for sending and receiving data.

[0400] "Legal inquiry information" refers to data that includes legal questions and consultations.

[0401] "Natural language processing" refers to the technology that enables computers to understand and analyze human language.

[0402] "Legal terminology" refers to specific keywords or phrases related to laws and precedents.

[0403] "Emotional analysis" refers to the process of identifying a user's emotional state from text data or audio data.

[0404] An "information aggregation structure" refers to a data structure that systematically organizes large amounts of data, enabling the efficient retrieval of necessary information.

[0405] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to generate new information and content based on given data and instructions.

[0406] A "solution" refers to information that describes specific measures and advice regarding the user's legal issues.

[0407] An "electronic payment system" refers to the technology and platform used to complete payments in a digital format.

[0408] This invention provides personalized solutions that take into account the user's emotions when they ask legal questions or seek advice. Specific embodiments are shown below.

[0409] Users launch communication software using their mobile devices or computers and input their legal inquiries in text format. This text is expected to include not only specific legal information but also context that reflects the user's emotions. For example, a sentence like, "I am anxious about divorce and child custody issues."

[0410] The terminal sends this entered text to the server via the internet. It is recommended to use a secure communication protocol for this transmission.

[0411] The server first processes the received text message using a natural language processing engine to analyze legal keywords. The analyzed data provides crucial information for understanding the user's inquiry.

[0412] Next, the server uses an emotion engine to identify the user's emotional state. This process allows it to determine whether the user is feeling anxious or stressed, or conversely, at ease.

[0413] Subsequently, the server uses the database system to search for relevant legal information and case precedents based on the analyzed keywords. By using query languages ​​such as SQL, data that matches the specified conditions can be retrieved quickly.

[0414] Based on the searched information, the server uses a generative AI model to generate legal solutions. During this process, the user's emotional state, as determined by sentiment analysis, is reflected, ensuring that advice is provided that takes the user's feelings into consideration.

[0415] The generated solution is sent back to the device, where the user can view it on the device's display. If the user wishes to add further questions, they can enter new text and send it again.

[0416] For example, if a user enters a question such as, "I want to know how legal advice can alleviate my anxiety," the system will identify the user's anxiety and return reassuring solutions to address it. An example of a prompt in this case would be, "Please provide advice for a user who is having difficulty reaching an agreement in divorce mediation. The user is feeling anxious."

[0417] This allows users to receive legal support that reflects their own feelings, enabling them to deal with legal issues with greater peace of mind.

[0418] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0419] Step 1:

[0420] The user enters legal questions into the terminal. The input is in text format and includes specific legal questions and consultations. An example of input might be, "What should I do if my rent payments are consistently behind?" The terminal temporarily stores the entered text data for use in subsequent processes.

[0421] Step 2:

[0422] The terminal sends the entered text data to the server. A secure communication protocol (e.g., HTTPS) is used for transmission to protect personal information. The transmitted data arrives at the server and is prepared for the next processing step.

[0423] Step 3:

[0424] The server passes the received text data to a natural language processing engine for analysis. The input here is text sent by the user, and the output extracts legal keywords and the user's intent. Specifically, morphological analysis and keyword extraction are performed.

[0425] Step 4:

[0426] The server uses an emotion analysis engine to identify the emotions contained in the message. The input is the user's text data, and the output is the user's emotional state (e.g., anxiety, relief, anger). This step involves emotion scoring and emotion classification of words.

[0427] Step 5:

[0428] The server uses the analyzed keywords to search the database for relevant legal information and case precedents. A database query is executed, with the analyzed keywords as input and the corresponding legal information as output. Specifically, information retrieval is performed using SQL.

[0429] Step 6:

[0430] The server generates legal solutions using a generative AI model based on search results and sentiment analysis data. The input is relevant legal information and user sentiment data, and the output is a solution tailored to the user's problem. In this process, the generative AI model constructs new solutions based on the data it has learned.

[0431] Step 7:

[0432] The server sends the generated solution to the terminal. The terminal receives it and displays it to the user in an appropriate format. A secure communication protocol is used again for transmission, and the solution is output as text. The user can review this and ask for further assistance if necessary.

[0433] Step 8:

[0434] If the user is satisfied with the advice provided, they will make a payment using the electronic payment system via the terminal. The input is the user's payment information, and the output is a notification that the payment has been completed. In this step, the transaction is processed through the payment gateway.

[0435] (Application Example 2)

[0436] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0437] A challenge exists in legal consultations where users are left feeling anxious and stressed because appropriate solutions are not provided that take their emotional state into consideration. Furthermore, the cumbersome payment process for related services after legal consultations can negatively impact the user experience. There is a need for a system that addresses these issues and provides emotionally sensitive legal consultations and smooth payment processes.

[0438] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0439] In this invention, the server includes means for receiving legal inquiry information and sentiment information via communication equipment; means for retrieving relevant legal information and sentiment information from a data store using natural language processing based on the legal inquiry information and sentiment information; means for generating solutions based on the relevant legal information and sentiment information using generative artificial intelligence; means for processing transactions through electronic transaction services; and means for providing a sentiment-sensitive user interface. This enables users to receive legal consultation in a sentiment-sensitive manner and to smoothly settle related services after the consultation.

[0440] "Communication equipment" refers to devices used by users to transmit legal inquiry information and emotional information.

[0441] "Legal inquiry information" refers to information that includes questions or consultations from users regarding legal matters.

[0442] "Emotional information" refers to information that indicates the user's emotional state, and is an important element when providing legal advice.

[0443] "Natural language processing" is a technology that enables computers to understand and process human language.

[0444] A "data store" is a recording medium that stores relevant legal and emotional information.

[0445] "Generative artificial intelligence" is a program that has the ability to generate new information and solutions based on data.

[0446] "Electronic transaction services" are services that allow for online transactions and payments.

[0447] "User interface" refers to the screens and means of operation that users use to interact with a system.

[0448] In order to implement this invention, it is necessary to effectively integrate communication equipment, servers, data stores, emotion engines, generative artificial intelligence, and electronic trading services.

[0449] System Configuration

[0450] Users use communication devices such as smartphones and personal computers to send legal questions and information along with emotional information. The device provides an intuitive user interface, designed to allow users to easily input information.

[0451] Processing flow

[0452] The server analyzes legal inquiry and sentiment information received from communication devices. Using Python's NLTK library and Transformers API, it performs natural language processing to identify legal keywords and intents. It also uses Microsoft's Azure Text Analytics and other tools to perform sentiment recognition and evaluate the user's emotional state. Based on this evaluation, it retrieves relevant legal information from a data store and generates solutions using generative artificial intelligence such as OpenAI's GPT-3. These generated solutions are then delivered to the user through a user interface.

[0453] Electronic payment

[0454] Furthermore, after providing solutions, the use of electronic transaction services such as Stripe and Square allows for smooth payment processing of related service fees and product purchases.

[0455] Specific example

[0456] For example, if a user requests "legal advice about purchasing an apartment" and emotional information detects anxiety, the system will generate detailed advice to alleviate concerns and present options such as hiring a lawyer or purchasing relevant books. Furthermore, payment services can be used to easily complete payments for related services.

[0457] As an example of a prompt, you can instruct the generating AI in the following format: "User question: What are the key points to check in a contract when purchasing an apartment? Emotional state: Anxious. Please generate careful and helpful advice."

[0458] In this way, a system is realized that provides legal consultations that take emotions into consideration and smooth settlements.

[0459] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0460] Step 1:

[0461] The terminal captures legal questions and emotions entered by the user. The input includes the user's text information and emotional nuances. After capturing this information, it is sent to the server via a communication device. The output is the legal question and emotional data, which is sent to the server.

