system

Abstract

We provide the system. [Solution] The system analyzes the input business information using natural language processing, extracts keywords and concepts, searches for relevant laws and risk mitigation measures from the data storage device, obtains the latest legal information in conjunction with an external information storage device, automatically generates documents and communication content based on the search results, and maintains document consistency and normality using templates. A means by which the generated documents and search results can be visualized and displayed on an information terminal, and transmitted or retrieved by the user, A means for converting business information entered by a user into a digital format via a print-to-telegraph conversion means and transmitting it to a processing device via a communication network, A means of automatically sending the generated documents and communications to the relevant departments and individuals, In the field of electronic money transfer services, a means of analyzing user-provided service information, checking relevant financial laws and regulations, and generating necessary contracts and agreements, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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] Business processes in a company's legal department need to cope with frequent updates of regulations and diverse business contents, which consume a large amount of time and resources. In addition, when employees who are not confident in legal knowledge perform legal-related tasks, mistakes and risks are likely to occur, which may damage the efficiency and accuracy of the tasks. It is necessary to overcome these problems and enable legal tasks to be performed quickly and accurately.

Means for Solving the Problems

[0005] This invention provides a system that uses natural language processing technology to analyze business information entered by users and automatically searches for and presents relevant laws and regulations and risk mitigation measures. Furthermore, it includes a means for automatically generating documents and communications using AI, and ensures consistency and formality of documents through the use of templates. This streamlines legal work and creates an environment where even beginners can perform their duties with confidence. By linking with external databases to obtain the latest legal information, this system can always provide accurate information and immediately present appropriate actions to users.

[0006] "Entered business information" refers to data about specific business operations that users provide to the system, and this data serves as the information source from which the system performs analysis.

[0007] "Natural language processing" is a technology that enables computers to understand, interpret, and generate natural language used by humans, and is a means that systems use to analyze input information.

[0008] "Means for extracting keywords and concepts" refer to processes and algorithms for identifying and organizing important elements from input business information.

[0009] A "database" is an information resource that systematically collects relevant laws and regulations and risk response measures and stores them in a searchable format.

[0010] An "external database" is a source of information that always contains the latest legal information, provided by other organizations or services with which the system interacts.

[0011] A "generative AI model" refers to algorithms and technologies that utilize artificial intelligence to automatically generate new documents and information.

[0012] "Means for automatically generating documents and communications" refers to a function that automatically creates necessary official documents and communications using a generation AI model.

[0013] A "template" is a predefined type or form used to generate documents or communications in a consistent format.

[0014] A "terminal" is a device that a user directly uses as an interface to input business information and display output information from a system.

[0015] "Means that enable the transmission or download of documents" refers to software functions or processes for transmitting generated documents via email or other formats. [Brief explanation of the drawing]

[0016] [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]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] 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."

[0024] [First Embodiment]

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

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0031] 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.

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

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention is an automated system for corporate legal operations, which begins operating when a user inputs information related to the work via a terminal. The system is primarily composed of a generative AI model and a natural language processing engine that run on a server. Specific embodiments of this invention are described below.

[0038] The user inputs task details, such as a request to create a new contract, in natural language through the terminal's user interface. The terminal converts this information into a digital format and sends it to the server via the network.

[0039] The server analyzes the received business information using a natural language processing engine to extract keywords and important concepts. This analysis provides the server with the basic information needed to identify which laws and regulations are relevant.

[0040] Next, the server uses RAG (Retrieval-Augmented Generation) technology to search for relevant laws and risk mitigation measures from internal databases and external legal databases. In this step, the server can always obtain the latest legal information, including information on legal revisions.

[0041] Subsequently, the server automatically generates the necessary documents and communications using a generation AI model based on the extracted information and relevant laws and regulations. Document templates are used in this process to maintain consistency and formality.

[0042] The generated documents and search results are sent from the server to the terminal and presented visually to the user. This allows the user to easily check the necessary information and send emails to relevant parties or download generated documents with a single click from their terminal.

[0043] As a concrete example, consider a case where a user initiates a new international trade contract. Once the user enters the contract details, the server searches relevant international trade laws and generates the necessary contract template. In addition, it also presents risk mitigation measures regarding customs duties and compliance. Based on this information, the user can proceed with the contract process quickly and accurately.

[0044] This automated system allows users engaged in legal work to perform their duties efficiently and safely, even if they lack sufficient specialized knowledge.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] Users input work-related information into an input interface on their terminal using natural language. For example, they might input transaction terms and partner information necessary for creating a contract.

[0048] Step 2:

[0049] The terminal converts the input information into a digital format and sends it to the server over the network. During this process, the format is adjusted to ensure accurate data transfer.

[0050] Step 3:

[0051] The server activates a natural language processing engine and analyzes the received information. Specifically, it extracts important keywords and phrases and uses them to identify the entered business content.

[0052] Step 4:

[0053] The server uses RAG technology to search for relevant laws and risk mitigation measures from internal and external legal databases. At this stage, search results, including the latest legal amendments, are retrieved.

[0054] Step 5:

[0055] The server automatically generates necessary contracts and communications based on detected legal information, utilizing an AI model. This process incorporates document templates to maintain official formatting.

[0056] Step 6:

[0057] The generated documents and search results are sent from the server to the terminal. The terminal visually displays the information to the user, allowing the user to review the content.

[0058] Step 7:

[0059] Users can send or download generated documents via email through their devices. Workflows aligned with internal approval processes are also executed as needed.

[0060] (Example 1)

[0061] 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."

[0062] In legal work, there is a challenge in that users, without specialized knowledge, find it difficult to quickly and accurately grasp relevant laws and risk management methods and to create the necessary documents and communications. Furthermore, while it is necessary to constantly acquire the latest regulatory information and promptly share appropriate information with relevant departments and stakeholders, traditional methods make it difficult to do this efficiently.

[0063] 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.

[0064] In this invention, the server includes means for analyzing input business information using a processing engine and extracting key points and important concepts; means for obtaining relevant rules and risk response methods from information sources based on the extracted information, and for collecting the latest rule information in cooperation with publicly available information sources; and means for automatically creating necessary documents and communication content using a model based on the acquired information, and for using a format to maintain the integrity and fairness of the documents. As a result, users can efficiently and safely perform legal work even without specialized knowledge, and quickly share the generated information with relevant departments and stakeholders.

[0065] "Business information" refers to a series of pieces of information related to legal work, including manually entered data and digital data provided by the user.

[0066] A "processing engine" refers to software or algorithms that analyze input information and extract necessary keywords and important concepts.

[0067] "Key points and important concepts" refer to keywords and phrases that are particularly important within business information, and are fundamental information for identifying laws and regulations and risk response methods.

[0068] "Information sources" refer to internal or external databases or repositories that provide relevant laws and regulations and risk management methods.

[0069] "Rules" refer to relevant laws, regulations, or guidelines that indicate the standards that a company must adhere to in its legal operations.

[0070] "Risk management methods" refer to appropriate countermeasures for potential problems and risks that may arise in the course of business operations.

[0071] "Public sources" refer to means of accessing the latest laws and related information provided on the internet and other external information networks.

[0072] A "generating model" refers to a computational model or algorithm used to automatically create documents or communication content based on given data.

[0073] "Consistency" refers to a state in which generated documents and communications consistently represent accurate information.

[0074] "Fairness" refers to the fact that the generated documents and communications are formal and legally correct.

[0075] This invention is a system for automating corporate legal operations, in which users, terminals, and servers work together. The system primarily utilizes a natural language processing engine and generative AI models, which are described in detail below.

[0076] Users input work-related information in natural language through the terminal's user interface. For example, they might input something like, "I want to create a new contract." This information is then converted into a digital format by the terminal.

[0077] The terminal converts the input natural language information into structured data and sends it to the server via the communication network. This provides the server with the foundation for efficiently processing the information.

[0078] The server analyzes received business information using a natural language processing engine to extract keywords and important concepts. This analysis provides foundational information for identifying relevant laws and risk mitigation measures. Next, the server uses RAG (Retrieval-Augmented Generation) technology to retrieve relevant data, including the latest legal information, from internal databases and external legal information sources. The server then uses a generative AI model to automatically generate necessary documents and communications based on document templates. This ensures that consistent, official documentation is provided.

[0079] The generated documents and search results are sent from the server to the terminal, where the user can visually review them. The user can then email or download the generated documents to relevant parties with a single click.

[0080] As a concrete example, consider a case where a user initiates a contract related to international trade. When the user enters "I would like to discuss a new international trade contract," the server searches for relevant international trade laws and generates a contract template. It also simultaneously presents risk mitigation measures related to customs duties and compliance.

[0081] A concrete example of a prompt message would be, "I am considering a new contract for an international transaction, and I need information on relevant laws and risks. Please also provide a suitable contract template." This system enables users with insufficient specialized knowledge to perform legal tasks quickly and accurately.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] Users input work-related information in natural language using the terminal's user interface. For example, they might provide information such as "I want to create a new contract." The terminal receives this information, converts it to a digital format, and prepares it as structured data. This lays the foundation for efficient data processing.

[0085] Step 2:

[0086] The terminal transmits the converted digital format information to the server via the network. The data is transmitted using a secure communication protocol. The input is structured business information, and the output is digital data reaching the server. This allows the system to proceed to the next analysis step.

[0087] Step 3:

[0088] The server analyzes the received information using a natural language processing engine. It extracts keywords and important concepts from the input data and identifies key points relevant to the business. This step involves data processing through morphological and semantic analysis. The output consists of the extracted keywords and concepts.

[0089] Step 4:

[0090] Based on the extracted information, the server uses RAG (Retrieval-Augmented Generation) technology to search for relevant regulations and risk mitigation measures from internal databases and external legal databases. Up-to-date legal information is crucial, and data calculations are performed to ensure constantly updated information is obtained. The output consists of appropriate legal information and risk mitigation measures.

[0091] Step 5:

[0092] The server uses a generative AI model to automatically create necessary documents and communications based on acquired legal information and related risks. It leverages document templates to provide consistent, formal documents. Inputs are legal information and risk mitigation measures, while output is the generated documents. Specific examples include documents such as "proposals based on contract templates."

[0093] Step 6:

[0094] The server sends the generated documents and related information to the terminal. The terminal receives them and displays them visually to the user. The input is the generated data, and the output is the document and result information presented to the user. The user reviews the generated and presented documents and, if necessary, emails them to relevant departments or stakeholders or downloads them.

[0095] (Application Example 1)

[0096] 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."

[0097] In modern electronic money transfer services, regulations change frequently, and service providers must always comply with the latest laws and regulations. Therefore, there is a need to streamline and automate legal operations to reduce compliance risks. Traditional methods require a great deal of time and effort to analyze complex and extensive legal information and prepare documents, so there is a need to provide a system that simplifies this process and allows for quick responses even without specialized knowledge.

[0098] 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.

[0099] This invention includes a server that analyzes input business information using natural language processing means, extracts keywords and concepts, searches for relevant laws and regulations and risk countermeasures from a user data storage device, obtains the latest legal information in cooperation with an external information storage device, automatically generates documents and communication content based on the search results, and maintains document consistency and normality using templates; means for visualizing and displaying the generated documents and search results on an information terminal, and for users to transmit or retrieve them; and means for analyzing user-provided service information, checking relevant financial laws and regulations, and generating necessary contracts and agreements in the field of electronic money transfer services. This enables rapid response to legal regulations and efficient document creation.

[0100] "Business information" refers to detailed information about the services and activities provided by the user, which the system analyzes and uses for legal compliance and document creation.

[0101] "Natural language processing means" refers to a technology or device for extracting keywords and concepts from input natural language data and understanding their meaning.

[0102] A "data storage device" is a database that stores legal information, past business data, and other data, and allows for searching and retrieval when needed.

[0103] An "external information storage device" is a device or system that links with legal databases or third-party databases located outside the company to acquire the latest and most relevant information.

[0104] An "information terminal" is an electronic device such as a computer, smartphone, or tablet used by a user, which can display and operate information from a system.

[0105] A "template" is a predetermined framework or format used to maintain consistency in the form and structure of a document.

[0106] "Electronic fund transfer services" are services that transfer money and assets electronically via the internet, and compliance with laws and regulations is a crucial aspect of this field.

[0107] A "contract" is an official document that records the terms of an agreement exchanged between a service provider and its user.

[0108] A "memorandum of agreement" is a legally binding official document that describes matters agreed upon between the parties involved.

[0109] A "prompt message" is a sentence that prompts the user to take the next action or confirm something, and is used as an instruction or hint from the system.

[0110] The system that realizes this invention is built to streamline the application of legal regulations and the automated document generation process in the modern field of electronic money transfer services. It primarily functions through the interface between a server, terminals, and users.

[0111] The server is primarily built using Python and the Flask framework. For natural language processing, the Hugging Face Transformers library is used to analyze user input in detail. This analysis process extracts important keywords and concepts, and relevant laws and risk mitigation measures are retrieved from the user data storage system and external information storage systems.

[0112] The terminal is built using React Native and provides an interface for users to input information. The user's entered work information is converted to a digital format in real time and sent to the server over the network. The terminal also visually displays the results received from the server, allowing users to review the generated documents and retrieve or send them as needed.

[0113] For example, when a user starts a new subscription service, they enter the service details into the application, and the server checks for relevant laws and regulations and generates the necessary contracts and agreements. Furthermore, the generated documents maintain consistency and normality using templates. Users can review these documents on screen and send them to relevant parties via email.

