system

Abstract

We provide the system. [Solution] A means of providing a user interface for inputting work details, A means of searching for relevant legal information and risk information based on the entered business details, A means of using a generative model that generates suggested content based on the searched information, Means for customizing generated content based on company-specific requirements, A means of sending the final generated content, A system that includes this.

Description

Technical Field

[0005] ,

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in 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] Even when an employee of a company does not have specialized knowledge about laws, it may not be possible to quickly identify relevant laws and regulations and risks. For this reason, during business operations, there may be copyright infringements and legal liabilities due to non-compliance with laws and lack of risk management. Also, it is difficult to respond to changes in legal information, which leads to a problem of reduced business efficiency.

Means for Solving the Problems

[0005] This system provides an interface for inputting business details and includes a means to automatically search for relevant legal and risk information based on the entered information. Furthermore, it can generate suggested content based on information obtained using a generative model and customize it to meet the specific requirements of each company. This allows the system to leverage technology to dynamically acquire information from the latest legal databases, thereby improving the user's work efficiency and mitigating risks.

[0006] A "user interface" is a component that provides a screen or operating environment for users to input work-related information.

[0007] "Legal information" refers to data on laws and regulations related to the business, including legal obligations and compliance requirements.

[0008] "Risk information" refers to data concerning potential legal risks and concerns that may arise in the course of performing business operations.

[0009] A "generative model" is an AI-based algorithm used to automatically generate new content based on given data.

[0010] "RAG technology" is an abbreviation for Retrieval-Augmented Generation, a technology that combines information retrieval and generative models to generate more accurate and relevant information.

[0011] "Customization" refers to the process of adjusting generated content to meet the specific requirements and needs of a company.

[0012] A "template" is a standard document model with a specific format and structure, enabling customization and standardized output. [Brief explanation of the drawing]

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

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

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

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

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

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

[0019] In the following embodiments, a tagged communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

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

[0021] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0034] This invention is a system for streamlining legal affairs within companies. The system automatically searches for relevant legal and risk information when a user inputs their work details, and then provides suggestions to the user through content generation using a generative model. Furthermore, the generated content is customized according to the company's specific requirements and distributed via appropriate transmission methods.

[0035] The user first accesses the user interface using a terminal and specifies the nature of their request. For example, if the user is seeking legal advice regarding copyright issues for a new product, they input the relevant information. The terminal receives this input, performs the necessary preprocessing, and then sends it to the server.

[0036] The server uses RAG technology to search for relevant legal information from databases. This process utilizes internal knowledge systems and up-to-date external legal databases. Based on the retrieved data, a generative model generates content that includes legal risks and recommended actions.

[0037] Next, the server customizes the generated content based on the company's standards. This includes adjusting company policies and industry-specific wording. Once the final adjustments are complete, the terminal displays the submitted content to the user, providing an opportunity for review and correction.

[0038] As a concrete example, when a new software product is released, a user might enter "I want to review the legal procedures associated with the release" into the interface. The server gathers the latest information related to copyright law and sales licenses, and a generative AI model creates a report containing the best course of action. The user reviews this information and makes any necessary manual corrections, after which the server automatically sends the completed email or documents to the relevant departments or legal representatives.

[0039] This significantly simplifies the process of legal compliance in business operations, while minimizing legal risks and improving operational efficiency.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] Users access a dedicated user interface and input the business details that need to be analyzed. Users can freely input specific information, such as legal matters related to the release of a new product.

[0043] Step 2:

[0044] The terminal receives the work details from the user and preprocesses the text data. Specifically, it tokenizes the input text and divides it into words and phrases. It also normalizes the data as needed and converts it into a standard format.

[0045] Step 3:

[0046] The server uses RAG technology to search for relevant legal and risk information from databases based on pre-processed data. The server accesses internal knowledge systems and external legal databases to collect the latest legal information.

[0047] Step 4:

[0048] The server inputs the collected information into a generative model and generates suggested content related to the user's work. At this stage, the generative model is adjusted to include specific advice regarding legal compliance and risk management.