[0462] Step 2:

[0463] The server analyzes received legal inquiry information and sentiment information. It takes text data and sentiment information as input, and uses Python's NLTK to extract keywords from the text. Next, it uses Microsoft Azure Text Analytics to analyze the sentiment and obtain the results. The output includes a list of analyzed keywords and the sentiment analysis results.

[0464] Step 3:

[0465] The server searches the data store for relevant legal information based on the analyzed keywords. It takes a list of keywords as input and executes SQL queries to retrieve the relevant legal information. The output is a set of relevant legal information.

[0466] Step 4:

[0467] The server generates solutions using a generative artificial intelligence model. It takes relevant legal information and sentiment analysis results as input, and sends prompts to the generative AI model (e.g., OpenAI's GPT-3) to generate solutions. The output is a specific, sentiment-sensitive solution.

[0468] Step 5:

[0469] The terminal displays the generated solutions to the user. The input is the solution text from the server, and the information is displayed to the user through a user interface in an easy-to-understand format. The output provides the user with the opportunity to review the solutions and choose further actions.

[0470] Step 6:

[0471] The user selects the necessary related services and makes payments through the electronic transaction service via the terminal. Inputs include the selected service information and payment information, and the transaction is completed using APIs such as Stripe or Square. Outputs include payment confirmation messages provided to both the user and the system.

[0472] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0473] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0474] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0475] [Third Embodiment]

[0476] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0477] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0478] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0479] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0480] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0481] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0482] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0483] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0484] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0485] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0486] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0487] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0488] This invention provides a system that allows users to easily consult about legal questions and problems. The system mainly consists of three components: a server, a terminal, and a user.

[0489] The user first launches a social networking app on their device, enters their legal question in text format, and sends it. This message is then packaged by the device and sent to the server via the internet.

[0490] The server analyzes received messages using natural language processing (NLP) techniques, extracting relevant legal keywords and context from their content. This allows for a detailed understanding of the user's intent. Next, the server accesses a database to search for information such as laws, precedents, and guidelines related to the extracted keywords.

[0491] Based on the information obtained through the search, the server utilizes artificial intelligence to generate solutions tailored to the user's situation. These generated solutions are not mere generalities, but specific advice tailored to the user's inquiry.

[0492] The generated solutions are then sent back to the device via SNS by the server. Users can review the advice displayed on their device, ask further questions if necessary, and receive advice repeatedly.

[0493] Furthermore, the payment process has been simplified, allowing users to easily pay for the legal advice provided using mobile payment services. This enables users to receive legal assistance quickly and easily.

[0494] For example, if a user wants to know "how to deal with problems that arise regarding the renewal of a lease agreement," the system will provide appropriate solutions based on the latest relevant laws and precedents through the server. This allows users to quickly address problems, saving them time and effort.

[0495] The following describes the processing flow.

[0496] Step 1:

[0497] The user opens a social networking app on their device, types a text message requesting legal advice, and sends it. The device then sends this message to the server.

[0498] Step 2:

[0499] The server receives messages via the SNS protocol. The received messages are then input into the natural language processing (NLP) module.

[0500] Step 3:

[0501] The server uses an NLP module to analyze the message, extracting legally relevant keywords and context.

[0502] Step 4:

[0503] The server searches the database based on the extracted keywords and retrieves relevant legal documents, precedents, and guidelines.

[0504] Step 5:

[0505] The server uses artificial intelligence based on acquired legal information to generate solutions related to the user's questions.

[0506] Step 6:

[0507] The server formats the generated solution as a text message and sends it to the terminal according to the SNS protocol.

[0508] Step 7:

[0509] The device receives messages from the server and displays them within the SNS app. Users can review the provided solutions, enter any further questions, and repeat the process from step 1.

[0510] Step 8:

[0511] If the user is satisfied with the consultation, they will use their device to activate the mobile payment service and follow the instructions to complete the payment. The server will then verify the payment information and complete the transaction.

[0512] (Example 1)

[0513] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0514] There is a need for a system that can respond quickly and accurately to legal inquiries. However, existing legal inquiry response systems have struggled to provide detailed solutions tailored to users' specific problems, and the complex payment procedures involved have sometimes compromised user convenience. Therefore, a system is needed that accurately understands user needs, provides specific solutions quickly based on those needs, and has a simple payment process.

[0515] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0516] In this invention, the server includes means for receiving legal question information via a communication device, means for analyzing the legal question information using natural language processing technology and extracting legal keywords, means for searching for relevant legal information from data storage based on the legal keywords, means for generating solutions based on the relevant legal information using generative machine learning, means for transmitting the solutions via the communication device, and means for processing transactions using an electronic payment service. This enables the rapid provision of detailed solutions tailored to the user's specific questions, making it easy for users to receive legal assistance.

[0517] "Communication equipment" is a general term for hardware or software used to send and receive data, and it plays a role in connecting users and servers.

[0518] "Legal inquiry information" refers to text data, audio data, and other information entered by users seeking legal advice or information.

[0519] "Natural language processing technology" is a technology that enables machines to understand and analyze human language, and it is used to extract legal keywords.

[0520] "Legal keywords" are legal terms and phrases, and are an important element in understanding the intent behind a user's question.

[0521] "Data storage" refers to a digital medium or system for storing data, specifically information on laws and precedents.

[0522] "Relevant legal information" refers to data on laws, precedents, or guidelines that are relevant to the user's question.

[0523] Generative machine learning is a type of artificial intelligence technology that generates new data and answers through pattern learning.

[0524] "Solution" refers to a response that includes specific advice and instructions provided regarding a user's legal issues.

[0525] An "electronic payment service" is a system for conducting monetary transactions over the internet and has the function of processing payments from users.

[0526] This invention is a system that provides quick and accurate solutions to legal questions. The system mainly consists of three components: a server, a terminal, and a user.

[0527] User

[0528] Users launch a social networking app on their devices and enter legal questions. The entered questions are sent in text format. For example, a specific question might be, "What legal options are available if my rent renewal is refused?"

[0529] terminal

[0530] The terminal packages the text data entered by the user and securely transmits it to the server over the internet. This process uses a secure communication protocol (e.g., HTTPS) to encrypt the data.

[0531] server

[0532] The server decodes the received data and analyzes the question using natural language processing techniques. A generative AI model (e.g., GPT-4) is used for the analysis, extracting legal keywords and context from the text. This analysis allows for a detailed understanding of the user's intent behind the question.

[0533] The server then accesses dedicated data storage and searches for legal information (laws, precedents, guidelines) related to the extracted keywords. Based on the search results, the server applies generative machine learning to generate specific solutions to provide to the user. These generated solutions are not merely general legal knowledge, but are tailored to the user's question.

[0534] The generated solution is then sent back to the user's device via the SNS app by the server. The user can review the advice displayed on their device and contact the server again if they have any further questions.

[0535] Furthermore, users can easily pay for the legal advice provided using the electronic payment function. This system enables users to obtain legal advice quickly, significantly reducing the time and effort required to deal with legal issues.

[0536] As a concrete example, an example of a prompt is as follows: "Please provide legal advice regarding the refusal to renew rent. I need a detailed guide tailored to my specific case." This prompt enables the generated AI model to provide the user with accurate legal advice.

[0537] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0538] Step 1:

[0539] User

[0540] The user launches a social networking app on their device to enter a legal question. The specific question entered is a legal issue, such as "What legal options are available if my rent renewal is refused?" The entered data is saved directly on the device. The input data consists of the user's legal questions and concerns in text format.

[0541] Step 2:

[0542] terminal

[0543] The terminal packages the text data entered by the user. This process includes encryption and data formatting. The packaged data is then sent to the server via the internet. The input is the user's question data, and the output is the encrypted packaged data.

[0544] Step 3:

[0545] server

[0546] The server receives packaged data sent from the terminal. First, the data is decrypted and returned to its original text format. Next, natural language processing is performed using a generative AI model to extract legal keywords and context. This allows for a detailed understanding of the user's question intent. The input is encrypted packaged data, and the output is legal keywords and contextual information.