[0114] A concrete example of a prompt message is: "Please check the necessary laws and regulations to launch a new digital content subscription service and create a terms of service template." This message allows users to intuitively understand what information is needed and enables them to complete their tasks quickly.

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The user enters business information through the terminal's user interface. Here, detailed data for a new subscription service is entered. The terminal converts this information into a digital format and sends it to the server as structured data. Data entry and format conversion take place.

[0118] Step 2:

[0119] The server analyzes the received business information. A natural language processing engine handles the analysis, extracting important keywords and concepts from the input data. In this processing step, the transformed data becomes the input for analysis, and the data with extracted keywords and concepts is output. Specifically, Hugging Face's Transformers perform the NLP analysis.

[0120] Step 3:

[0121] The server searches internal databases and external information storage devices based on extracted keywords and concepts to retrieve relevant regulations and risk mitigation measures. Data processing in this step involves generating search queries and querying databases. The output includes the latest regulations and risk information.

[0122] Step 4:

[0123] Using a generative AI model, the server generates relevant legal information and the contracts and agreements required by the user. Templates are used to maintain document consistency and normality. Here, acquired legal information and templates serve as input, and the completed document is output based on them. This process also includes a document generation engine.

[0124] Step 5:

[0125] The server sends the generated document to the user so that it is displayed on the terminal. The user can then view the generated document on their terminal. In this step, visualization processing is performed for data display, and the output is sent to the user. The user can also download the document or send it to relevant parties as needed.

[0126] 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.

[0127] This invention combines an automated system for efficiently supporting corporate legal operations with an emotion engine that recognizes emotions from user input information. In this system, users input business information via a terminal, and a server plays a primary role in efficiently and appropriately processing that information.

[0128] For example, when creating a contract for a new project, the user uses a terminal to input the necessary information. During this input process, the system uses an emotion engine to analyze the user's emotions from the entered text. This process allows the server to understand the user's emotional state and select the most appropriate communication method.

[0129] The server first uses a natural language processing engine to analyze the input business information. Keywords and phrases extracted through this analysis are then searched against internal and external legal databases. The search results include the latest legal information, and based on this, appropriate legal regulations and risk mitigation measures are selected.

[0130] Next, the server automatically generates the necessary documents and communications using a generative AI model based on the output of the emotion engine. The tone and content are adjusted according to the emotion, resulting in better communication for the user. These documents are generated using official templates, maintaining consistency and proper formatting.

[0131] The generated documents and legal information are sent from the server to the terminal and presented visually to the user. The user can review the provided information and, if necessary, automatically send it via email to the relevant departments or individuals. Furthermore, the system analyzes and provides feedback based on the recognized user sentiment information to improve the user experience and further streamline operations.

[0132] As a concrete example, consider a scenario where a user is negotiating a contract while feeling anxious about an international transaction. In this case, the emotion engine detects the user's anxiety in their input, and the server adjusts the wording and content of the document to provide greater reassurance. In this way, the user can proceed with the contract negotiation with greater confidence.

[0133] Thus, this invention not only advances the automation of legal work but also enables flexible responses to emotional needs, providing an environment in which even users who are unsure of their legal knowledge can comfortably perform their duties.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] Users input business information, specifically contract details and context, into the terminal's input interface using natural language. This information needs to include not only regular business data but also the user's emotional expression.

[0137] Step 2:

[0138] The terminal converts the input information into a digital format and sends it to the server. During this process, accuracy of the information and optimization of network traffic are ensured.

[0139] Step 3:

[0140] The server uses a natural language processing engine to analyze the received information and extract keywords and phrases relevant to the business. This is a preparatory step for determining the scope of application of regulations.

[0141] Step 4:

[0142] The server drives an emotion engine to analyze the user's emotions. From the input natural language data, it identifies emotional states such as reassurance, anxiety, and impatience.

[0143] Step 5:

[0144] The server utilizes RAG technology to search for relevant laws and risk mitigation measures from internal and external databases. This always includes the latest legal information, and its relevance is evaluated based on extracted keywords.

[0145] Step 6:

[0146] Based on the output from the emotion engine and natural language processing engine, the server uses a generative AI model to automatically generate documents and communications that are sensitive to the user's emotions. Document templates are used during generation to ensure a consistent and formal tone.

[0147] Step 7:

[0148] The generated documents and legal information are sent from the server to the terminal and displayed. The user reviews them and checks that the content of the documents is appropriate for their needs.

[0149] Step 8:

[0150] Based on the information provided, users send generated documents and emails to the relevant departments and individuals. If necessary, they can perform the sending action with a single click from their device.

[0151] Step 9:

[0152] After a transaction or process is completed, the server analyzes user sentiment data and provides feedback to improve business processes and enhance the user experience. This feedback is used for continuous system improvement.

[0153] (Example 2)

[0154] 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".

[0155] Corporate legal work requires access to specialized legal information, handling vast amounts of business information, and effective communication with stakeholders. However, traditional legal systems lack mechanisms to comprehensively support these elements. Furthermore, they may fail to consider user emotions, potentially leading to a decline in communication quality. This situation makes it difficult for users with limited legal knowledge to perform their duties smoothly.

[0156] 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.

[0157] In this invention, the server includes means for analyzing input business information using natural language processing and extracting features from the information; means for searching for relevant rule information from a database based on the extracted information and linking data with external information sources; and means for recognizing the emotional state from the input information using an emotion engine that analyzes the user's emotions. This enables the acquisition of integrated legal information and communication that takes the user's emotions into consideration.

[0158] "Business information" refers to information necessary for an organization to carry out its operations, such as contract terms, names of stakeholders, and project outlines.

[0159] "Natural language processing" is a technology that enables computers to understand and process human language, and includes methods for extracting meaning and features from text data.

[0160] An "emotion engine" refers to a technology that analyzes a user's emotional state from input text data and recognizes emotional tones such as positive, negative, and neutral.

[0161] A "generative AI model" refers to artificial intelligence technology that learns from large amounts of data and automatically generates new text and information.

[0162] A "template" refers to a predefined format or style used to maintain consistency and formality in document creation.

[0163] A "communication interface" refers to the technology and devices used to convert user input data into a digital format and transmit it to a server via a network.

[0164] "External information sources" refer to information provision platforms and resources outside the organization that are used to obtain databases and legal information.

[0165] This invention is a system that automates corporate legal operations and enables communication that takes user emotions into consideration. This system is implemented through the interaction of users, terminals, and servers.

[0166] User: Users input business information through their terminals. This includes data such as contract terms, names of stakeholders, and project outlines. The information entered by the user is analyzed in real time by an emotion engine, which recognizes the user's emotional state.

[0167] Terminal: The terminal converts user input into digital information and transmits that information to the server via a communication interface. This conversion typically uses standard keyboard input or voice input technology.

[0168] Server: The server analyzes the received business information using a natural language processing engine (e.g., Google® Cloud Natural Language API or spaCy). This analysis extracts important features from the text. Based on this, it refers to databases and external information sources to obtain relevant legal information and risk mitigation measures. In parallel, an emotion engine (e.g., IBM Watson® or Microsoft® Azure® Sentiment Analysis API) analyzes the user's emotional state and generates information that meets their emotional needs.

[0169] Generative AI models (e.g., OpenAI's GPT series) automatically create contracts and communications based on analyzed information and sentiment data, formatting the documents using formal templates. These documents are then sent to the terminal and presented visually to the user.

[0170] Specific example: Consider a scenario where a user is feeling anxious about a new overseas transaction when drafting a contract. The system detects this anxiety using an emotion engine, and the server uses a generation AI model to automatically generate a contract that includes reassuring language. The user can then review the generated document in their browser and confidently proceed to the next step. An example of a prompt generated by this system might be: "Please generate a contract for a new project that includes reassuring language to alleviate anxiety. The contract concerns an acquisition, and should have a reassuring tone."

[0171] Thus, the present invention combines the efficiency of legal work with emotion-based adjustments, making it possible to provide a more user-friendly legal environment.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] The user uses a terminal to input the necessary business information for the new project contract. This information includes contract terms, project overview, and names of stakeholders. The input is converted into a digital format via keyboard or voice input.

[0175] Step 2:

[0176] The terminal transmits business information in digital format, obtained from the user, to the server via a communication interface. In this process, the input data is packaged in an appropriate format and sent to the server over the network.

[0177] Step 3:

[0178] The server uses a natural language processing engine to analyze the received business information. First, it tokenizes the text data and extracts important features and keywords from it. The extracted results become input for querying legal information in the database.

[0179] Step 4:

[0180] In parallel, the server uses an emotion engine to analyze the user's emotional state. It calculates an emotion score from the text and recognizes emotional tones such as positive, negative, and neutral. The output serves as the basis for tone adjustment in subsequent document generation.

[0181] Step 5:

[0182] The server combines the output of natural language processing (keywords and features) with the results of sentiment analysis, and retrieves relevant legal information by referencing databases and external sources. This identifies the latest legal information and risk mitigation measures necessary for the user's business.

[0183] Step 6:

[0184] The server uses a generative AI model to automatically generate contracts and communications based on analysis results and sentiment data. The model adjusts the tone and expression of the documents according to the sentiment information and maintains document consistency using official templates. The generated documents are formatted in electronic format.

[0185] Step 7:

[0186] The server sends the generated documents and legal information to the terminal. The terminal visually displays the received data to the user. The user can review the displayed documents and, if necessary, edit them or send them to relevant parties.

[0187] Step 8:

[0188] The server generates feedback based on overall system usage and sentiment analysis to improve operational efficiency and optimize the user experience. It analyzes user sentiment data and trends related to work content to derive improvement measures that will be useful in the future.

[0189] (Application Example 2)

[0190] 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".

[0191] In online transactions and electronic payments, users often experience anxiety and doubt, which can hinder their purchasing decisions. Furthermore, processes such as obtaining information on legal compliance and risk management, and generating documentation, often lack sufficient support to address users' emotional needs. Therefore, improving the user experience and operational efficiency are key challenges.

[0192] 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.

[0193] In this invention, the server includes means for analyzing input business information using natural language processing and extracting keywords and concepts; means for searching for relevant laws and risk countermeasures from a database based on the extracted information and coordinating with an external database to obtain the latest legal information; and emotion recognition means for analyzing the user's emotions and providing communication means corresponding to those emotions. This enables the provision of appropriate communication that takes the user's emotions into consideration, as well as the rapid acquisition of legal information and the streamlining of business procedures.

[0194] "Entered business information" refers to the business-related data and information that users provide to the system.

[0195] "Natural language processing" is a technology that uses computers to understand, appropriately analyze, and process the language that humans use on a daily basis.

[0196] "Keywords and concepts" are important words or concepts with specific meanings or themes that are extracted from the input information.

[0197] A "database" is a collection of data that systematically stores information and allows for searching and updating.

[0198] "Legal regulations and risk response measures" refer to information regarding laws and regulations, as well as measures to prevent and manage the occurrence of risks.

[0199] An "external database" is another database that exists outside the system and can be linked to.

[0200] "Emotion recognition means" refers to technologies and methods for detecting and analyzing emotions from user input information.

[0201] "Communication methods" refer to techniques and devices used for transmitting and communicating information with users.

[0202] "Generative artificial intelligence technology" is a technology that uses machine learning and advanced algorithms to enable humans to think and make judgments similar to those of humans.

[0203] This invention involves a system that receives business information provided by the user via a user interface, converts that information into a digital format, and transmits it to a server. The server analyzes the input information using a natural language processing engine and extracts keywords and concepts. Software such as the Google Cloud Natural Language API can be used for this natural language processing.

[0204] The extracted information is cross-referenced with a database to search for relevant laws and risk mitigation measures. The server quickly obtains the latest legal information by linking with an external database. A standard database management system is used for this data linkage.

[0205] Next, the server uses an emotion recognition engine to understand the user's emotional state and executes commands based on those emotions. It utilizes generative artificial intelligence technology (generative AI models) to adjust the tone and content of the communication generated according to the user's emotions. OpenAI's GPT-3® is a suitable generative AI model for this purpose.

[0206] As a concrete example, if a user feels uneasy during an electronic payment, the system could generate an automated response to reassure them that "this transaction is securely protected." In this case, one possible example is to input the text "Suggest a reassuring message when the user feels uneasy" as a prompt into the AI ​​model.

[0207] Ultimately, the generated documents and adapted response messages are visually displayed on the terminal, allowing the user to review them and proceed with their work. Furthermore, the user can automatically send the results to relevant departments and stakeholders, ensuring smooth workflow.

[0208] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0209] Step 1:

[0210] Users input business information via a terminal. This input information is in natural language text format. The input information is converted into a digital format and sent to the server. This conversion makes the information processable as digital data.

[0211] Step 2:

[0212] The server uses a natural language processing engine to analyze the input information. It syntactically parses the received text data and extracts relevant keywords and concepts. The output obtained from the analysis is a list of important themes and phrases contained in the text.

[0213] Step 3:

[0214] The server queries a database based on the extracted keywords. This query retrieves information on relevant laws and risk mitigation measures. If necessary, it also communicates with external databases to obtain the latest legal information. As a result, a set of legal information is output.

[0215] Step 4:

[0216] To analyze user emotions, the server uses an emotion recognition engine. It estimates the user's emotional state from the input information. Using keywords as input, an emotion score is output. This score quantitatively represents the user's emotions.

[0217] Step 5:

[0218] The server automatically generates appropriate communication content using a generative AI model. It takes a prompt as input and outputs a response message with a tone and content that corresponds to the user's emotions. This model adjusts the text based on an emotion score.

[0219] Step 6:

[0220] The generated response message and document are returned to the terminal and displayed visually. The user can review this and proceed with their work based on its content. The displayed information can be downloaded or sent via email.

[0221] Step 7:

[0222] If necessary, the server automatically sends generated documents and messages to the relevant departments and individuals. This function allows for the forwarding of outputted documents via email or internal communication systems, thereby facilitating efficient information sharing.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] [Second Embodiment]

[0227] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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".

[0239] This invention is an automated system for corporate legal operations, which begins operating when a user inputs information related to the work via a terminal. The system is primarily composed of a generative AI model and a natural language processing engine that run on a server. Specific embodiments of this invention are described below.

[0240] The user inputs task details, such as a request to create a new contract, in natural language through the terminal's user interface. The terminal converts this information into a digital format and sends it to the server via the network.

[0241] The server analyzes the received business information using a natural language processing engine to extract keywords and important concepts. This analysis provides the server with the basic information needed to identify which laws and regulations are relevant.

[0242] Next, the server uses RAG (Retrieval-Augmented Generation) technology to search for relevant laws and risk mitigation measures from internal databases and external legal databases. In this step, the server can always obtain the latest legal information, including information on legal revisions.

[0243] Subsequently, the server automatically generates the necessary documents and communications using a generation AI model based on the extracted information and relevant laws and regulations. Document templates are used in this process to maintain consistency and formality.

[0244] The generated documents and search results are sent from the server to the terminal and presented visually to the user. This allows the user to easily check the necessary information and send emails to relevant parties or download generated documents with a single click from their terminal.

[0245] As a concrete example, consider a case where a user initiates a new international trade contract. Once the user enters the contract details, the server searches relevant international trade laws and generates the necessary contract template. In addition, it also presents risk mitigation measures regarding customs duties and compliance. Based on this information, the user can proceed with the contract process quickly and accurately.

[0246] This automated system allows users engaged in legal work to perform their duties efficiently and safely, even if they lack sufficient specialized knowledge.

[0247] The following describes the processing flow.

[0248] Step 1:

[0249] Users input work-related information into an input interface on their terminal using natural language. For example, they might input transaction terms and partner information necessary for creating a contract.

[0250] Step 2:

[0251] The terminal converts the input information into a digital format and sends it to the server over the network. During this process, the format is adjusted to ensure accurate data transfer.

[0252] Step 3:

[0253] The server activates a natural language processing engine and analyzes the received information. Specifically, it extracts important keywords and phrases and uses them to identify the entered business content.

[0254] Step 4:

[0255] The server uses RAG technology to search for relevant laws and risk mitigation measures from internal and external legal databases. At this stage, search results, including the latest legal amendments, are retrieved.

[0256] Step 5:

[0257] The server automatically generates necessary contracts and communications based on detected legal information, utilizing an AI model. This process incorporates document templates to maintain official formatting.

[0258] Step 6:

[0259] The generated documents and search results are sent from the server to the terminal. The terminal visually displays the information to the user, allowing the user to review the content.

[0260] Step 7:

[0261] Users can send or download generated documents via email through their devices. Workflows aligned with internal approval processes are also executed as needed.

[0262] (Example 1)

[0263] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0264] In legal work, there is a challenge in that users, without specialized knowledge, find it difficult to quickly and accurately grasp relevant laws and risk management methods and to create the necessary documents and communications. Furthermore, while it is necessary to constantly acquire the latest regulatory information and promptly share appropriate information with relevant departments and stakeholders, traditional methods make it difficult to do this efficiently.

[0265] 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.

[0266] In this invention, the server includes means for analyzing input business information using a processing engine and extracting key points and important concepts; means for obtaining relevant rules and risk response methods from information sources based on the extracted information, and for collecting the latest rule information in cooperation with publicly available information sources; and means for automatically creating necessary documents and communication content using a model based on the acquired information, and for using a format to maintain the integrity and fairness of the documents. As a result, users can efficiently and safely perform legal work even without specialized knowledge, and quickly share the generated information with relevant departments and stakeholders.

[0267] "Business information" refers to a series of pieces of information related to legal work, including manually entered data and digital data provided by the user.

[0268] A "processing engine" refers to software or algorithms that analyze input information and extract necessary keywords and important concepts.

[0269] "Key points and important concepts" refer to keywords and phrases that are particularly important within business information, and are fundamental information for identifying laws and regulations and risk response methods.

[0270] "Information sources" refer to internal or external databases or repositories that provide relevant laws and regulations and risk management methods.

[0271] "Rules" refer to relevant laws, regulations, or guidelines that indicate the standards that a company must adhere to in its legal operations.

[0272] "Risk management methods" refer to appropriate countermeasures for potential problems and risks that may arise in the course of business operations.

[0273] "Public sources" refer to means of accessing the latest laws and related information provided on the internet and other external information networks.

[0274] A "generating model" refers to a computational model or algorithm used to automatically create documents or communication content based on given data.

[0275] "Consistency" refers to a state in which generated documents and communications consistently represent accurate information.

[0276] "Fairness" refers to the fact that the generated documents and communications are formal and legally correct.

[0277] This invention is a system for automating corporate legal operations, in which users, terminals, and servers work together. The system primarily utilizes a natural language processing engine and generative AI models, which are described in detail below.

[0278] Users input work-related information in natural language through the terminal's user interface. For example, they might input something like, "I want to create a new contract." This information is then converted into a digital format by the terminal.

[0279] The terminal converts the input natural language information into structured data and sends it to the server via the communication network. This provides the server with the foundation for efficiently processing the information.

[0280] The server analyzes received business information using a natural language processing engine to extract keywords and important concepts. This analysis provides foundational information for identifying relevant laws and risk mitigation measures. Next, the server uses RAG (Retrieval-Augmented Generation) technology to retrieve relevant data, including the latest legal information, from internal databases and external legal information sources. The server then uses a generative AI model to automatically generate necessary documents and communications based on document templates. This ensures that consistent, official documentation is provided.

[0281] The generated documents and search results are sent from the server to the terminal, where the user can visually review them. The user can then email or download the generated documents to relevant parties with a single click.

[0282] As a concrete example, consider a case where a user initiates a contract related to international trade. When the user enters "I would like to discuss a new international trade contract," the server searches for relevant international trade laws and generates a contract template. It also simultaneously presents risk mitigation measures related to customs duties and compliance.

[0283] A concrete example of a prompt message would be, "I am considering a new contract for an international transaction, and I need information on relevant laws and risks. Please also provide a suitable contract template." This system enables users with insufficient specialized knowledge to perform legal tasks quickly and accurately.

[0284] The flow of the specific process in Example 1 will be described with reference to FIG. 11.

[0285] Step 1:

[0286] The user uses the user interface of the terminal to input information related to the business in natural language. Information such as "want to create a new contract" is provided as the input. The terminal receives this information, converts it into a digital format, and prepares it as structured data. This builds the basis for efficient data processing.

[0287] Step 2:

[0288] The terminal transmits the converted digital format information to the server via the network. At this time, the data is transmitted by a secure communication protocol. The input is structured business information, and the output is the digital data that reaches the server. This enables the next analysis step to proceed.

[0289] Step 3:

[0290] The server analyzes the received information using a natural language processing engine. Keywords and important concepts are extracted from the input data to identify the main points related to the business. In this step, data processing is performed through morphological analysis and semantic analysis. The output is the extracted keywords and concepts.

[0291] Step 4:

[0292] The server uses RAG (Retrieval-Augmented Generation) technology based on the extracted information to search for relevant rules and risk countermeasures from the in-house database and external legal databases. Here, the latest legal information is important, and data operations are performed to obtain always-updated information. The output is appropriate legal information and risk countermeasures.

[0293] Step 5:

[0294] The server uses a generative AI model to automatically create necessary documents and communications based on acquired legal information and related risks. It leverages document templates to provide consistent, formal documents. Inputs are legal information and risk mitigation measures, while output is the generated documents. Specific examples include documents such as "proposals based on contract templates."

[0295] Step 6:

[0296] The server sends the generated documents and related information to the terminal. The terminal receives them and displays them visually to the user. The input is the generated data, and the output is the document and result information presented to the user. The user reviews the generated and presented documents and, if necessary, emails them to relevant departments or stakeholders or downloads them.

[0297] (Application Example 1)

[0298] 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."

[0299] In modern electronic money transfer services, regulations change frequently, and service providers must always comply with the latest laws and regulations. Therefore, there is a need to streamline and automate legal operations to reduce compliance risks. Traditional methods require a great deal of time and effort to analyze complex and extensive legal information and prepare documents, so there is a need to provide a system that simplifies this process and allows for quick responses even without specialized knowledge.

[0300] 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.

[0301] In this invention, the server analyzes the input business information by means of natural language analysis, extracts keywords and concepts, searches for relevant laws and regulations and risk response measures from the utilization data storage device, obtains the latest legal information by collaborating with the external information storage device, automatically generates documents and communication content based on the search results, and uses templates to maintain document consistency and normality. It also visualizes and displays the generated documents and search results on an information terminal, enabling the user to perform transmission or acquisition. In the field of electronic fund transfer services, it includes means for analyzing service information provided by users, checking relevant financial regulations, and generating necessary contracts and agreements. This enables rapid response to legal regulations and efficient document creation.

[0302] "Business information" refers to detailed information regarding the services and activities provided by users, which is data that the system analyzes and uses for compliance with laws and regulations and document creation.

[0303] "Natural language analysis means" refers to a technology or device that extracts keywords and concepts from input natural language data and understands the meaning.

[0304] "Utilization data storage device" refers to a database that stores legal information, past business data, etc., and enables search and extraction when necessary.

[0305] "External information storage device" refers to a device or system that collaborates with legal databases or third-party databases outside the enterprise to obtain the latest and relevant information.

[0306] "Information terminal" refers to electronic devices such as computers, smartphones, and tablets used by users, which can display and operate on information from the system.

[0307] "Template" refers to a predefined framework or format used to consistently maintain the form and structure of a document.

[0308] "Electronic fund transfer services" are services that transfer money and assets electronically via the internet, and compliance with laws and regulations is a crucial aspect of this field.

[0309] A "contract" is an official document that records the terms of an agreement exchanged between a service provider and its user.

[0310] A "memorandum of agreement" is a legally binding official document that describes matters agreed upon between the parties involved.

[0311] A "prompt message" is a sentence that prompts the user to take the next action or confirm something, and is used as an instruction or hint from the system.

[0312] The system that realizes this invention is built to streamline the application of legal regulations and the automated document generation process in the modern field of electronic money transfer services. It primarily functions through the interface between a server, terminals, and users.

[0313] The server is primarily built using Python and the Flask framework. For natural language processing, the Hugging Face Transformers library is used to analyze user input in detail. This analysis process extracts important keywords and concepts, and relevant laws and risk mitigation measures are retrieved from the user data storage system and external information storage systems.

[0314] The terminal is built using React Native and provides an interface for users to input information. The user's entered work information is converted to a digital format in real time and sent to the server over the network. The terminal also visually displays the results received from the server, allowing users to review the generated documents and retrieve or send them as needed.

[0315] For example, when a user starts a new subscription service, they enter the service details into the application, and the server checks for relevant laws and regulations and generates the necessary contracts and agreements. Furthermore, the generated documents maintain consistency and normality using templates. Users can review these documents on screen and send them to relevant parties via email.

[0316] A concrete example of a prompt message is: "Please check the necessary laws and regulations to launch a new digital content subscription service and create a terms of service template." This message allows users to intuitively understand what information is needed and enables them to complete their tasks quickly.

[0317] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0318] Step 1:

[0319] The user enters business information through the terminal's user interface. Here, detailed data for a new subscription service is entered. The terminal converts this information into a digital format and sends it to the server as structured data. Data entry and format conversion take place.

[0320] Step 2:

[0321] The server analyzes the received business information. A natural language processing engine handles the analysis, extracting important keywords and concepts from the input data. In this processing step, the transformed data becomes the input for analysis, and the data with extracted keywords and concepts is output. Specifically, Hugging Face's Transformers perform the NLP analysis.

[0322] Step 3:

[0323] The server searches internal databases and external information storage devices based on extracted keywords and concepts to retrieve relevant regulations and risk mitigation measures. Data processing in this step involves generating search queries and querying databases. The output includes the latest regulations and risk information.

[0324] Step 4:

[0325] Using a generative AI model, the server generates relevant legal information and the contracts and agreements required by the user. Templates are used to maintain document consistency and normality. Here, acquired legal information and templates serve as input, and the completed document is output based on them. This process also includes a document generation engine.

[0326] Step 5:

[0327] The server sends the generated document to the user so that it is displayed on the terminal. The user can then view the generated document on their terminal. In this step, visualization processing is performed for data display, and the output is sent to the user. The user can also download the document or send it to relevant parties as needed.

[0328] 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.

[0329] This invention combines an automated system for efficiently supporting corporate legal operations with an emotion engine that recognizes emotions from user input information. In this system, users input business information via a terminal, and a server plays a primary role in efficiently and appropriately processing that information.

[0330] For example, when creating a contract for a new project, the user uses a terminal to input the necessary information. During this input process, the system uses an emotion engine to analyze the user's emotions from the entered text. This process allows the server to understand the user's emotional state and select the most appropriate communication method.

[0331] The server first uses a natural language processing engine to analyze the input business information. Keywords and phrases extracted through this analysis are then searched against internal and external legal databases. The search results include the latest legal information, and based on this, appropriate legal regulations and risk mitigation measures are selected.