[0547] Step 4:

[0548] server

[0549] The server uses the extracted legal keywords to search its internal database for relevant legal information. The database contains the latest laws, precedents, and guidelines. The search results are output as a set of information relevant to the user's question. The input is legal keywords, and the output is the relevant legal information.

[0550] Step 5:

[0551] server

[0552] The server uses a generative machine learning model to generate specific solutions based on the acquired legal information. A prompt such as "Please provide legal advice regarding the refusal to renew rent" is used as the generation prompt. This process generates solutions tailored to the user's specific problem. The input is relevant legal information, and the output is a specific legal solution.

[0553] Step 6:

[0554] server

[0555] The server repackages the generated solution and reliably sends it to the terminal. All data is re-encrypted before transmission. The input is the solution data, and the output is the encrypted transmission data.

[0556] Step 7:

[0557] User

[0558] The user receives the solution on their device and reviews its contents. If they have further questions, they can inquire again. This allows the user to receive ongoing legal support. The input is the received solution, and the output is the user's feedback or additional questions.

[0559] (Application Example 1)

[0560] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0561] There is a need for a system that can respond quickly and specifically to legal questions and problems. Existing systems have a cumbersome legal consultation process, and payment after the consultation is cumbersome. Furthermore, there is a need for a service that offers the flexibility to allow users to receive advice multiple times.

[0562] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0563] In this invention, the server includes means for receiving information via communication means, means for searching for relevant information using natural language processing, means for generating suggestions based on the relevant information using generative artificial intelligence, and means for selecting a payment method by selection means and completing the transaction. This allows users to smoothly conduct legal consultations and complete payments with a single tap. Furthermore, since advice can be received multiple times, continuous legal support is available.

[0564] "Communication means" refers to devices or technologies for sending and receiving information with other devices or systems.

[0565] "Natural language processing" is a technology that uses computers to analyze and understand human language.

[0566] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to generate new information and suggestions based on input data.

[0567] "Relevant information" refers to legal or other knowledge necessary for resolving the issue, derived from the information received.

[0568] A "proposal" is a solution or advice presented for a specific problem.

[0569] An "electronic payment service" is a technology or platform that enables online payments.

[0570] A "selection mechanism" is a system that allows users to choose their preferred option from multiple choices.

[0571] "Completing a transaction" means ending a series of procedures related to the purchase of goods or the provision of services.

[0572] The system for implementing this invention mainly consists of a server, a terminal, and a user. First, the user uses the terminal to input legal questions in text format from a specific social networking application. The terminal packages this information and sends it to the server via the internet.

[0573] The server analyzes the received information using natural language processing techniques. Libraries used for this analysis include, for example, SpaCy and NLTK. After analysis, it extracts relevant legal information and searches for necessary laws and precedents using a database. The server also uses generative artificial intelligence techniques to generate specific solutions tailored to the user's situation from the collected information. Here, OpenAI's generative models (e.g., GPT) are used.

[0574] The generated solutions are then sent back to the user's device via social media. The user can review the solutions displayed on their device and ask further questions if necessary. Furthermore, if payment is required, they can easily complete the transaction using their preferred electronic payment service from the options displayed on their device. Examples of payment services used include Apple Pay and Google Pay.

[0575] For example, if a user wants to know "how to deal with problems that arise regarding the renewal of a lease agreement," the server will provide a solution based on the latest relevant laws and precedents, and after the procedure is completed, the service fee can be paid immediately with Apple Pay.

[0576] Examples of prompts for a generative AI model include the following:

[0577] "Question from a user: What are some solutions for problems with lease renewals?"

[0578] The model generates the following: Please provide legal solutions related to this question.

[0579] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0580] Step 1:

[0581] Users launch a social networking app on their devices and enter legal questions in text format. In this scenario, user input consists of specific legal questions, which are then stored as digital data on the device.

[0582] Step 2:

[0583] The terminal sends the entered question as a data packet to the server over the internet. In this step, the entered text data is packetized and routed to the specified address on the server.

[0584] Step 3:

[0585] The server interprets the received data and extracts legal keywords and context using natural language processing techniques. The input is the user's raw text, and the output is the identified keywords and contextual information. The techniques used here include, for example, SpaCy and NLTK.

[0586] Step 4:

[0587] The server searches the database for relevant legal information based on keywords. This process involves querying the database and extracting relevant laws and precedents. The input is keywords, and the output is the relevant legal information.

[0588] Step 5:

[0589] The server uses generative artificial intelligence technology to generate solutions from extracted legal information. Here, a generative AI model (e.g., GPT) generates advice specific to the question and provides it as output.

[0590] Step 6:

[0591] The server sends the generated solutions to the user's device via the SNS app. The input here is the text data of the generated solutions, which is converted into a format that can be displayed on the device.

[0592] Step 7:

[0593] Users can view solutions displayed on their device and enter additional questions as needed. They can also immediately pay any fees incurred for legal consultation using the displayed payment options. Input is the user's selection, and output is a confirmation message upon completion of the transaction. Electronic payment services (such as Apple Pay and Google Pay) are used in this process.

[0594] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0595] This invention provides a system that allows users to easily ask legal questions and seek advice, while also understanding the user's emotions and providing solutions tailored to those emotions. This system includes three components: a server, a terminal, and the user, and further incorporates an emotion engine.

[0596] Users launch a social networking app on their device and enter their legal questions in text format. The message is sent from the device to the server. Here, the text entered by the user may include not only legal information but also emotional nuances.

[0597] When the server receives a message, it first uses natural language processing (NLP) to analyze legal keywords. This helps it understand the user's intent and the content of their inquiry. Next, it uses an emotion engine to identify the user's emotional state from the message. This information is used in subsequent processes to generate solutions.

[0598] Based on keywords analyzed by the server, it accesses a database to search for relevant legal information and precedents. Based on these search results, the generating artificial intelligence generates solutions. Here, the user's emotional state, as recognized by the emotion engine, is taken into consideration. For example, if the user is feeling anxious, the server adjusts its solution to provide a greater sense of reassurance.

[0599] The generated solutions are formatted as text messages and sent back to the device. The user can review the advice displayed on the device and enter any further questions they may have.

[0600] Furthermore, if the user is satisfied with the advice provided, they can easily make a payment using their device. Once the transaction is completed using the mobile payment service, the server verifies the information and terminates the transaction.

[0601] For example, if a user is struggling to reach an agreement with their spouse during divorce mediation, the emotion engine can be used to provide empathetic advice to alleviate the user's stress and anxiety. By providing emotionally sensitive legal support in this way, a more personalized experience can be offered to the user.

[0602] The following describes the processing flow.

[0603] Step 1:

[0604] Users launch a social networking app on their device, type a message requesting legal advice, and send it. The sent message may reflect not only legal questions but also the user's emotions.

[0605] Step 2:

[0606] The terminal sends messages from the user to the server. These messages reach the server via the communication network.

[0607] Step 3:

[0608] The server analyzes the received message, using natural language processing (NLP) techniques to extract legal keywords and context. This allows the server to understand the content of the consultation.

[0609] Step 4:

[0610] The server uses an emotion engine to analyze the user's emotions within a message. The emotion engine identifies emotional states such as joy, anxiety, and anger.

[0611] Step 5:

[0612] The server uses the extracted keywords to search the database for relevant legal information and case law. Based on these results, it prepares to create legal solutions.

[0613] Step 6:

[0614] The server utilizes a generative artificial intelligence model to generate solutions that take into account the user's emotions, as recognized by the emotion engine. This provides legal advice that is appropriate to the user's emotions.

[0615] Step 7:

[0616] The server formats the generated solution as a text message and sends it to the user's terminal to inform them of the solution.