[0332] Next, the server automatically generates the necessary documents and communications using a generative AI model based on the output of the emotion engine. The tone and content are adjusted according to the emotion, resulting in better communication for the user. These documents are generated using official templates, maintaining consistency and proper formatting.

[0333] The generated documents and legal information are sent from the server to the terminal and presented visually to the user. The user can review the provided information and, if necessary, automatically send it via email to the relevant departments or individuals. Furthermore, the system analyzes and provides feedback based on the recognized user sentiment information to improve the user experience and further streamline operations.

[0334] As a concrete example, consider a scenario where a user is negotiating a contract while feeling anxious about an international transaction. In this case, the emotion engine detects the user's anxiety in their input, and the server adjusts the wording and content of the document to provide greater reassurance. In this way, the user can proceed with the contract negotiation with greater confidence.

[0335] Thus, this invention not only advances the automation of legal work but also enables flexible responses to emotional needs, providing an environment in which even users who are unsure of their legal knowledge can comfortably perform their duties.

[0336] The following describes the processing flow.

[0337] Step 1:

[0338] Users input business information, specifically contract details and context, into the terminal's input interface using natural language. This information needs to include not only regular business data but also the user's emotional expression.

[0339] Step 2:

[0340] The terminal converts the input information into a digital format and sends it to the server. During this process, accuracy of the information and optimization of network traffic are ensured.

[0341] Step 3:

[0342] The server uses a natural language processing engine to analyze the received information and extract keywords and phrases relevant to the business. This is a preparatory step for determining the scope of application of regulations.

[0343] Step 4:

[0344] The server drives an emotion engine to analyze the user's emotions. From the input natural language data, it identifies emotional states such as reassurance, anxiety, and impatience.

[0345] Step 5:

[0346] The server utilizes RAG technology to search for relevant laws and risk mitigation measures from internal and external databases. This always includes the latest legal information, and its relevance is evaluated based on extracted keywords.

[0347] Step 6:

[0348] Based on the output from the emotion engine and natural language processing engine, the server uses a generative AI model to automatically generate documents and communications that are sensitive to the user's emotions. Document templates are used during generation to ensure a consistent and formal tone.

[0349] Step 7:

[0350] The generated documents and legal information are sent from the server to the terminal and displayed. The user reviews them and checks that the content of the documents is appropriate for their needs.

[0351] Step 8:

[0352] Based on the information provided, users send generated documents and emails to the relevant departments and individuals. If necessary, they can perform the sending action with a single click from their device.

[0353] Step 9:

[0354] After a transaction or process is completed, the server analyzes user sentiment data and provides feedback to improve business processes and enhance the user experience. This feedback is used for continuous system improvement.

[0355] (Example 2)

[0356] 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".

[0357] Corporate legal work requires access to specialized legal information, handling vast amounts of business information, and effective communication with stakeholders. However, traditional legal systems lack mechanisms to comprehensively support these elements. Furthermore, they may fail to consider user emotions, potentially leading to a decline in communication quality. This situation makes it difficult for users with limited legal knowledge to perform their duties smoothly.

[0358] 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.

[0359] In this invention, the server includes means for analyzing input business information using natural language processing and extracting features from the information; means for searching for relevant rule information from a database based on the extracted information and linking data with external information sources; and means for recognizing the emotional state from the input information using an emotion engine that analyzes the user's emotions. This enables the acquisition of integrated legal information and communication that takes the user's emotions into consideration.

[0360] "Business information" refers to information necessary for an organization to carry out its operations, such as contract terms, names of stakeholders, and project outlines.

[0361] "Natural language processing" is a technology that enables computers to understand and process human language, and includes methods for extracting meaning and features from text data.

[0362] An "emotion engine" refers to a technology that analyzes a user's emotional state from input text data and recognizes emotional tones such as positive, negative, and neutral.

[0363] A "generative AI model" refers to artificial intelligence technology that learns from large amounts of data and automatically generates new text and information.

[0364] A "template" refers to a predefined format or style used to maintain consistency and formality in document creation.

[0365] A "communication interface" refers to the technology and devices used to convert user input data into a digital format and transmit it to a server via a network.

[0366] "External information sources" refer to information provision platforms and resources outside the organization that are used to obtain databases and legal information.

[0367] This invention is a system that automates corporate legal operations and enables communication that takes user emotions into consideration. This system is implemented through the interaction of users, terminals, and servers.

[0368] User: Users input business information through their terminals. This includes data such as contract terms, names of stakeholders, and project outlines. The information entered by the user is analyzed in real time by an emotion engine, which recognizes the user's emotional state.

[0369] Terminal: The terminal converts user input into digital information and transmits that information to the server via a communication interface. This conversion typically uses standard keyboard input or voice input technology.

[0370] Server: The server analyzes the received business information using a natural language processing engine (e.g., Google Cloud Natural Language API or spaCy). This analysis extracts important features from the text. Based on this, it references databases and external information sources to obtain relevant legal information and risk mitigation measures. In parallel, an emotion engine (e.g., IBM Watson or Microsoft Azure's Sentiment Analysis API) analyzes the user's emotional state and generates information that meets their emotional needs.

[0371] Generative AI models (e.g., OpenAI's GPT series) automatically create contracts and communications based on analyzed information and sentiment data, formatting the documents using formal templates. These documents are then sent to the terminal and presented visually to the user.

[0372] Specific example: Consider a scenario where a user is feeling anxious about a new overseas transaction when drafting a contract. The system detects this anxiety using an emotion engine, and the server uses a generation AI model to automatically generate a contract that includes reassuring language. The user can then review the generated document in their browser and confidently proceed to the next step. An example of a prompt generated by this system might be: "Please generate a contract for a new project that includes reassuring language to alleviate anxiety. The contract concerns an acquisition, and should have a reassuring tone."

[0373] Thus, the present invention combines the efficiency of legal work with emotion-based adjustments, making it possible to provide a more user-friendly legal environment.

[0374] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0375] Step 1:

[0376] The user uses a terminal to input the necessary business information for the new project contract. This information includes contract terms, project overview, and names of stakeholders. The input is converted into a digital format via keyboard or voice input.

[0377] Step 2:

[0378] The terminal transmits business information in digital format, obtained from the user, to the server via a communication interface. In this process, the input data is packaged in an appropriate format and sent to the server over the network.

[0379] Step 3:

[0380] The server uses a natural language processing engine to analyze the received business information. First, it tokenizes the text data and extracts important features and keywords from it. The extracted results become input for querying legal information in the database.

[0381] Step 4:

[0382] In parallel, the server uses an emotion engine to analyze the user's emotional state. It calculates an emotion score from the text and recognizes emotional tones such as positive, negative, and neutral. The output serves as the basis for tone adjustment in subsequent document generation.

[0383] Step 5:

[0384] The server combines the output of natural language processing (keywords and features) with the results of sentiment analysis, and retrieves relevant legal information by referencing databases and external sources. This identifies the latest legal information and risk mitigation measures necessary for the user's business.

[0385] Step 6:

[0386] The server uses a generative AI model to automatically generate contracts and communications based on analysis results and sentiment data. The model adjusts the tone and expression of the documents according to the sentiment information and maintains document consistency using official templates. The generated documents are formatted in electronic format.

[0387] Step 7:

[0388] The server sends the generated documents and legal information to the terminal. The terminal visually displays the received data to the user. The user can review the displayed documents and, if necessary, edit them or send them to relevant parties.

[0389] Step 8:

[0390] The server generates feedback based on overall system usage and sentiment analysis to improve operational efficiency and optimize the user experience. It analyzes user sentiment data and trends related to work content to derive improvement measures that will be useful in the future.

[0391] (Application Example 2)

[0392] 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."

[0393] In online transactions and electronic payments, users often experience anxiety and doubt, which can hinder their purchasing decisions. Furthermore, processes such as obtaining information on legal compliance and risk management, and generating documentation, often lack sufficient support to address users' emotional needs. Therefore, improving the user experience and operational efficiency are key challenges.

[0394] 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.

[0395] In this invention, the server includes means for analyzing input business information using natural language processing and extracting keywords and concepts; means for searching for relevant laws and risk countermeasures from a database based on the extracted information and coordinating with an external database to obtain the latest legal information; and emotion recognition means for analyzing the user's emotions and providing communication means corresponding to those emotions. This enables the provision of appropriate communication that takes the user's emotions into consideration, as well as the rapid acquisition of legal information and the streamlining of business procedures.

[0396] "Entered business information" refers to the business-related data and information that users provide to the system.

[0397] "Natural language processing" is a technology that uses computers to understand, appropriately analyze, and process the language that humans use on a daily basis.

[0398] "Keywords and concepts" are important words or concepts with specific meanings or themes that are extracted from the input information.

[0399] A "database" is a collection of data that systematically stores information and allows for searching and updating.

[0400] "Legal regulations and risk response measures" refer to information regarding laws and regulations, as well as measures to prevent and manage the occurrence of risks.

[0401] An "external database" is another database that exists outside the system and can be linked to.

[0402] "Emotion recognition means" refers to technologies and methods for detecting and analyzing emotions from user input information.

[0403] "Communication methods" refer to techniques and devices used for transmitting and communicating information with users.

[0404] "Generative artificial intelligence technology" is a technology that uses machine learning and advanced algorithms to enable humans to think and make judgments similar to those of humans.

[0405] This invention involves a system that receives business information provided by the user via a user interface, converts that information into a digital format, and transmits it to a server. The server analyzes the input information using a natural language processing engine and extracts keywords and concepts. Software such as the Google Cloud Natural Language API can be used for this natural language processing.

[0406] The extracted information is cross-referenced with a database to search for relevant laws and risk mitigation measures. The server quickly obtains the latest legal information by linking with an external database. A standard database management system is used for this data linkage.

[0407] Next, the server uses an emotion recognition engine to understand the user's emotional state and executes commands based on those emotions. It utilizes generative artificial intelligence technology (generative AI models) to adjust the tone and content of the communication generated according to the user's emotions. OpenAI's GPT-3 is a suitable generative AI model for this purpose.

[0408] As a concrete example, if a user feels uneasy during an electronic payment, the system could generate an automated response to reassure them that "this transaction is securely protected." In this case, one possible example is to input the text "Suggest a reassuring message when the user feels uneasy" as a prompt into the AI ​​model.

[0409] Ultimately, the generated documents and adapted response messages are visually displayed on the terminal, allowing the user to review them and proceed with their work. Furthermore, the user can automatically send the results to relevant departments and stakeholders, ensuring smooth workflow.

[0410] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0411] Step 1:

[0412] Users input business information via a terminal. This input information is in natural language text format. The input information is converted into a digital format and sent to the server. This conversion makes the information processable as digital data.

[0413] Step 2:

[0414] The server uses a natural language processing engine to analyze the input information. It syntactically parses the received text data and extracts relevant keywords and concepts. The output obtained from the analysis is a list of important themes and phrases contained in the text.

[0415] Step 3:

[0416] The server queries a database based on the extracted keywords. This query retrieves information on relevant laws and risk mitigation measures. If necessary, it also communicates with external databases to obtain the latest legal information. As a result, a set of legal information is output.

[0417] Step 4:

[0418] To analyze user emotions, the server uses an emotion recognition engine. It estimates the user's emotional state from the input information. Using keywords as input, an emotion score is output. This score quantitatively represents the user's emotions.

[0419] Step 5:

[0420] The server automatically generates appropriate communication content using a generative AI model. It takes a prompt as input and outputs a response message with a tone and content that corresponds to the user's emotions. This model adjusts the text based on an emotion score.

[0421] Step 6:

[0422] The generated response message and document are returned to the terminal and displayed visually. The user can review this and proceed with their work based on its content. The displayed information can be downloaded or sent via email.

[0423] Step 7:

[0424] If necessary, the server automatically sends generated documents and messages to the relevant departments and individuals. This function allows for the forwarding of outputted documents via email or internal communication systems, thereby facilitating efficient information sharing.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] [Third Embodiment]

[0429] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0430] 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.

[0431] 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).

[0432] 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.

[0433] 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.

[0434] 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).

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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".

[0441] This invention is an automated system for corporate legal operations, which begins operating when a user inputs information related to the work via a terminal. The system is primarily composed of a generative AI model and a natural language processing engine that run on a server. Specific embodiments of this invention are described below.

[0442] The user inputs task details, such as a request to create a new contract, in natural language through the terminal's user interface. The terminal converts this information into a digital format and sends it to the server via the network.

[0443] The server analyzes the received business information using a natural language processing engine to extract keywords and important concepts. This analysis provides the server with the basic information needed to identify which laws and regulations are relevant.

[0444] Next, the server uses RAG (Retrieval-Augmented Generation) technology to search for relevant laws and risk mitigation measures from internal databases and external legal databases. In this step, the server can always obtain the latest legal information, including information on legal revisions.

[0445] Subsequently, the server automatically generates the necessary documents and communications using a generation AI model based on the extracted information and relevant laws and regulations. Document templates are used in this process to maintain consistency and formality.

[0446] The generated documents and search results are sent from the server to the terminal and presented visually to the user. This allows the user to easily check the necessary information and send emails to relevant parties or download generated documents with a single click from their terminal.

[0447] As a concrete example, consider a case where a user initiates a new international trade contract. Once the user enters the contract details, the server searches relevant international trade laws and generates the necessary contract template. In addition, it also presents risk mitigation measures regarding customs duties and compliance. Based on this information, the user can proceed with the contract process quickly and accurately.

[0448] This automated system allows users engaged in legal work to perform their duties efficiently and safely, even if they lack sufficient specialized knowledge.

[0449] The following describes the processing flow.

[0450] Step 1:

[0451] Users input work-related information into an input interface on their terminal using natural language. For example, they might input transaction terms and partner information necessary for creating a contract.

[0452] Step 2:

[0453] The terminal converts the input information into a digital format and sends it to the server over the network. During this process, the format is adjusted to ensure accurate data transfer.

[0454] Step 3:

[0455] The server activates a natural language processing engine and analyzes the received information. Specifically, it extracts important keywords and phrases and uses them to identify the entered business content.

[0456] Step 4:

[0457] The server uses RAG technology to search for relevant laws and risk mitigation measures from internal and external legal databases. At this stage, search results, including the latest legal amendments, are retrieved.

[0458] Step 5:

[0459] The server automatically generates necessary contracts and communications based on detected legal information, utilizing an AI model. This process incorporates document templates to maintain official formatting.

[0460] Step 6:

[0461] The generated documents and search results are sent from the server to the terminal. The terminal visually displays the information to the user, allowing the user to review the content.

[0462] Step 7:

[0463] Users can send or download generated documents via email through their devices. Workflows aligned with internal approval processes are also executed as needed.

[0464] (Example 1)

[0465] 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."

[0466] In legal work, there is a challenge in that users, without specialized knowledge, find it difficult to quickly and accurately grasp relevant laws and risk management methods and to create the necessary documents and communications. Furthermore, while it is necessary to constantly acquire the latest regulatory information and promptly share appropriate information with relevant departments and stakeholders, traditional methods make it difficult to do this efficiently.

[0467] 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.

[0468] In this invention, the server includes means for analyzing input business information using a processing engine and extracting key points and important concepts; means for obtaining relevant rules and risk response methods from information sources based on the extracted information, and for collecting the latest rule information in cooperation with publicly available information sources; and means for automatically creating necessary documents and communication content using a model based on the acquired information, and for using a format to maintain the integrity and fairness of the documents. As a result, users can efficiently and safely perform legal work even without specialized knowledge, and quickly share the generated information with relevant departments and stakeholders.

[0469] "Business information" refers to a series of pieces of information related to legal work, including manually entered data and digital data provided by the user.

[0470] A "processing engine" refers to software or algorithms that analyze input information and extract necessary keywords and important concepts.

[0471] "Key points and important concepts" refer to keywords and phrases that are particularly important within business information, and are fundamental information for identifying laws and regulations and risk response methods.

[0472] "Information sources" refer to internal or external databases or repositories that provide relevant laws and regulations and risk management methods.

[0473] "Rules" refer to relevant laws, regulations, or guidelines that indicate the standards that a company must adhere to in its legal operations.

[0474] "Risk management methods" refer to appropriate countermeasures for potential problems and risks that may arise in the course of business operations.

[0475] "Public sources" refer to means of accessing the latest laws and related information provided on the internet and other external information networks.

[0476] A "generating model" refers to a computational model or algorithm used to automatically create documents or communication content based on given data.

[0477] "Consistency" refers to a state in which generated documents and communications consistently represent accurate information.

[0478] "Fairness" refers to the fact that the generated documents and communications are formal and legally correct.

[0479] This invention is a system for automating corporate legal operations, in which users, terminals, and servers work together. The system primarily utilizes a natural language processing engine and generative AI models, which are described in detail below.

[0480] Users input work-related information in natural language through the terminal's user interface. For example, they might input something like, "I want to create a new contract." This information is then converted into a digital format by the terminal.

[0481] The terminal converts the input natural language information into structured data and sends it to the server via the communication network. This provides the server with the foundation for efficiently processing the information.

[0482] The server analyzes received business information using a natural language processing engine to extract keywords and important concepts. This analysis provides foundational information for identifying relevant laws and risk mitigation measures. Next, the server uses RAG (Retrieval-Augmented Generation) technology to retrieve relevant data, including the latest legal information, from internal databases and external legal information sources. The server then uses a generative AI model to automatically generate necessary documents and communications based on document templates. This ensures that consistent, official documentation is provided.

[0483] The generated documents and search results are sent from the server to the terminal, where the user can visually review them. The user can then email or download the generated documents to relevant parties with a single click.

[0484] As a concrete example, consider a case where a user initiates a contract related to international trade. When the user enters "I would like to discuss a new international trade contract," the server searches for relevant international trade laws and generates a contract template. It also simultaneously presents risk mitigation measures related to customs duties and compliance.

[0485] A concrete example of a prompt message would be, "I am considering a new contract for an international transaction, and I need information on relevant laws and risks. Please also provide a suitable contract template." This system enables users with insufficient specialized knowledge to perform legal tasks quickly and accurately.

[0486] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0487] Step 1:

[0488] Users input work-related information in natural language using the terminal's user interface. For example, they might provide information such as "I want to create a new contract." The terminal receives this information, converts it to a digital format, and prepares it as structured data. This lays the foundation for efficient data processing.

[0489] Step 2:

[0490] The terminal transmits the converted digital format information to the server via the network. The data is transmitted using a secure communication protocol. The input is structured business information, and the output is digital data reaching the server. This allows the system to proceed to the next analysis step.

[0491] Step 3:

[0492] The server analyzes the received information using a natural language processing engine. It extracts keywords and important concepts from the input data and identifies key points relevant to the business. This step involves data processing through morphological and semantic analysis. The output consists of the extracted keywords and concepts.

[0493] Step 4:

[0494] Based on the extracted information, the server uses RAG (Retrieval-Augmented Generation) technology to search for relevant regulations and risk mitigation measures from internal databases and external legal databases. Up-to-date legal information is crucial, and data calculations are performed to ensure constantly updated information is obtained. The output consists of appropriate legal information and risk mitigation measures.

[0495] Step 5:

[0496] The server uses a generative AI model to automatically create necessary documents and communications based on acquired legal information and related risks. It leverages document templates to provide consistent, formal documents. Inputs are legal information and risk mitigation measures, while output is the generated documents. Specific examples include documents such as "proposals based on contract templates."

[0497] Step 6:

[0498] The server sends the generated documents and related information to the terminal. The terminal receives them and displays them visually to the user. The input is the generated data, and the output is the document and result information presented to the user. The user reviews the generated and presented documents and, if necessary, emails them to relevant departments or stakeholders or downloads them.

[0499] (Application Example 1)

[0500] 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."

[0501] In modern electronic money transfer services, regulations change frequently, and service providers must always comply with the latest laws and regulations. Therefore, there is a need to streamline and automate legal operations to reduce compliance risks. Traditional methods require a great deal of time and effort to analyze complex and extensive legal information and prepare documents, so there is a need to provide a system that simplifies this process and allows for quick responses even without specialized knowledge.

[0502] 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.

[0503] This invention includes a server that analyzes input business information using natural language processing means, extracts keywords and concepts, searches for relevant laws and regulations and risk countermeasures from a user data storage device, obtains the latest legal information in cooperation with an external information storage device, automatically generates documents and communication content based on the search results, and maintains document consistency and normality using templates; means for visualizing and displaying the generated documents and search results on an information terminal, and for users to transmit or retrieve them; and means for analyzing user-provided service information, checking relevant financial laws and regulations, and generating necessary contracts and agreements in the field of electronic money transfer services. This enables rapid response to legal regulations and efficient document creation.

[0504] "Business information" refers to detailed information about the services and activities provided by the user, which the system analyzes and uses for legal compliance and document creation.

[0505] "Natural language processing means" refers to a technology or device for extracting keywords and concepts from input natural language data and understanding their meaning.

[0506] A "data storage device" is a database that stores legal information, past business data, and other data, and allows for searching and retrieval when needed.

[0507] An "external information storage device" is a device or system that links with legal databases or third-party databases located outside the company to acquire the latest and most relevant information.

[0508] An "information terminal" is an electronic device such as a computer, smartphone, or tablet used by a user, which can display and operate information from a system.

[0509] A "template" is a predetermined framework or format used to maintain consistency in the form and structure of a document.

[0510] "Electronic fund transfer services" are services that transfer money and assets electronically via the internet, and compliance with laws and regulations is a crucial aspect of this field.

[0511] A "contract" is an official document that records the terms of an agreement exchanged between a service provider and its user.

[0512] A "memorandum of agreement" is a legally binding official document that describes matters agreed upon between the parties involved.

[0513] A "prompt message" is a sentence that prompts the user to take the next action or confirm something, and is used as an instruction or hint from the system.

[0514] The system that realizes this invention is built to streamline the application of legal regulations and the automated document generation process in the modern field of electronic money transfer services. It primarily functions through the interface between a server, terminals, and users.

[0515] The server is primarily built using Python and the Flask framework. For natural language processing, the Hugging Face Transformers library is used to analyze user input in detail. This analysis process extracts important keywords and concepts, and relevant laws and risk mitigation measures are retrieved from the user data storage system and external information storage systems.

[0516] The terminal is built using React Native and provides an interface for users to input information. The user's entered work information is converted to a digital format in real time and sent to the server over the network. The terminal also visually displays the results received from the server, allowing users to review the generated documents and retrieve or send them as needed.

[0517] For example, when a user starts a new subscription service, they enter the service details into the application, and the server checks for relevant laws and regulations and generates the necessary contracts and agreements. Furthermore, the generated documents maintain consistency and normality using templates. Users can review these documents on screen and send them to relevant parties via email.

[0518] A concrete example of a prompt message is: "Please check the necessary laws and regulations to launch a new digital content subscription service and create a terms of service template." This message allows users to intuitively understand what information is needed and enables them to complete their tasks quickly.

[0519] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0520] Step 1:

[0521] The user enters business information through the terminal's user interface. Here, detailed data for a new subscription service is entered. The terminal converts this information into a digital format and sends it to the server as structured data. Data entry and format conversion take place.

[0522] Step 2:

[0523] The server analyzes the received business information. A natural language processing engine handles the analysis, extracting important keywords and concepts from the input data. In this processing step, the transformed data becomes the input for analysis, and the data with extracted keywords and concepts is output. Specifically, Hugging Face's Transformers perform the NLP analysis.

[0524] Step 3:

[0525] The server searches internal databases and external information storage devices based on extracted keywords and concepts to retrieve relevant regulations and risk mitigation measures. Data processing in this step involves generating search queries and querying databases. The output includes the latest regulations and risk information.

[0526] Step 4:

[0527] Using a generative AI model, the server generates relevant legal information and the contracts and agreements required by the user. Templates are used to maintain document consistency and normality. Here, acquired legal information and templates serve as input, and the completed document is output based on them. This process also includes a document generation engine.

[0528] Step 5:

[0529] The server sends the generated document to the user so that it is displayed on the terminal. The user can then view the generated document on their terminal. In this step, visualization processing is performed for data display, and the output is sent to the user. The user can also download the document or send it to relevant parties as needed.

[0530] 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.

[0531] This invention combines an automated system for efficiently supporting corporate legal operations with an emotion engine that recognizes emotions from user input information. In this system, users input business information via a terminal, and a server plays a primary role in efficiently and appropriately processing that information.

[0532] For example, when creating a contract for a new project, the user uses a terminal to input the necessary information. During this input process, the system uses an emotion engine to analyze the user's emotions from the entered text. This process allows the server to understand the user's emotional state and select the most appropriate communication method.

[0533] The server first uses a natural language processing engine to analyze the input business information. Keywords and phrases extracted through this analysis are then searched against internal and external legal databases. The search results include the latest legal information, and based on this, appropriate legal regulations and risk mitigation measures are selected.

[0534] Next, the server automatically generates the necessary documents and communications using a generative AI model based on the output of the emotion engine. The tone and content are adjusted according to the emotion, resulting in better communication for the user. These documents are generated using official templates, maintaining consistency and proper formatting.

[0535] The generated documents and legal information are sent from the server to the terminal and presented visually to the user. The user can review the provided information and, if necessary, automatically send it via email to the relevant departments or individuals. Furthermore, the system analyzes and provides feedback based on the recognized user sentiment information to improve the user experience and further streamline operations.

[0536] As a concrete example, consider a scenario where a user is negotiating a contract while feeling anxious about an international transaction. In this case, the emotion engine detects the user's anxiety in their input, and the server adjusts the wording and content of the document to provide greater reassurance. In this way, the user can proceed with the contract negotiation with greater confidence.

[0537] Thus, this invention not only advances the automation of legal work but also enables flexible responses to emotional needs, providing an environment in which even users who are unsure of their legal knowledge can comfortably perform their duties.

[0538] The following describes the processing flow.

[0539] Step 1:

[0540] Users input business information, specifically contract details and context, into the terminal's input interface using natural language. This information needs to include not only regular business data but also the user's emotional expression.

[0541] Step 2:

[0542] The terminal converts the input information into a digital format and sends it to the server. During this process, accuracy of the information and optimization of network traffic are ensured.

[0543] Step 3:

[0544] The server uses a natural language processing engine to analyze the received information and extract keywords and phrases relevant to the business. This is a preparatory step for determining the scope of application of regulations.

[0545] Step 4:

[0546] The server drives an emotion engine to analyze the user's emotions. From the input natural language data, it identifies emotional states such as reassurance, anxiety, and impatience.

[0547] Step 5:

[0548] The server utilizes RAG technology to search for relevant laws and risk mitigation measures from internal and external databases. This always includes the latest legal information, and its relevance is evaluated based on extracted keywords.

[0549] Step 6:

[0550] Based on the output from the emotion engine and natural language processing engine, the server uses a generative AI model to automatically generate documents and communications that are sensitive to the user's emotions. Document templates are used during generation to ensure a consistent and formal tone.