[0617] Step 8:

[0618] The device receives messages from the server and displays them within the SNS app. Users can review the provided advice, enter new messages if they have further questions, and repeat the process from step 1.

[0619] Step 9:

[0620] If the user is satisfied with the advice, they will use their device to initiate the mobile payment service and follow the instructions to complete the payment. Once the payment is complete, the server will verify the information and complete the transaction.

[0621] (Example 2)

[0622] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0623] In modern society, there is a need for a system that can respond quickly and appropriately to legal questions and consultations from individuals. However, many current systems do not take into account the user's feelings and have difficulty providing personalized legal advice. Furthermore, generating legal solutions requires specialized knowledge, making it difficult for the average user to access. This invention aims to solve these problems and provide a user-friendly and accessible legal consultation system.

[0624] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0625] In this invention, the server includes means for receiving legal inquiry information via communication equipment, means for analyzing legal terminology using natural language processing, and means for performing sentiment analysis to identify emotional states. This enables users to receive personalized legal support that takes their emotional state into consideration quickly and efficiently.

[0626] "Communication equipment" refers to devices and infrastructure used for sending and receiving data.

[0627] "Legal inquiry information" refers to data that includes legal questions and consultations.

[0628] "Natural language processing" refers to the technology that enables computers to understand and analyze human language.

[0629] "Legal terminology" refers to specific keywords or phrases related to laws and precedents.

[0630] "Emotional analysis" refers to the process of identifying a user's emotional state from text data or audio data.

[0631] An "information aggregation structure" refers to a data structure that systematically organizes large amounts of data, enabling the efficient retrieval of necessary information.

[0632] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to generate new information and content based on given data and instructions.

[0633] A "solution" refers to information that describes specific measures and advice regarding the user's legal issues.

[0634] An "electronic payment system" refers to the technology and platform used to complete payments in a digital format.

[0635] This invention provides personalized solutions that take into account the user's emotions when they ask legal questions or seek advice. Specific embodiments are shown below.

[0636] Users launch communication software using their mobile devices or computers and input their legal inquiries in text format. This text is expected to include not only specific legal information but also context that reflects the user's emotions. For example, a sentence like, "I am anxious about divorce and child custody issues."

[0637] The terminal sends this entered text to the server via the internet. It is recommended to use a secure communication protocol for this transmission.

[0638] The server first processes the received text message using a natural language processing engine to analyze legal keywords. The analyzed data provides crucial information for understanding the user's inquiry.

[0639] Next, the server uses an emotion engine to identify the user's emotional state. This process allows it to determine whether the user is feeling anxious or stressed, or conversely, at ease.

[0640] Subsequently, the server uses the database system to search for relevant legal information and case precedents based on the analyzed keywords. By using query languages ​​such as SQL, data that matches the specified conditions can be retrieved quickly.

[0641] Based on the searched information, the server uses a generative AI model to generate legal solutions. During this process, the user's emotional state, as determined by sentiment analysis, is reflected, ensuring that advice is provided that takes the user's feelings into consideration.

[0642] The generated solution is sent back to the device, where the user can view it on the device's display. If the user wishes to add further questions, they can enter new text and send it again.

[0643] For example, if a user enters a question such as, "I want to know how legal advice can alleviate my anxiety," the system will identify the user's anxiety and return reassuring solutions to address it. An example of a prompt in this case would be, "Please provide advice for a user who is having difficulty reaching an agreement in divorce mediation. The user is feeling anxious."

[0644] This allows users to receive legal support that reflects their own feelings, enabling them to deal with legal issues with greater peace of mind.

[0645] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0646] Step 1:

[0647] The user enters legal questions into the terminal. The input is in text format and includes specific legal questions and consultations. An example of input might be, "What should I do if my rent payments are consistently behind?" The terminal temporarily stores the entered text data for use in subsequent processes.

[0648] Step 2:

[0649] The terminal sends the entered text data to the server. A secure communication protocol (e.g., HTTPS) is used for transmission to protect personal information. The transmitted data arrives at the server and is prepared for the next processing step.

[0650] Step 3:

[0651] The server passes the received text data to a natural language processing engine for analysis. The input here is text sent by the user, and the output extracts legal keywords and the user's intent. Specifically, morphological analysis and keyword extraction are performed.

[0652] Step 4:

[0653] The server uses an emotion analysis engine to identify the emotions contained in the message. The input is the user's text data, and the output is the user's emotional state (e.g., anxiety, relief, anger). This step involves emotion scoring and emotion classification of words.

[0654] Step 5:

[0655] The server uses the analyzed keywords to search the database for relevant legal information and case precedents. A database query is executed, with the analyzed keywords as input and the corresponding legal information as output. Specifically, information retrieval is performed using SQL.

[0656] Step 6:

[0657] The server generates legal solutions using a generative AI model based on search results and sentiment analysis data. The input is relevant legal information and user sentiment data, and the output is a solution tailored to the user's problem. In this process, the generative AI model constructs new solutions based on the data it has learned.

[0658] Step 7:

[0659] The server sends the generated solution to the terminal. The terminal receives it and displays it to the user in an appropriate format. A secure communication protocol is used again for transmission, and the solution is output as text. The user can review this and ask for further assistance if necessary.

[0660] Step 8:

[0661] If the user is satisfied with the advice provided, they will make a payment using the electronic payment system via the terminal. The input is the user's payment information, and the output is a notification that the payment has been completed. In this step, the transaction is processed through the payment gateway.

[0662] (Application Example 2)

[0663] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0664] A challenge exists in legal consultations where users are left feeling anxious and stressed because appropriate solutions are not provided that take their emotional state into consideration. Furthermore, the cumbersome payment process for related services after legal consultations can negatively impact the user experience. There is a need for a system that addresses these issues and provides emotionally sensitive legal consultations and smooth payment processes.

[0665] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0666] In this invention, the server includes means for receiving legal inquiry information and sentiment information via communication equipment; means for retrieving relevant legal information and sentiment information from a data store using natural language processing based on the legal inquiry information and sentiment information; means for generating solutions based on the relevant legal information and sentiment information using generative artificial intelligence; means for processing transactions through electronic transaction services; and means for providing a sentiment-sensitive user interface. This enables users to receive legal consultation in a sentiment-sensitive manner and to smoothly settle related services after the consultation.

[0667] "Communication equipment" refers to devices used by users to transmit legal inquiry information and emotional information.

[0668] "Legal inquiry information" refers to information that includes questions or consultations from users regarding legal matters.

[0669] "Emotional information" refers to information that indicates the user's emotional state, and is an important element when providing legal advice.

[0670] "Natural language processing" is a technology that enables computers to understand and process human language.

[0671] A "data store" is a recording medium that stores relevant legal and emotional information.

[0672] "Generative artificial intelligence" is a program that has the ability to generate new information and solutions based on data.

[0673] "Electronic transaction services" are services that allow for online transactions and payments.

[0674] "User interface" refers to the screens and means of operation that users use to interact with a system.

[0675] In order to implement this invention, it is necessary to effectively integrate communication equipment, servers, data stores, emotion engines, generative artificial intelligence, and electronic trading services.

[0676] System Configuration

[0677] Users use communication devices such as smartphones and personal computers to send legal questions and information along with emotional information. The device provides an intuitive user interface, designed to allow users to easily input information.

[0678] Processing flow

[0679] The server analyzes legal inquiry and sentiment information received from communication devices. Using Python's NLTK library and Transformers API, it performs natural language processing to identify legal keywords and intents. It also uses Microsoft's Azure Text Analytics and other tools to perform sentiment recognition and evaluate the user's emotional state. Based on this evaluation, it retrieves relevant legal information from a data store and generates solutions using generative artificial intelligence such as OpenAI's GPT-3. These generated solutions are then delivered to the user through a user interface.