[0551] Step 7:

[0552] The generated documents and legal information are sent from the server to the terminal and displayed. The user reviews them and checks that the content of the documents is appropriate for their needs.

[0553] Step 8:

[0554] Based on the information provided, users send generated documents and emails to the relevant departments and individuals. If necessary, they can perform the sending action with a single click from their device.

[0555] Step 9:

[0556] After a transaction or process is completed, the server analyzes user sentiment data and provides feedback to improve business processes and enhance the user experience. This feedback is used for continuous system improvement.

[0557] (Example 2)

[0558] 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."

[0559] Corporate legal work requires access to specialized legal information, handling vast amounts of business information, and effective communication with stakeholders. However, traditional legal systems lack mechanisms to comprehensively support these elements. Furthermore, they may fail to consider user emotions, potentially leading to a decline in communication quality. This situation makes it difficult for users with limited legal knowledge to perform their duties smoothly.

[0560] 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.

[0561] In this invention, the server includes means for analyzing input business information using natural language processing and extracting features from the information; means for searching for relevant rule information from a database based on the extracted information and linking data with external information sources; and means for recognizing the emotional state from the input information using an emotion engine that analyzes the user's emotions. This enables the acquisition of integrated legal information and communication that takes the user's emotions into consideration.

[0562] "Business information" refers to information necessary for an organization to carry out its operations, such as contract terms, names of stakeholders, and project outlines.

[0563] "Natural language processing" is a technology that enables computers to understand and process human language, and includes methods for extracting meaning and features from text data.

[0564] An "emotion engine" refers to a technology that analyzes a user's emotional state from input text data and recognizes emotional tones such as positive, negative, and neutral.

[0565] A "generative AI model" refers to artificial intelligence technology that learns from large amounts of data and automatically generates new text and information.

[0566] A "template" refers to a predefined format or style used to maintain consistency and formality in document creation.

[0567] A "communication interface" refers to the technology and devices used to convert user input data into a digital format and transmit it to a server via a network.

[0568] "External information sources" refer to information provision platforms and resources outside the organization that are used to obtain databases and legal information.

[0569] This invention is a system that automates corporate legal operations and enables communication that takes user emotions into consideration. This system is implemented through the interaction of users, terminals, and servers.

[0570] User: Users input business information through their terminals. This includes data such as contract terms, names of stakeholders, and project outlines. The information entered by the user is analyzed in real time by an emotion engine, which recognizes the user's emotional state.

[0571] Terminal: The terminal converts user input into digital information and transmits that information to the server via a communication interface. This conversion typically uses standard keyboard input or voice input technology.

[0572] Server: The server analyzes the received business information using a natural language processing engine (e.g., Google Cloud Natural Language API or spaCy). This analysis extracts important features from the text. Based on this, it references databases and external information sources to obtain relevant legal information and risk mitigation measures. In parallel, an emotion engine (e.g., IBM Watson or Microsoft Azure's Sentiment Analysis API) analyzes the user's emotional state and generates information that meets their emotional needs.

[0573] Generative AI models (e.g., OpenAI's GPT series) automatically create contracts and communications based on analyzed information and sentiment data, formatting the documents using formal templates. These documents are then sent to the terminal and presented visually to the user.

[0574] Specific example: Consider a scenario where a user is feeling anxious about a new overseas transaction when drafting a contract. The system detects this anxiety using an emotion engine, and the server uses a generation AI model to automatically generate a contract that includes reassuring language. The user can then review the generated document in their browser and confidently proceed to the next step. An example of a prompt generated by this system might be: "Please generate a contract for a new project that includes reassuring language to alleviate anxiety. The contract concerns an acquisition, and should have a reassuring tone."

[0575] Thus, the present invention combines the efficiency of legal work with emotion-based adjustments, making it possible to provide a more user-friendly legal environment.

[0576] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0577] Step 1:

[0578] The user uses a terminal to input the necessary business information for the new project contract. This information includes contract terms, project overview, and names of stakeholders. The input is converted into a digital format via keyboard or voice input.

[0579] Step 2:

[0580] The terminal transmits business information in digital format, obtained from the user, to the server via a communication interface. In this process, the input data is packaged in an appropriate format and sent to the server over the network.

[0581] Step 3:

[0582] The server uses a natural language processing engine to analyze the received business information. First, it tokenizes the text data and extracts important features and keywords from it. The extracted results become input for querying legal information in the database.

[0583] Step 4:

[0584] In parallel, the server uses an emotion engine to analyze the user's emotional state. It calculates an emotion score from the text and recognizes emotional tones such as positive, negative, and neutral. The output serves as the basis for tone adjustment in subsequent document generation.

[0585] Step 5:

[0586] The server combines the output of natural language processing (keywords and features) with the results of sentiment analysis, and retrieves relevant legal information by referencing databases and external sources. This identifies the latest legal information and risk mitigation measures necessary for the user's business.

[0587] Step 6:

[0588] The server uses a generative AI model to automatically generate contracts and communications based on analysis results and sentiment data. The model adjusts the tone and expression of the documents according to the sentiment information and maintains document consistency using official templates. The generated documents are formatted in electronic format.

[0589] Step 7:

[0590] The server sends the generated documents and legal information to the terminal. The terminal visually displays the received data to the user. The user can review the displayed documents and, if necessary, edit them or send them to relevant parties.

[0591] Step 8:

[0592] The server generates feedback based on overall system usage and sentiment analysis to improve operational efficiency and optimize the user experience. It analyzes user sentiment data and trends related to work content to derive improvement measures that will be useful in the future.

[0593] (Application Example 2)

[0594] 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."

[0595] In online transactions and electronic payments, users often experience anxiety and doubt, which can hinder their purchasing decisions. Furthermore, processes such as obtaining information on legal compliance and risk management, and generating documentation, often lack sufficient support to address users' emotional needs. Therefore, improving the user experience and operational efficiency are key challenges.

[0596] 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.

[0597] In this invention, the server includes means for analyzing input business information using natural language processing and extracting keywords and concepts; means for searching for relevant laws and risk countermeasures from a database based on the extracted information and coordinating with an external database to obtain the latest legal information; and emotion recognition means for analyzing the user's emotions and providing communication means corresponding to those emotions. This enables the provision of appropriate communication that takes the user's emotions into consideration, as well as the rapid acquisition of legal information and the streamlining of business procedures.

[0598] "Entered business information" refers to the business-related data and information that users provide to the system.

[0599] "Natural language processing" is a technology that uses computers to understand, appropriately analyze, and process the language that humans use on a daily basis.

[0600] "Keywords and concepts" are important words or concepts with specific meanings or themes that are extracted from the input information.

[0601] A "database" is a collection of data that systematically stores information and allows for searching and updating.

[0602] "Legal regulations and risk response measures" refer to information regarding laws and regulations, as well as measures to prevent and manage the occurrence of risks.

[0603] An "external database" is another database that exists outside the system and can be linked to.

[0604] "Emotion recognition means" refers to technologies and methods for detecting and analyzing emotions from user input information.

[0605] "Communication methods" refer to techniques and devices used for transmitting and communicating information with users.

[0606] "Generative artificial intelligence technology" is a technology that uses machine learning and advanced algorithms to enable humans to think and make judgments similar to those of humans.

[0607] This invention involves a system that receives business information provided by the user via a user interface, converts that information into a digital format, and transmits it to a server. The server analyzes the input information using a natural language processing engine and extracts keywords and concepts. Software such as the Google Cloud Natural Language API can be used for this natural language processing.

[0608] The extracted information is cross-referenced with a database to search for relevant laws and risk mitigation measures. The server quickly obtains the latest legal information by linking with an external database. A standard database management system is used for this data linkage.

[0609] Next, the server uses an emotion recognition engine to understand the user's emotional state and executes commands based on those emotions. It utilizes generative artificial intelligence technology (generative AI models) to adjust the tone and content of the communication generated according to the user's emotions. OpenAI's GPT-3 is a suitable generative AI model for this purpose.

[0610] As a concrete example, if a user feels uneasy during an electronic payment, the system could generate an automated response to reassure them that "this transaction is securely protected." In this case, one possible example is to input the text "Suggest a reassuring message when the user feels uneasy" as a prompt into the AI ​​model.

[0611] Ultimately, the generated documents and adapted response messages are visually displayed on the terminal, allowing the user to review them and proceed with their work. Furthermore, the user can automatically send the results to relevant departments and stakeholders, ensuring smooth workflow.

[0612] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0613] Step 1:

[0614] Users input business information via a terminal. This input information is in natural language text format. The input information is converted into a digital format and sent to the server. This conversion makes the information processable as digital data.

[0615] Step 2:

[0616] The server uses a natural language processing engine to analyze the input information. It syntactically parses the received text data and extracts relevant keywords and concepts. The output obtained from the analysis is a list of important themes and phrases contained in the text.

[0617] Step 3:

[0618] The server queries a database based on the extracted keywords. This query retrieves information on relevant laws and risk mitigation measures. If necessary, it also communicates with external databases to obtain the latest legal information. As a result, a set of legal information is output.

[0619] Step 4:

[0620] To analyze user emotions, the server uses an emotion recognition engine. It estimates the user's emotional state from the input information. Using keywords as input, an emotion score is output. This score quantitatively represents the user's emotions.

[0621] Step 5:

[0622] The server automatically generates appropriate communication content using a generative AI model. It takes a prompt as input and outputs a response message with a tone and content that corresponds to the user's emotions. This model adjusts the text based on an emotion score.

[0623] Step 6:

[0624] The generated response message and document are returned to the terminal and displayed visually. The user can review this and proceed with their work based on its content. The displayed information can be downloaded or sent via email.

[0625] Step 7:

[0626] If necessary, the server automatically sends generated documents and messages to the relevant departments and individuals. This function allows for the forwarding of outputted documents via email or internal communication systems, thereby facilitating efficient information sharing.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] [Fourth Embodiment]

[0631] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0632] 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.

[0633] 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).

[0634] 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.

[0635] 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.

[0636] 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).

[0637] 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.

[0638] 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.

[0639] 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.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] 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".

[0644] This invention is an automated system for corporate legal operations, which begins operating when a user inputs information related to the work via a terminal. The system is primarily composed of a generative AI model and a natural language processing engine that run on a server. Specific embodiments of this invention are described below.

[0645] The user inputs task details, such as a request to create a new contract, in natural language through the terminal's user interface. The terminal converts this information into a digital format and sends it to the server via the network.

[0646] The server analyzes the received business information using a natural language processing engine to extract keywords and important concepts. This analysis provides the server with the basic information needed to identify which laws and regulations are relevant.

[0647] Next, the server uses RAG (Retrieval-Augmented Generation) technology to search for relevant laws and risk mitigation measures from internal databases and external legal databases. In this step, the server can always obtain the latest legal information, including information on legal revisions.

[0648] Subsequently, the server automatically generates the necessary documents and communications using a generation AI model based on the extracted information and relevant laws and regulations. Document templates are used in this process to maintain consistency and formality.

[0649] The generated documents and search results are sent from the server to the terminal and presented visually to the user. This allows the user to easily check the necessary information and send emails to relevant parties or download generated documents with a single click from their terminal.

[0650] As a concrete example, consider a case where a user initiates a new international trade contract. Once the user enters the contract details, the server searches relevant international trade laws and generates the necessary contract template. In addition, it also presents risk mitigation measures regarding customs duties and compliance. Based on this information, the user can proceed with the contract process quickly and accurately.

[0651] This automated system allows users engaged in legal work to perform their duties efficiently and safely, even if they lack sufficient specialized knowledge.

[0652] The following describes the processing flow.

[0653] Step 1:

[0654] Users input work-related information into an input interface on their terminal using natural language. For example, they might input transaction terms and partner information necessary for creating a contract.

[0655] Step 2:

[0656] The terminal converts the input information into a digital format and sends it to the server over the network. During this process, the format is adjusted to ensure accurate data transfer.

[0657] Step 3:

[0658] The server activates a natural language processing engine and analyzes the received information. Specifically, it extracts important keywords and phrases and uses them to identify the entered business content.

[0659] Step 4:

[0660] The server uses RAG technology to search for relevant laws and risk mitigation measures from internal and external legal databases. At this stage, search results, including the latest legal amendments, are retrieved.

[0661] Step 5:

[0662] The server automatically generates necessary contracts and communications based on detected legal information, utilizing an AI model. This process incorporates document templates to maintain official formatting.

[0663] Step 6:

[0664] The generated documents and search results are sent from the server to the terminal. The terminal visually displays the information to the user, allowing the user to review the content.

[0665] Step 7:

[0666] Users can send or download generated documents via email through their devices. Workflows aligned with internal approval processes are also executed as needed.

[0667] (Example 1)

[0668] 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".

[0669] In legal work, there is a challenge in that users, without specialized knowledge, find it difficult to quickly and accurately grasp relevant laws and risk management methods and to create the necessary documents and communications. Furthermore, while it is necessary to constantly acquire the latest regulatory information and promptly share appropriate information with relevant departments and stakeholders, traditional methods make it difficult to do this efficiently.

[0670] 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.

[0671] In this invention, the server includes means for analyzing input business information using a processing engine and extracting key points and important concepts; means for obtaining relevant rules and risk response methods from information sources based on the extracted information, and for collecting the latest rule information in cooperation with publicly available information sources; and means for automatically creating necessary documents and communication content using a model based on the acquired information, and for using a format to maintain the integrity and fairness of the documents. As a result, users can efficiently and safely perform legal work even without specialized knowledge, and quickly share the generated information with relevant departments and stakeholders.

[0672] "Business information" refers to a series of pieces of information related to legal work, including manually entered data and digital data provided by the user.

[0673] A "processing engine" refers to software or algorithms that analyze input information and extract necessary keywords and important concepts.

[0674] "Key points and important concepts" refer to keywords and phrases that are particularly important within business information, and are fundamental information for identifying laws and regulations and risk response methods.

[0675] "Information sources" refer to internal or external databases or repositories that provide relevant laws and regulations and risk management methods.

[0676] "Rules" refer to relevant laws, regulations, or guidelines that indicate the standards that a company must adhere to in its legal operations.

[0677] "Risk management methods" refer to appropriate countermeasures for potential problems and risks that may arise in the course of business operations.

[0678] "Public sources" refer to means of accessing the latest laws and related information provided on the internet and other external information networks.

[0679] A "generating model" refers to a computational model or algorithm used to automatically create documents or communication content based on given data.

[0680] "Consistency" refers to a state in which generated documents and communications consistently represent accurate information.

[0681] "Fairness" refers to the fact that the generated documents and communications are formal and legally correct.

[0682] This invention is a system for automating corporate legal operations, in which users, terminals, and servers work together. The system primarily utilizes a natural language processing engine and generative AI models, which are described in detail below.

[0683] Users input work-related information in natural language through the terminal's user interface. For example, they might input something like, "I want to create a new contract." This information is then converted into a digital format by the terminal.

[0684] The terminal converts the input natural language information into structured data and sends it to the server via the communication network. This provides the server with the foundation for efficiently processing the information.

[0685] The server analyzes received business information using a natural language processing engine to extract keywords and important concepts. This analysis provides foundational information for identifying relevant laws and risk mitigation measures. Next, the server uses RAG (Retrieval-Augmented Generation) technology to retrieve relevant data, including the latest legal information, from internal databases and external legal information sources. The server then uses a generative AI model to automatically generate necessary documents and communications based on document templates. This ensures that consistent, official documentation is provided.

[0686] The generated documents and search results are sent from the server to the terminal, where the user can visually review them. The user can then email or download the generated documents to relevant parties with a single click.

[0687] As a concrete example, consider a case where a user initiates a contract related to international trade. When the user enters "I would like to discuss a new international trade contract," the server searches for relevant international trade laws and generates a contract template. It also simultaneously presents risk mitigation measures related to customs duties and compliance.

[0688] A concrete example of a prompt message would be, "I am considering a new contract for an international transaction, and I need information on relevant laws and risks. Please also provide a suitable contract template." This system enables users with insufficient specialized knowledge to perform legal tasks quickly and accurately.

[0689] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0690] Step 1:

[0691] Users input work-related information in natural language using the terminal's user interface. For example, they might provide information such as "I want to create a new contract." The terminal receives this information, converts it to a digital format, and prepares it as structured data. This lays the foundation for efficient data processing.

[0692] Step 2:

[0693] The terminal transmits the converted digital format information to the server via the network. The data is transmitted using a secure communication protocol. The input is structured business information, and the output is digital data reaching the server. This allows the system to proceed to the next analysis step.

[0694] Step 3:

[0695] The server analyzes the received information using a natural language processing engine. It extracts keywords and important concepts from the input data and identifies key points relevant to the business. This step involves data processing through morphological and semantic analysis. The output consists of the extracted keywords and concepts.

[0696] Step 4:

[0697] Based on the extracted information, the server uses RAG (Retrieval-Augmented Generation) technology to search for relevant regulations and risk mitigation measures from internal databases and external legal databases. Up-to-date legal information is crucial, and data calculations are performed to ensure constantly updated information is obtained. The output consists of appropriate legal information and risk mitigation measures.

[0698] Step 5:

[0699] The server uses a generative AI model to automatically create necessary documents and communications based on acquired legal information and related risks. It leverages document templates to provide consistent, formal documents. Inputs are legal information and risk mitigation measures, while output is the generated documents. Specific examples include documents such as "proposals based on contract templates."

[0700] Step 6:

[0701] The server sends the generated documents and related information to the terminal. The terminal receives them and displays them visually to the user. The input is the generated data, and the output is the document and result information presented to the user. The user reviews the generated and presented documents and, if necessary, emails them to relevant departments or stakeholders or downloads them.

[0702] (Application Example 1)

[0703] 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".

[0704] In modern electronic money transfer services, regulations change frequently, and service providers must always comply with the latest laws and regulations. Therefore, there is a need to streamline and automate legal operations to reduce compliance risks. Traditional methods require a great deal of time and effort to analyze complex and extensive legal information and prepare documents, so there is a need to provide a system that simplifies this process and allows for quick responses even without specialized knowledge.

[0705] 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.

[0706] This invention includes a server that analyzes input business information using natural language processing means, extracts keywords and concepts, searches for relevant laws and regulations and risk countermeasures from a user data storage device, obtains the latest legal information in cooperation with an external information storage device, automatically generates documents and communication content based on the search results, and maintains document consistency and normality using templates; means for visualizing and displaying the generated documents and search results on an information terminal, and for users to transmit or retrieve them; and means for analyzing user-provided service information, checking relevant financial laws and regulations, and generating necessary contracts and agreements in the field of electronic money transfer services. This enables rapid response to legal regulations and efficient document creation.

[0707] "Business information" refers to detailed information about the services and activities provided by the user, which the system analyzes and uses for legal compliance and document creation.

[0708] "Natural language processing means" refers to a technology or device for extracting keywords and concepts from input natural language data and understanding their meaning.

[0709] A "data storage device" is a database that stores legal information, past business data, and other data, and allows for searching and retrieval when needed.

[0710] An "external information storage device" is a device or system that links with legal databases or third-party databases located outside the company to acquire the latest and most relevant information.

[0711] An "information terminal" is an electronic device such as a computer, smartphone, or tablet used by a user, which can display and operate information from a system.

[0712] A "template" is a predetermined framework or format used to maintain consistency in the form and structure of a document.

[0713] "Electronic fund transfer services" are services that transfer money and assets electronically via the internet, and compliance with laws and regulations is a crucial aspect of this field.

[0714] A "contract" is an official document that records the terms of an agreement exchanged between a service provider and its user.

[0715] A "memorandum of agreement" is a legally binding official document that describes matters agreed upon between the parties involved.

[0716] A "prompt message" is a sentence that prompts the user to take the next action or confirm something, and is used as an instruction or hint from the system.

[0717] The system that realizes this invention is built to streamline the application of legal regulations and the automated document generation process in the modern field of electronic money transfer services. It primarily functions through the interface between a server, terminals, and users.

[0718] The server is primarily built using Python and the Flask framework. For natural language processing, the Hugging Face Transformers library is used to analyze user input in detail. This analysis process extracts important keywords and concepts, and relevant laws and risk mitigation measures are retrieved from the user data storage system and external information storage systems.

[0719] The terminal is built using React Native and provides an interface for users to input information. The user's entered work information is converted to a digital format in real time and sent to the server over the network. The terminal also visually displays the results received from the server, allowing users to review the generated documents and retrieve or send them as needed.

[0720] For example, when a user starts a new subscription service, they enter the service details into the application, and the server checks for relevant laws and regulations and generates the necessary contracts and agreements. Furthermore, the generated documents maintain consistency and normality using templates. Users can review these documents on screen and send them to relevant parties via email.

[0721] A concrete example of a prompt message is: "Please check the necessary laws and regulations to launch a new digital content subscription service and create a terms of service template." This message allows users to intuitively understand what information is needed and enables them to complete their tasks quickly.

[0722] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0723] Step 1:

[0724] The user enters business information through the terminal's user interface. Here, detailed data for a new subscription service is entered. The terminal converts this information into a digital format and sends it to the server as structured data. Data entry and format conversion take place.

[0725] Step 2:

[0726] The server analyzes the received business information. A natural language processing engine handles the analysis, extracting important keywords and concepts from the input data. In this processing step, the transformed data becomes the input for analysis, and the data with extracted keywords and concepts is output. Specifically, Hugging Face's Transformers perform the NLP analysis.

[0727] Step 3:

[0728] The server searches internal databases and external information storage devices based on extracted keywords and concepts to retrieve relevant regulations and risk mitigation measures. Data processing in this step involves generating search queries and querying databases. The output includes the latest regulations and risk information.

[0729] Step 4:

[0730] Using a generative AI model, the server generates relevant legal information and the contracts and agreements required by the user. Templates are used to maintain document consistency and normality. Here, acquired legal information and templates serve as input, and the completed document is output based on them. This process also includes a document generation engine.

[0731] Step 5:

[0732] The server sends the generated document to the user so that it is displayed on the terminal. The user can then view the generated document on their terminal. In this step, visualization processing is performed for data display, and the output is sent to the user. The user can also download the document or send it to relevant parties as needed.

[0733] 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.

[0734] This invention combines an automated system for efficiently supporting corporate legal operations with an emotion engine that recognizes emotions from user input information. In this system, users input business information via a terminal, and a server plays a primary role in efficiently and appropriately processing that information.

[0735] For example, when creating a contract for a new project, the user uses a terminal to input the necessary information. During this input process, the system uses an emotion engine to analyze the user's emotions from the entered text. This process allows the server to understand the user's emotional state and select the most appropriate communication method.

[0736] The server first uses a natural language processing engine to analyze the input business information. Keywords and phrases extracted through this analysis are then searched against internal and external legal databases. The search results include the latest legal information, and based on this, appropriate legal regulations and risk mitigation measures are selected.

[0737] Next, the server automatically generates the necessary documents and communications using a generative AI model based on the output of the emotion engine. The tone and content are adjusted according to the emotion, resulting in better communication for the user. These documents are generated using official templates, maintaining consistency and proper formatting.

[0738] The generated documents and legal information are sent from the server to the terminal and presented visually to the user. The user can review the provided information and, if necessary, automatically send it via email to the relevant departments or individuals. Furthermore, the system analyzes and provides feedback based on the recognized user sentiment information to improve the user experience and further streamline operations.

[0739] As a concrete example, consider a scenario where a user is negotiating a contract while feeling anxious about an international transaction. In this case, the emotion engine detects the user's anxiety in their input, and the server adjusts the wording and content of the document to provide greater reassurance. In this way, the user can proceed with the contract negotiation with greater confidence.

[0740] Thus, this invention not only advances the automation of legal work but also enables flexible responses to emotional needs, providing an environment in which even users who are unsure of their legal knowledge can comfortably perform their duties.

[0741] The following describes the processing flow.

[0742] Step 1:

[0743] Users input business information, specifically contract details and context, into the terminal's input interface using natural language. This information needs to include not only regular business data but also the user's emotional expression.

[0744] Step 2:

[0745] The terminal converts the input information into a digital format and sends it to the server. During this process, accuracy of the information and optimization of network traffic are ensured.

[0746] Step 3:

[0747] The server uses a natural language processing engine to analyze the received information and extract keywords and phrases relevant to the business. This is a preparatory step for determining the scope of application of regulations.

[0748] Step 4:

[0749] The server drives an emotion engine to analyze the user's emotions. From the input natural language data, it identifies emotional states such as reassurance, anxiety, and impatience.

[0750] Step 5:

[0751] The server utilizes RAG technology to search for relevant laws and risk mitigation measures from internal and external databases. This always includes the latest legal information, and its relevance is evaluated based on extracted keywords.

[0752] Step 6:

[0753] Based on the output from the emotion engine and natural language processing engine, the server uses a generative AI model to automatically generate documents and communications that are sensitive to the user's emotions. Document templates are used during generation to ensure a consistent and formal tone.

[0754] Step 7:

[0755] The generated documents and legal information are sent from the server to the terminal and displayed. The user reviews them and checks that the content of the documents is appropriate for their needs.

[0756] Step 8:

[0757] Based on the information provided, users send generated documents and emails to the relevant departments and individuals. If necessary, they can perform the sending action with a single click from their device.

[0758] Step 9:

[0759] After a transaction or process is completed, the server analyzes user sentiment data and provides feedback to improve business processes and enhance the user experience. This feedback is used for continuous system improvement.

[0760] (Example 2)

[0761] 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".

[0762] Corporate legal work requires access to specialized legal information, handling vast amounts of business information, and effective communication with stakeholders. However, traditional legal systems lack mechanisms to comprehensively support these elements. Furthermore, they may fail to consider user emotions, potentially leading to a decline in communication quality. This situation makes it difficult for users with limited legal knowledge to perform their duties smoothly.

[0763] 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.

[0764] In this invention, the server includes means for analyzing input business information using natural language processing and extracting features from the information; means for searching for relevant rule information from a database based on the extracted information and linking data with external information sources; and means for recognizing the emotional state from the input information using an emotion engine that analyzes the user's emotions. This enables the acquisition of integrated legal information and communication that takes the user's emotions into consideration.

[0765] "Business information" refers to information necessary for an organization to carry out its operations, such as contract terms, names of stakeholders, and project outlines.

[0766] "Natural language processing" is a technology that enables computers to understand and process human language, and includes methods for extracting meaning and features from text data.

[0767] An "emotion engine" refers to a technology that analyzes a user's emotional state from input text data and recognizes emotional tones such as positive, negative, and neutral.

[0768] A "generative AI model" refers to artificial intelligence technology that learns from large amounts of data and automatically generates new text and information.

[0769] A "template" refers to a predefined format or style used to maintain consistency and formality in document creation.

[0770] A "communication interface" refers to the technology and devices used to convert user input data into a digital format and transmit it to a server via a network.

[0771] "External information sources" refer to information provision platforms and resources outside the organization that are used to obtain databases and legal information.