[0680] Electronic payment

[0681] Furthermore, after providing solutions, the use of electronic transaction services such as Stripe and Square allows for smooth payment processing of related service fees and product purchases.

[0682] Specific example

[0683] For example, if a user requests "legal advice about purchasing an apartment" and emotional information detects anxiety, the system will generate detailed advice to alleviate concerns and present options such as hiring a lawyer or purchasing relevant books. Furthermore, payment services can be used to easily complete payments for related services.

[0684] As an example of a prompt, you can instruct the generating AI in the following format: "User question: What are the key points to check in a contract when purchasing an apartment? Emotional state: Anxious. Please generate careful and helpful advice."

[0685] In this way, a system is realized that provides legal consultations that take emotions into consideration and smooth settlements.

[0686] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0687] Step 1:

[0688] The terminal captures legal questions and emotions entered by the user. The input includes the user's text information and emotional nuances. After capturing this information, it is sent to the server via a communication device. The output is the legal question and emotional data, which is sent to the server.

[0689] Step 2:

[0690] The server analyzes received legal inquiry information and sentiment information. It takes text data and sentiment information as input, and uses Python's NLTK to extract keywords from the text. Next, it uses Microsoft Azure Text Analytics to analyze the sentiment and obtain the results. The output includes a list of analyzed keywords and the sentiment analysis results.

[0691] Step 3:

[0692] The server searches the data store for relevant legal information based on the analyzed keywords. It takes a list of keywords as input and executes SQL queries to retrieve the relevant legal information. The output is a set of relevant legal information.

[0693] Step 4:

[0694] The server generates solutions using a generative artificial intelligence model. It takes relevant legal information and sentiment analysis results as input, and sends prompts to the generative AI model (e.g., OpenAI's GPT-3) to generate solutions. The output is a specific, sentiment-sensitive solution.

[0695] Step 5:

[0696] The terminal displays the generated solutions to the user. The input is the solution text from the server, and the information is displayed to the user through a user interface in an easy-to-understand format. The output provides the user with the opportunity to review the solutions and choose further actions.

[0697] Step 6:

[0698] The user selects the necessary related services and makes payments through the electronic transaction service via the terminal. Inputs include the selected service information and payment information, and the transaction is completed using APIs such as Stripe or Square. Outputs include payment confirmation messages provided to both the user and the system.

[0699] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0700] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0701] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0702] [Fourth Embodiment]

[0703] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0704] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0705] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0706] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0707] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0708] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0709] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0710] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0711] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0712] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0713] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0714] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0715] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0716] This invention provides a system that allows users to easily consult about legal questions and problems. The system mainly consists of three components: a server, a terminal, and a user.

[0717] The user first launches a social networking app on their device, enters their legal question in text format, and sends it. This message is then packaged by the device and sent to the server via the internet.

[0718] The server analyzes received messages using natural language processing (NLP) techniques, extracting relevant legal keywords and context from their content. This allows for a detailed understanding of the user's intent. Next, the server accesses a database to search for information such as laws, precedents, and guidelines related to the extracted keywords.

[0719] Based on the information obtained through the search, the server utilizes artificial intelligence to generate solutions tailored to the user's situation. These generated solutions are not mere generalities, but specific advice tailored to the user's inquiry.

[0720] The generated solutions are then sent back to the device via SNS by the server. Users can review the advice displayed on their device, ask further questions if necessary, and receive advice repeatedly.

[0721] Furthermore, the payment process has been simplified, allowing users to easily pay for the legal advice provided using mobile payment services. This enables users to receive legal assistance quickly and easily.

[0722] For example, if a user wants to know "how to deal with problems that arise regarding the renewal of a lease agreement," the system will provide appropriate solutions based on the latest relevant laws and precedents through the server. This allows users to quickly address problems, saving them time and effort.

[0723] The following describes the processing flow.

[0724] Step 1:

[0725] The user opens a social networking app on their device, types a text message requesting legal advice, and sends it. The device then sends this message to the server.

[0726] Step 2:

[0727] The server receives messages via the SNS protocol. The received messages are then input into the natural language processing (NLP) module.

[0728] Step 3:

[0729] The server uses an NLP module to analyze the message, extracting legally relevant keywords and context.

[0730] Step 4:

[0731] The server searches the database based on the extracted keywords and retrieves relevant legal documents, precedents, and guidelines.

[0732] Step 5:

[0733] The server uses artificial intelligence based on acquired legal information to generate solutions related to the user's questions.

[0734] Step 6:

[0735] The server formats the generated solution as a text message and sends it to the terminal according to the SNS protocol.

[0736] Step 7:

[0737] The device receives messages from the server and displays them within the SNS app. Users can review the provided solutions, enter any further questions, and repeat the process from step 1.

[0738] Step 8:

[0739] If the user is satisfied with the consultation, they will use their device to activate the mobile payment service and follow the instructions to complete the payment. The server will then verify the payment information and complete the transaction.

[0740] (Example 1)

[0741] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0742] There is a need for a system that can respond quickly and accurately to legal inquiries. However, existing legal inquiry response systems have struggled to provide detailed solutions tailored to users' specific problems, and the complex payment procedures involved have sometimes compromised user convenience. Therefore, a system is needed that accurately understands user needs, provides specific solutions quickly based on those needs, and has a simple payment process.

[0743] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0744] In this invention, the server includes means for receiving legal question information via a communication device, means for analyzing the legal question information using natural language processing technology and extracting legal keywords, means for searching for relevant legal information from data storage based on the legal keywords, means for generating solutions based on the relevant legal information using generative machine learning, means for transmitting the solutions via the communication device, and means for processing transactions using an electronic payment service. This enables the rapid provision of detailed solutions tailored to the user's specific questions, making it easy for users to receive legal assistance.

[0745] "Communication equipment" is a general term for hardware or software used to send and receive data, and it plays a role in connecting users and servers.

[0746] "Legal inquiry information" refers to text data, audio data, and other information entered by users seeking legal advice or information.

[0747] "Natural language processing technology" is a technology that enables machines to understand and analyze human language, and it is used to extract legal keywords.

[0748] "Legal keywords" are legal terms and phrases, and are an important element in understanding the intent behind a user's question.

[0749] "Data storage" refers to a digital medium or system for storing data, specifically information on laws and precedents.

[0750] "Relevant legal information" refers to data on laws, precedents, or guidelines that are relevant to the user's question.

[0751] Generative machine learning is a type of artificial intelligence technology that generates new data and answers through pattern learning.

[0752] "Solution" refers to a response that includes specific advice and instructions provided regarding a user's legal issues.

[0753] An "electronic payment service" is a system for conducting monetary transactions over the internet and has the function of processing payments from users.

[0754] This invention is a system that provides quick and accurate solutions to legal questions. The system mainly consists of three components: a server, a terminal, and a user.

[0755] User

[0756] Users launch a social networking app on their devices and enter legal questions. The entered questions are sent in text format. For example, a specific question might be, "What legal options are available if my rent renewal is refused?"

[0757] terminal

[0758] The terminal packages the text data entered by the user and securely transmits it to the server over the internet. This process uses a secure communication protocol (e.g., HTTPS) to encrypt the data.

[0759] server

[0760] The server decodes the received data and analyzes the question using natural language processing techniques. A generative AI model (e.g., GPT-4) is used for the analysis, extracting legal keywords and context from the text. This analysis allows for a detailed understanding of the user's intent behind the question.

[0761] The server then accesses dedicated data storage and searches for legal information (laws, precedents, guidelines) related to the extracted keywords. Based on the search results, the server applies generative machine learning to generate specific solutions to provide to the user. These generated solutions are not merely general legal knowledge, but are tailored to the user's question.

[0762] The generated solution is then sent back to the user's device via the SNS app by the server. The user can review the advice displayed on their device and contact the server again if they have any further questions.

[0763] Furthermore, users can easily pay for the legal advice provided using the electronic payment function. This system enables users to obtain legal advice quickly, significantly reducing the time and effort required to deal with legal issues.

[0764] As a concrete example, an example of a prompt is as follows: "Please provide legal advice regarding the refusal to renew rent. I need a detailed guide tailored to my specific case." This prompt enables the generated AI model to provide the user with accurate legal advice.

[0765] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0766] Step 1:

[0767] User

[0768] The user launches a social networking app on their device to enter a legal question. The specific question entered is a legal issue, such as "What legal options are available if my rent renewal is refused?" The entered data is saved directly on the device. The input data consists of the user's legal questions and concerns in text format.

[0769] Step 2:

[0770] terminal

[0771] The terminal packages the text data entered by the user. This process includes encryption and data formatting. The packaged data is then sent to the server via the internet. The input is the user's question data, and the output is the encrypted packaged data.

[0772] Step 3:

[0773] server

[0774] The server receives packaged data sent from the terminal. First, the data is decrypted and returned to its original text format. Next, natural language processing is performed using a generative AI model to extract legal keywords and context. This allows for a detailed understanding of the user's question intent. The input is encrypted packaged data, and the output is legal keywords and contextual information.

[0775] Step 4:

[0776] server

[0777] The server uses the extracted legal keywords to search its internal database for relevant legal information. The database contains the latest laws, precedents, and guidelines. The search results are output as a set of information relevant to the user's question. The input is legal keywords, and the output is the relevant legal information.

[0778] Step 5:

[0779] server

[0780] The server uses a generative machine learning model to generate specific solutions based on the acquired legal information. A prompt such as "Please provide legal advice regarding the refusal to renew rent" is used as the generation prompt. This process generates solutions tailored to the user's specific problem. The input is relevant legal information, and the output is a specific legal solution.

[0781] Step 6:

[0782] server

[0783] The server repackages the generated solution and reliably sends it to the terminal. All data is re-encrypted before transmission. The input is the solution data, and the output is the encrypted transmission data.

[0784] Step 7:

[0785] User

[0786] The user receives the solution on their device and reviews its contents. If they have further questions, they can inquire again. This allows the user to receive ongoing legal support. The input is the received solution, and the output is the user's feedback or additional questions.

[0787] (Application Example 1)

[0788] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0789] There is a need for a system that can respond quickly and specifically to legal questions and problems. Existing systems have a cumbersome legal consultation process, and payment after the consultation is cumbersome. Furthermore, there is a need for a service that offers the flexibility to allow users to receive advice multiple times.

[0790] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0791] In this invention, the server includes means for receiving information via communication means, means for searching for relevant information using natural language processing, means for generating suggestions based on the relevant information using generative artificial intelligence, and means for selecting a payment method by selection means and completing the transaction. This allows users to smoothly conduct legal consultations and complete payments with a single tap. Furthermore, since advice can be received multiple times, continuous legal support is available.

[0792] "Communication means" refers to devices or technologies for sending and receiving information with other devices or systems.

[0793] "Natural language processing" is a technology that uses computers to analyze and understand human language.

[0794] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to generate new information and suggestions based on input data.

[0795] "Relevant information" refers to legal or other knowledge necessary for resolving the issue, derived from the information received.

[0796] A "proposal" is a solution or advice presented for a specific problem.

[0797] An "electronic payment service" is a technology or platform that enables online payments.

[0798] A "selection mechanism" is a system that allows users to choose their preferred option from multiple choices.

[0799] "Completing a transaction" means ending a series of procedures related to the purchase of goods or the provision of services.

[0800] The system for implementing this invention mainly consists of a server, a terminal, and a user. First, the user uses the terminal to input legal questions in text format from a specific social networking application. The terminal packages this information and sends it to the server via the internet.

[0801] The server analyzes the received information using natural language processing techniques. Libraries used for this analysis include, for example, SpaCy and NLTK. After analysis, it extracts relevant legal information and searches for necessary laws and precedents using a database. The server also uses generative artificial intelligence techniques to generate specific solutions tailored to the user's situation from the collected information. Here, OpenAI's generative models (e.g., GPT) are used.

[0802] The generated solutions are then sent back to the user's device via social media. The user can review the solutions displayed on their device and ask further questions if necessary. Furthermore, if payment is required, they can easily complete the transaction using their preferred electronic payment service from the options displayed on their device. Examples of payment services used include Apple Pay and Google Pay.

[0803] For example, if a user wants to know "how to deal with problems that arise regarding the renewal of a lease agreement," the server will provide a solution based on the latest relevant laws and precedents, and after the procedure is completed, the service fee can be paid immediately with Apple Pay.

[0804] Examples of prompts for a generative AI model include the following:

[0805] "Question from a user: What are some solutions for problems with lease renewals?"

[0806] The model generates the following: Please provide legal solutions related to this question.

[0807] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0808] Step 1:

[0809] Users launch a social networking app on their devices and enter legal questions in text format. In this scenario, user input consists of specific legal questions, which are then stored as digital data on the device.

[0810] Step 2:

[0811] The terminal sends the entered question as a data packet to the server over the internet. In this step, the entered text data is packetized and routed to the specified address on the server.

[0812] Step 3:

[0813] The server interprets the received data and extracts legal keywords and context using natural language processing techniques. The input is the user's raw text, and the output is the identified keywords and contextual information. The techniques used here include, for example, SpaCy and NLTK.

[0814] Step 4:

[0815] The server searches the database for relevant legal information based on keywords. This process involves querying the database and extracting relevant laws and precedents. The input is keywords, and the output is the relevant legal information.

[0816] Step 5:

[0817] The server uses generative artificial intelligence technology to generate solutions from extracted legal information. Here, a generative AI model (e.g., GPT) generates advice specific to the question and provides it as output.

[0818] Step 6:

[0819] The server sends the generated solutions to the user's device via the SNS app. The input here is the text data of the generated solutions, which is converted into a format that can be displayed on the device.

[0820] Step 7:

[0821] Users can view solutions displayed on their device and enter additional questions as needed. They can also immediately pay any fees incurred for legal consultation using the displayed payment options. Input is the user's selection, and output is a confirmation message upon completion of the transaction. Electronic payment services (such as Apple Pay and Google Pay) are used in this process.

[0822] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0823] This invention provides a system that allows users to easily ask legal questions and seek advice, while also understanding the user's emotions and providing solutions tailored to those emotions. This system includes three components: a server, a terminal, and the user, and further incorporates an emotion engine.

[0824] Users launch a social networking app on their device and enter their legal questions in text format. The message is sent from the device to the server. Here, the text entered by the user may include not only legal information but also emotional nuances.

[0825] When the server receives a message, it first uses natural language processing (NLP) to analyze legal keywords. This helps it understand the user's intent and the content of their inquiry. Next, it uses an emotion engine to identify the user's emotional state from the message. This information is used in subsequent processes to generate solutions.

[0826] Based on keywords analyzed by the server, it accesses a database to search for relevant legal information and precedents. Based on these search results, the generating artificial intelligence generates solutions. Here, the user's emotional state, as recognized by the emotion engine, is taken into consideration. For example, if the user is feeling anxious, the server adjusts its solution to provide a greater sense of reassurance.

[0827] The generated solutions are formatted as text messages and sent back to the device. The user can review the advice displayed on the device and enter any further questions they may have.

[0828] Furthermore, if the user is satisfied with the advice provided, they can easily make a payment using their device. Once the transaction is completed using the mobile payment service, the server verifies the information and terminates the transaction.

[0829] For example, if a user is struggling to reach an agreement with their spouse during divorce mediation, the emotion engine can be used to provide empathetic advice to alleviate the user's stress and anxiety. By providing emotionally sensitive legal support in this way, a more personalized experience can be offered to the user.

[0830] The following describes the processing flow.