[0772] This invention is a system that automates corporate legal operations and enables communication that takes user emotions into consideration. This system is implemented through the interaction of users, terminals, and servers.

[0773] User: Users input business information through their terminals. This includes data such as contract terms, names of stakeholders, and project outlines. The information entered by the user is analyzed in real time by an emotion engine, which recognizes the user's emotional state.

[0774] Terminal: The terminal converts user input into digital information and transmits that information to the server via a communication interface. This conversion typically uses standard keyboard input or voice input technology.

[0775] Server: The server analyzes the received business information using a natural language processing engine (e.g., Google Cloud Natural Language API or spaCy). This analysis extracts important features from the text. Based on this, it references databases and external information sources to obtain relevant legal information and risk mitigation measures. In parallel, an emotion engine (e.g., IBM Watson or Microsoft Azure's Sentiment Analysis API) analyzes the user's emotional state and generates information that meets their emotional needs.

[0776] Generative AI models (e.g., OpenAI's GPT series) automatically create contracts and communications based on analyzed information and sentiment data, formatting the documents using formal templates. These documents are then sent to the terminal and presented visually to the user.

[0777] Specific example: Consider a scenario where a user is feeling anxious about a new overseas transaction when drafting a contract. The system detects this anxiety using an emotion engine, and the server uses a generation AI model to automatically generate a contract that includes reassuring language. The user can then review the generated document in their browser and confidently proceed to the next step. An example of a prompt generated by this system might be: "Please generate a contract for a new project that includes reassuring language to alleviate anxiety. The contract concerns an acquisition, and should have a reassuring tone."

[0778] Thus, the present invention combines the efficiency of legal work with emotion-based adjustments, making it possible to provide a more user-friendly legal environment.

[0779] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0780] Step 1:

[0781] The user uses a terminal to input the necessary business information for the new project contract. This information includes contract terms, project overview, and names of stakeholders. The input is converted into a digital format via keyboard or voice input.

[0782] Step 2:

[0783] The terminal transmits business information in digital format, obtained from the user, to the server via a communication interface. In this process, the input data is packaged in an appropriate format and sent to the server over the network.

[0784] Step 3:

[0785] The server uses a natural language processing engine to analyze the received business information. First, it tokenizes the text data and extracts important features and keywords from it. The extracted results become input for querying legal information in the database.

[0786] Step 4:

[0787] In parallel, the server uses an emotion engine to analyze the user's emotional state. It calculates an emotion score from the text and recognizes emotional tones such as positive, negative, and neutral. The output serves as the basis for tone adjustment in subsequent document generation.

[0788] Step 5:

[0789] The server combines the output of natural language processing (keywords and features) with the results of sentiment analysis, and retrieves relevant legal information by referencing databases and external sources. This identifies the latest legal information and risk mitigation measures necessary for the user's business.

[0790] Step 6:

[0791] The server uses a generative AI model to automatically generate contracts and communications based on analysis results and sentiment data. The model adjusts the tone and expression of the documents according to the sentiment information and maintains document consistency using official templates. The generated documents are formatted in electronic format.

[0792] Step 7:

[0793] The server sends the generated documents and legal information to the terminal. The terminal visually displays the received data to the user. The user can review the displayed documents and, if necessary, edit them or send them to relevant parties.

[0794] Step 8:

[0795] The server generates feedback based on overall system usage and sentiment analysis to improve operational efficiency and optimize the user experience. It analyzes user sentiment data and trends related to work content to derive improvement measures that will be useful in the future.

[0796] (Application Example 2)

[0797] 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".

[0798] In online transactions and electronic payments, users often experience anxiety and doubt, which can hinder their purchasing decisions. Furthermore, processes such as obtaining information on legal compliance and risk management, and generating documentation, often lack sufficient support to address users' emotional needs. Therefore, improving the user experience and operational efficiency are key challenges.

[0799] 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.

[0800] In this invention, the server includes means for analyzing input business information using natural language processing and extracting keywords and concepts; means for searching for relevant laws and risk countermeasures from a database based on the extracted information and coordinating with an external database to obtain the latest legal information; and emotion recognition means for analyzing the user's emotions and providing communication means corresponding to those emotions. This enables the provision of appropriate communication that takes the user's emotions into consideration, as well as the rapid acquisition of legal information and the streamlining of business procedures.

[0801] "Entered business information" refers to the business-related data and information that users provide to the system.

[0802] "Natural language processing" is a technology that uses computers to understand, appropriately analyze, and process the language that humans use on a daily basis.

[0803] "Keywords and concepts" are important words or concepts with specific meanings or themes that are extracted from the input information.

[0804] A "database" is a collection of data that systematically stores information and allows for searching and updating.

[0805] "Legal regulations and risk response measures" refer to information regarding laws and regulations, as well as measures to prevent and manage the occurrence of risks.

[0806] An "external database" is another database that exists outside the system and can be linked to.

[0807] "Emotion recognition means" refers to technologies and methods for detecting and analyzing emotions from user input information.

[0808] "Communication methods" refer to techniques and devices used for transmitting and communicating information with users.

[0809] "Generative artificial intelligence technology" is a technology that uses machine learning and advanced algorithms to enable humans to think and make judgments similar to those of humans.

[0810] This invention involves a system that receives business information provided by the user via a user interface, converts that information into a digital format, and transmits it to a server. The server analyzes the input information using a natural language processing engine and extracts keywords and concepts. Software such as the Google Cloud Natural Language API can be used for this natural language processing.

[0811] The extracted information is cross-referenced with a database to search for relevant laws and risk mitigation measures. The server quickly obtains the latest legal information by linking with an external database. A standard database management system is used for this data linkage.

[0812] Next, the server uses an emotion recognition engine to understand the user's emotional state and executes commands based on those emotions. It utilizes generative artificial intelligence technology (generative AI models) to adjust the tone and content of the communication generated according to the user's emotions. OpenAI's GPT-3 is a suitable generative AI model for this purpose.

[0813] As a concrete example, if a user feels uneasy during an electronic payment, the system could generate an automated response to reassure them that "this transaction is securely protected." In this case, one possible example is to input the text "Suggest a reassuring message when the user feels uneasy" as a prompt into the AI ​​model.

[0814] Ultimately, the generated documents and adapted response messages are visually displayed on the terminal, allowing the user to review them and proceed with their work. Furthermore, the user can automatically send the results to relevant departments and stakeholders, ensuring smooth workflow.

[0815] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0816] Step 1:

[0817] Users input business information via a terminal. This input information is in natural language text format. The input information is converted into a digital format and sent to the server. This conversion makes the information processable as digital data.

[0818] Step 2:

[0819] The server uses a natural language processing engine to analyze the input information. It syntactically parses the received text data and extracts relevant keywords and concepts. The output obtained from the analysis is a list of important themes and phrases contained in the text.

[0820] Step 3:

[0821] The server queries a database based on the extracted keywords. This query retrieves information on relevant laws and risk mitigation measures. If necessary, it also communicates with external databases to obtain the latest legal information. As a result, a set of legal information is output.

[0822] Step 4:

[0823] To analyze user emotions, the server uses an emotion recognition engine. It estimates the user's emotional state from the input information. Using keywords as input, an emotion score is output. This score quantitatively represents the user's emotions.

[0824] Step 5:

[0825] The server automatically generates appropriate communication content using a generative AI model. It takes a prompt as input and outputs a response message with a tone and content that corresponds to the user's emotions. This model adjusts the text based on an emotion score.

[0826] Step 6:

[0827] The generated response message and document are returned to the terminal and displayed visually. The user can review this and proceed with their work based on its content. The displayed information can be downloaded or sent via email.

[0828] Step 7:

[0829] If necessary, the server automatically sends generated documents and messages to the relevant departments and individuals. This function allows for the forwarding of outputted documents via email or internal communication systems, thereby facilitating efficient information sharing.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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."

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] The following is further disclosed regarding the embodiments described above.

[0852] (Claim 1)

[0853] A means for analyzing input business information using natural language processing and extracting keywords and concepts,

[0854] Based on the extracted information, the system searches for relevant laws and risk mitigation measures from the database and also integrates with external databases to obtain the latest legal information.

[0855] Based on the searched information, the system automatically generates the necessary documents and communications, and utilizes templates to maintain document consistency and formality.

[0856] A system that visualizes and displays generated documents and search results on a terminal, and includes means to enable users to send or download documents.

[0857] (Claim 2)

[0858] The system according to claim 1, further comprising means for converting business information entered by a user into a digital format through an input interface and transmitting it to a server via a communication network.

[0859] (Claim 3)

[0860] The system according to claim 1, further comprising means for automatically sending generated documents and communications to relevant departments and persons.

[0861] "Example 1"

[0862] (Claim 1)

[0863] A means for analyzing input business information using a processing engine and extracting key points and important concepts,

[0864] Based on the extracted information, a means of obtaining relevant rules and risk response methods from the information source, and collecting the latest rule information in cooperation with publicly available information sources,

[0865] Based on the acquired information, a model is used to automatically generate the necessary documents and communication content, and a means of using formatting to maintain the integrity and fairness of the documents is employed.

[0866] A system that includes means for displaying generated documents and retrieval results on a device, and enabling users to share or retrieve documents.

[0867] (Claim 2)

[0868] The system according to claim 1, further comprising means for converting business information entered by a user into an electronic format via an input device and transmitting it to a main unit via a communication network.

[0869] (Claim 3)

[0870] The system according to claim 1, further comprising means for automatically transmitting generated documents and communications to relevant departments and persons.

[0871] "Application Example 1"

[0872] (Claim 1)

[0873] The system analyzes the input business information using natural language processing, extracts keywords and concepts, searches for relevant laws and risk mitigation measures from the data storage device, obtains the latest legal information in conjunction with an external information storage device, automatically generates documents and communication content based on the search results, and maintains document consistency and normality using templates.

[0874] A means by which the generated documents and search results can be visualized and displayed on an information terminal, and transmitted or retrieved by the user,

[0875] A means for converting business information entered by a user into a digital format via a print-to-telegraph conversion means and transmitting it to a processing device via a communication network,

[0876] A means of automatically sending the generated documents and communications to the relevant departments and individuals,

[0877] In the field of electronic money transfer services, a means of analyzing user-provided service information, checking relevant financial laws and regulations, and generating necessary contracts and agreements,

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, further comprising means for assisting a user in performing necessary legal checks and drafting contracts when providing a new subscription service.

[0881] (Claim 3)

[0882] The system according to claim 1, further comprising means for visualizing the generated terms of use and privacy policy and for prompting the user to indicate necessary information using prompt statements, in order to promote legal compliance.

[0883] "Example 2 of combining an emotion engine"

[0884] (Claim 1)

[0885] A means of analyzing input business information using natural language processing and extracting features from the information,

[0886] Based on the extracted information, a means to search for relevant rule information from the database and to link data with external information sources,

[0887] A means of recognizing an emotional state from input information using an emotion engine that analyzes the user's emotions,

[0888] Based on sentiment analysis results and retrieved information, a generative AI model automatically creates documents and communications, and a means of maintaining formality using format templates.

[0889] A system that includes means for displaying generated documents and analysis results on a terminal and enabling the user to send them.

[0890] (Claim 2)

[0891] The system according to claim 1, further comprising means for converting business information entered by a user into digital information via a communication interface and transmitting it to a server via a network.

[0892] (Claim 3)

[0893] The system according to claim 1, further comprising means for automatically sending generated documents and communications to relevant departments and individuals.

[0894] "Application example 2 when combining with an emotional engine"

[0895] (Claim 1)

[0896] A means for analyzing input business information using natural language processing and extracting keywords and concepts,

[0897] Based on the extracted information, the system searches for relevant laws and risk mitigation measures from the database and also integrates with external databases to obtain the latest legal information.

[0898] Based on the searched information, the system automatically generates the necessary documents and communications, and utilizes templates to maintain document consistency and formality.

[0899] A means for visually displaying generated documents and search results on a terminal, and enabling the user to send or download documents,

[0900] An emotion recognition means that analyzes the user's emotions and provides communication methods that correspond to those emotions,

[0901] A means of adjusting the tone and content of communication using generative artificial intelligence technology based on emotional responses,

[0902] A system that includes this.

[0903] (Claim 2)

[0904] The system according to claim 1, further comprising means for converting business information entered by a user into a digital format through an input interface and transmitting it to a server via a communication network.

[0905] (Claim 3)

[0906] The system according to claim 1, further comprising means for automatically sending generated documents and communications to relevant departments and persons. [Explanation of symbols]

[0907] 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. The system analyzes the input business information using natural language processing, extracts keywords and concepts, searches for relevant laws and risk mitigation measures from the data storage device, obtains the latest legal information in conjunction with an external information storage device, automatically generates documents and communication content based on the search results, and maintains document consistency and normality using templates. A means by which the generated documents and search results can be visualized and displayed on an information terminal, and transmitted or retrieved by the user, A means for converting business information entered by a user into a digital format via a print-to-telegraph conversion means and transmitting it to a processing device via a communication network, A means of automatically sending the generated documents and communications to the relevant departments and individuals, In the field of electronic money transfer services, a means of analyzing user-provided service information, checking relevant financial laws and regulations, and generating necessary contracts and agreements, A system that includes this.

2. The system according to claim 1, further comprising means for assisting a user in performing necessary legal checks and drafting contracts when providing a new subscription service.

3. The system according to claim 1, further comprising means for visualizing the generated terms of use and privacy policy and for prompting the user to indicate necessary information using prompt statements, in order to promote legal compliance.

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