[0831] Step 1:

[0832] Users launch a social networking app on their device, type a message requesting legal advice, and send it. The sent message may reflect not only legal questions but also the user's emotions.

[0833] Step 2:

[0834] The terminal sends messages from the user to the server. These messages reach the server via the communication network.

[0835] Step 3:

[0836] The server analyzes the received message, using natural language processing (NLP) techniques to extract legal keywords and context. This allows the server to understand the content of the consultation.

[0837] Step 4:

[0838] The server uses an emotion engine to analyze the user's emotions within a message. The emotion engine identifies emotional states such as joy, anxiety, and anger.

[0839] Step 5:

[0840] The server uses the extracted keywords to search the database for relevant legal information and case law. Based on these results, it prepares to create legal solutions.

[0841] Step 6:

[0842] The server utilizes a generative artificial intelligence model to generate solutions that take into account the user's emotions, as recognized by the emotion engine. This provides legal advice that is appropriate to the user's emotions.

[0843] Step 7:

[0844] The server formats the generated solution as a text message and sends it to the user's terminal to inform them of the solution.

[0845] Step 8:

[0846] The device receives messages from the server and displays them within the SNS app. Users can review the provided advice, enter new messages if they have further questions, and repeat the process from step 1.

[0847] Step 9:

[0848] If the user is satisfied with the advice, they will use their device to initiate the mobile payment service and follow the instructions to complete the payment. Once the payment is complete, the server will verify the information and complete the transaction.

[0849] (Example 2)

[0850] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0851] In modern society, there is a need for a system that can respond quickly and appropriately to legal questions and consultations from individuals. However, many current systems do not take into account the user's feelings and have difficulty providing personalized legal advice. Furthermore, generating legal solutions requires specialized knowledge, making it difficult for the average user to access. This invention aims to solve these problems and provide a user-friendly and accessible legal consultation system.

[0852] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0853] In this invention, the server includes means for receiving legal inquiry information via communication equipment, means for analyzing legal terminology using natural language processing, and means for performing sentiment analysis to identify emotional states. This enables users to receive personalized legal support that takes their emotional state into consideration quickly and efficiently.

[0854] "Communication equipment" refers to devices and infrastructure used for sending and receiving data.

[0855] "Legal inquiry information" refers to data that includes legal questions and consultations.

[0856] "Natural language processing" refers to the technology that enables computers to understand and analyze human language.

[0857] "Legal terminology" refers to specific keywords or phrases related to laws and precedents.

[0858] "Emotional analysis" refers to the process of identifying a user's emotional state from text data or audio data.

[0859] An "information aggregation structure" refers to a data structure that systematically organizes large amounts of data, enabling the efficient retrieval of necessary information.

[0860] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to generate new information and content based on given data and instructions.

[0861] A "solution" refers to information that describes specific measures and advice regarding the user's legal issues.

[0862] An "electronic payment system" refers to the technology and platform used to complete payments in a digital format.

[0863] This invention provides personalized solutions that take into account the user's emotions when they ask legal questions or seek advice. Specific embodiments are shown below.

[0864] Users launch communication software using their mobile devices or computers and input their legal inquiries in text format. This text is expected to include not only specific legal information but also context that reflects the user's emotions. For example, a sentence like, "I am anxious about divorce and child custody issues."

[0865] The terminal sends this entered text to the server via the internet. It is recommended to use a secure communication protocol for this transmission.

[0866] The server first processes the received text message using a natural language processing engine to analyze legal keywords. The analyzed data provides crucial information for understanding the user's inquiry.

[0867] Next, the server uses an emotion engine to identify the user's emotional state. This process allows it to determine whether the user is feeling anxious or stressed, or conversely, at ease.

[0868] Subsequently, the server uses the database system to search for relevant legal information and case precedents based on the analyzed keywords. By using query languages ​​such as SQL, data that matches the specified conditions can be retrieved quickly.

[0869] Based on the searched information, the server uses a generative AI model to generate legal solutions. During this process, the user's emotional state, as determined by sentiment analysis, is reflected, ensuring that advice is provided that takes the user's feelings into consideration.

[0870] The generated solution is sent back to the device, where the user can view it on the device's display. If the user wishes to add further questions, they can enter new text and send it again.

[0871] For example, if a user enters a question such as, "I want to know how legal advice can alleviate my anxiety," the system will identify the user's anxiety and return reassuring solutions to address it. An example of a prompt in this case would be, "Please provide advice for a user who is having difficulty reaching an agreement in divorce mediation. The user is feeling anxious."

[0872] This allows users to receive legal support that reflects their own feelings, enabling them to deal with legal issues with greater peace of mind.

[0873] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0874] Step 1:

[0875] The user enters legal questions into the terminal. The input is in text format and includes specific legal questions and consultations. An example of input might be, "What should I do if my rent payments are consistently behind?" The terminal temporarily stores the entered text data for use in subsequent processes.

[0876] Step 2:

[0877] The terminal sends the entered text data to the server. A secure communication protocol (e.g., HTTPS) is used for transmission to protect personal information. The transmitted data arrives at the server and is prepared for the next processing step.

[0878] Step 3:

[0879] The server passes the received text data to a natural language processing engine for analysis. The input here is text sent by the user, and the output extracts legal keywords and the user's intent. Specifically, morphological analysis and keyword extraction are performed.

[0880] Step 4:

[0881] The server uses an emotion analysis engine to identify the emotions contained in the message. The input is the user's text data, and the output is the user's emotional state (e.g., anxiety, relief, anger). This step involves emotion scoring and emotion classification of words.

[0882] Step 5:

[0883] The server uses the analyzed keywords to search the database for relevant legal information and case precedents. A database query is executed, with the analyzed keywords as input and the corresponding legal information as output. Specifically, information retrieval is performed using SQL.

[0884] Step 6:

[0885] The server generates legal solutions using a generative AI model based on search results and sentiment analysis data. The input is relevant legal information and user sentiment data, and the output is a solution tailored to the user's problem. In this process, the generative AI model constructs new solutions based on the data it has learned.

[0886] Step 7:

[0887] The server sends the generated solution to the terminal. The terminal receives it and displays it to the user in an appropriate format. A secure communication protocol is used again for transmission, and the solution is output as text. The user can review this and ask for further assistance if necessary.

[0888] Step 8:

[0889] If the user is satisfied with the advice provided, they will make a payment using the electronic payment system via the terminal. The input is the user's payment information, and the output is a notification that the payment has been completed. In this step, the transaction is processed through the payment gateway.

[0890] (Application Example 2)

[0891] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0892] A challenge exists in legal consultations where users are left feeling anxious and stressed because appropriate solutions are not provided that take their emotional state into consideration. Furthermore, the cumbersome payment process for related services after legal consultations can negatively impact the user experience. There is a need for a system that addresses these issues and provides emotionally sensitive legal consultations and smooth payment processes.

[0893] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0894] In this invention, the server includes means for receiving legal inquiry information and sentiment information via communication equipment; means for retrieving relevant legal information and sentiment information from a data store using natural language processing based on the legal inquiry information and sentiment information; means for generating solutions based on the relevant legal information and sentiment information using generative artificial intelligence; means for processing transactions through electronic transaction services; and means for providing a sentiment-sensitive user interface. This enables users to receive legal consultation in a sentiment-sensitive manner and to smoothly settle related services after the consultation.

[0895] "Communication equipment" refers to devices used by users to transmit legal inquiry information and emotional information.

[0896] "Legal inquiry information" refers to information that includes questions or consultations from users regarding legal matters.

[0897] "Emotional information" refers to information that indicates the user's emotional state, and is an important element when providing legal advice.

[0898] "Natural language processing" is a technology that enables computers to understand and process human language.

[0899] A "data store" is a recording medium that stores relevant legal and emotional information.

[0900] "Generative artificial intelligence" is a program that has the ability to generate new information and solutions based on data.

[0901] "Electronic transaction services" are services that allow for online transactions and payments.

[0902] "User interface" refers to the screens and means of operation that users use to interact with a system.

[0903] In order to implement this invention, it is necessary to effectively integrate communication equipment, servers, data stores, emotion engines, generative artificial intelligence, and electronic trading services.

[0904] System Configuration

[0905] Users use communication devices such as smartphones and personal computers to send legal questions and information along with emotional information. The device provides an intuitive user interface, designed to allow users to easily input information.

[0906] Processing flow

[0907] The server analyzes legal inquiry and sentiment information received from communication devices. Using Python's NLTK library and Transformers API, it performs natural language processing to identify legal keywords and intents. It also uses Microsoft's Azure Text Analytics and other tools to perform sentiment recognition and evaluate the user's emotional state. Based on this evaluation, it retrieves relevant legal information from a data store and generates solutions using generative artificial intelligence such as OpenAI's GPT-3. These generated solutions are then delivered to the user through a user interface.

[0908] Electronic payment

[0909] Furthermore, after providing solutions, the use of electronic transaction services such as Stripe and Square allows for smooth payment processing of related service fees and product purchases.

[0910] Specific example

[0911] For example, if a user requests "legal advice about purchasing an apartment" and emotional information detects anxiety, the system will generate detailed advice to alleviate concerns and present options such as hiring a lawyer or purchasing relevant books. Furthermore, payment services can be used to easily complete payments for related services.

[0912] As an example of a prompt, you can instruct the generating AI in the following format: "User question: What are the key points to check in a contract when purchasing an apartment? Emotional state: Anxious. Please generate careful and helpful advice."

[0913] In this way, a system is realized that provides legal consultations that take emotions into consideration and smooth settlements.

[0914] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0915] Step 1:

[0916] The terminal captures legal questions and emotions entered by the user. The input includes the user's text information and emotional nuances. After capturing this information, it is sent to the server via a communication device. The output is the legal question and emotional data, which is sent to the server.

[0917] Step 2:

[0918] The server analyzes received legal inquiry information and sentiment information. It takes text data and sentiment information as input, and uses Python's NLTK to extract keywords from the text. Next, it uses Microsoft Azure Text Analytics to analyze the sentiment and obtain the results. The output includes a list of analyzed keywords and the sentiment analysis results.

[0919] Step 3:

[0920] The server searches the data store for relevant legal information based on the analyzed keywords. It takes a list of keywords as input and executes SQL queries to retrieve the relevant legal information. The output is a set of relevant legal information.

[0921] Step 4:

[0922] The server generates solutions using a generative artificial intelligence model. It takes relevant legal information and sentiment analysis results as input, and sends prompts to the generative AI model (e.g., OpenAI's GPT-3) to generate solutions. The output is a specific, sentiment-sensitive solution.

[0923] Step 5:

[0924] The terminal displays the generated solutions to the user. The input is the solution text from the server, and the information is displayed to the user through a user interface in an easy-to-understand format. The output provides the user with the opportunity to review the solutions and choose further actions.

[0925] Step 6:

[0926] The user selects the necessary related services and makes payments through the electronic transaction service via the terminal. Inputs include the selected service information and payment information, and the transaction is completed using APIs such as Stripe or Square. Outputs include payment confirmation messages provided to both the user and the system.

[0927] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0928] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0929] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0930] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0931] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0932] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0933] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0934] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0935] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0936] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0937] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0938] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0939] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0940] 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.

[0941] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0942] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0943] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0944] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0945] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0946] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0947] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0948] The following is further disclosed regarding the embodiments described above.

[0949] (Claim 1)

[0950] A means of receiving legal inquiry information via a communication device,

[0951] A means for retrieving relevant legal information from a database using natural language processing based on the aforementioned legal inquiry information,

[0952] A means for generating solutions based on the relevant legal information using generative artificial intelligence,

[0953] Means for transmitting the aforementioned solution via a communication device,

[0954] A system that includes means for processing payments through mobile payment services.

[0955] (Claim 2)

[0956] The system according to claim 1, which analyzes inquiry information based on legal keywords.

[0957] (Claim 3)

[0958] The system according to claim 1, which can repeatedly provide the generated solutions.

[0959] "Example 1"

[0960] (Claim 1)

[0961] A means of receiving legal questions and information via a communication device,

[0962] A means for analyzing the aforementioned legal question information using natural language processing technology and extracting legal keywords,

[0963] A means for searching for relevant legal information from data storage based on the aforementioned legal keywords,

[0964] A means for generating solutions based on the relevant legal information using generative machine learning,

[0965] Means for transmitting the aforementioned solution via a communication device,

[0966] A system that includes means for processing transactions using electronic payment services.

[0967] (Claim 2)

[0968] The system according to claim 1, which uses generative machine learning to analyze question information and understand user needs.

[0969] (Claim 3)

[0970] The system according to claim 1, which enables the generated solution to be continuously provided to the user.

[0971] "Application Example 1"

[0972] (Claim 1)

[0973] A means of receiving information via a communication means,

[0974] Based on the aforementioned information, a means for searching for relevant information using natural language processing,

[0975] A means for generating proposals based on the aforementioned related information using generative artificial intelligence,

[0976] Means for transmitting the above proposal via communication means,

[0977] A means of processing transactions through electronic payment services,

[0978] A system that includes a means to select a payment method and complete a transaction.

[0979] (Claim 2)

[0980] The system according to claim 1, which analyzes information based on specific keywords.

[0981] (Claim 3)

[0982] The system according to claim 1, which enables the repeated provision of the generated proposals.

[0983] "Example 2 of combining an emotion engine"

[0984] (Claim 1)

[0985] A means of receiving legal inquiry information via communication equipment,

[0986] Based on the aforementioned legal inquiry information, a means for analyzing legal terminology using natural language processing,

[0987] A means for performing sentiment analysis to identify emotional states,

[0988] Based on the aforementioned analyzed legal terminology, a means for retrieving relevant legal information from the information aggregation structure,

[0989] A means of generating solutions that take into account the results of emotion analysis using generative artificial intelligence,

[0990] Means for transmitting the aforementioned solution via communication equipment,

[0991] A system that includes means for processing payments through an electronic payment system.

[0992] (Claim 2)

[0993] The system according to claim 1, which provides personalized legal support to individual users by analyzing the emotional state in a message.

[0994] (Claim 3)

[0995] The system according to claim 1, which can provide the generated solutions any number of times.

[0996] "Application example 2 when combining with an emotional engine"

[0997] (Claim 1)

[0998] A means of receiving legal inquiry information and sentiment information via communication equipment,

[0999] A means for retrieving relevant legal and sentiment information from a data store using natural language processing, based on the aforementioned legal inquiry information and sentiment information.

[1000] A means for generating solutions based on the aforementioned related legal information and emotional information using generative artificial intelligence,

[1001] Means for transmitting the aforementioned solution via communication equipment,

[1002] A means of processing transactions through electronic trading services,

[1003] A system that includes means for providing an emotionally sensitive user interface.

[1004] (Claim 2)

[1005] The system according to claim 1, which analyzes inquiry information based on legal keywords and sentiment values.

[1006] (Claim 3)

[1007] The system according to claim 1, wherein the generated solution is adjusted according to the emotional state and can be repeatedly provided. [Explanation of Symbols]

[1008] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving information via a communication means, Based on the aforementioned information, a means for searching for relevant information using natural language processing, A means for generating proposals based on the aforementioned related information using generative artificial intelligence, Means for transmitting the above proposal via communication means, A means of processing transactions through electronic payment services, A system that includes a means to select a payment method and complete a transaction.

2. The system according to claim 1, which analyzes information based on specific keywords.

3. The system according to claim 1, which enables the repeated provision of the generated proposals.

Images