[0049] Step 5:

[0050] The server customizes the generated content to meet the company's needs. A process is carried out to adjust the format and content of output documents and emails based on company policies and industry-specific requirements.

[0051] Step 6:

[0052] The device presents the user with customized generated content. The user reviews this content and makes manual corrections if necessary. A mechanism is provided on the interface to request approval of the content.

[0053] Step 7:

[0054] The server automatically sends the final content, once confirmed by the user, to the designated recipient. The email or document recipient is either pre-specified by the user or automatically determined by the system.

[0055] (Example 1)

[0056] 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."

[0057] Modern business activities require companies to conduct business quickly while complying with a wide range of laws and regulations. However, because legal information is frequently updated and its scope of application is broad, it is difficult to implement appropriate risk countermeasures based on the latest information. As a result, the burden of legal compliance increases, and operational efficiency declines. There is a need for effective solutions to these problems and streamline corporate legal affairs.

[0058] 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.

[0059] In this invention, the server includes means for providing a user screen for inputting business details, means for searching relevant legal and risk information based on the input business details, means for using a generation model to generate suggested content based on the acquired information, means for adjusting the generated content based on organization-specific requirements, and means including an interface for users to review and modify the content. This enables companies to comply with laws and regulations quickly and efficiently, reduces the burden of legal work, and improves operational efficiency.

[0060] The "user screen" refers to the interface used for inputting work details and is used when users provide work-related information to the system.

[0061] "Legal information" refers to information about various laws and regulations, and is data that should be referenced in order to meet the legal requirements related to business operations.

[0062] "Risk information" refers to information about risks that should be recognized in order to prevent violations of laws and regulations related to the performance of business operations.

[0063] A "generative model" is an algorithm or technology that is used to automatically create suggested content based on input information.

[0064] "Organization-specific requirements" refer to the unique standards and rules that a particular company or organization must adhere to, in accordance with its policies and industry characteristics.

[0065] "Means including an interface" means technical means that allow users to review and modify generated content as needed.

[0066] This invention provides a system that streamlines legal affairs for companies. Users begin using the system by inputting their work details using a terminal. The terminal receives input from the user through an interactive screen called a user interface, organizes the information, and sends it to the server.

[0067] The server has the functionality to search for legal and risk information from multiple databases. Utilizing RAG technology, it acquires information from internal knowledge systems and external, up-to-date legal databases. Based on the acquired information, the server uses a generative AI model to generate content that suggests to the user. This suggested content includes risk assessments and recommended actions related to the law.

[0068] Furthermore, the server adjusts the generated content based on specific company-specific policies and requirements. This process reflects the company's unique language and industry characteristics. The adjusted content is then presented to the user via their device, where they can review and modify it as needed.

[0069] As a concrete example, when releasing a new software product, a user might input "I want to check the legal procedures associated with the release" into their terminal. The server collects relevant copyright law and sales license information and generates a report summarizing countermeasures through a generative AI model. Based on this report, the user can proceed smoothly with the new product release procedures while minimizing legal risks.

[0070] This system simplifies the process of legal compliance in business operations, reduces the legal risks faced by companies, and enables efficient business execution.

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] The user accesses the terminal's user interface and inputs specific details about their work. This input is sent to the system as a prompt message. For example, the input might be in the format of, "I would like to confirm the legal procedures regarding the copyright of the new product." The purpose of the input is to accurately identify the relevant information needed for subsequent processing.

[0074] Step 2:

[0075] After receiving input from the user, the terminal performs necessary data formatting and checks. This preprocessing verifies that the input data is in the correct format and content to ensure it can be processed correctly on the server. Once verification is complete, the formatted data is sent to the server. The output is the preprocessed data.

[0076] Step 3:

[0077] The server uses RAG technology to search for legal and risk information based on the received data. Specifically, it accesses internal knowledge systems and external legal databases to quickly collect relevant information. The input for this step is pre-processed user input data, and the output is the retrieved legal and risk information.

[0078] Step 4:

[0079] The server generates suggested content using a generative AI model based on the search results. This generation process automatically creates content that includes risk assessments of relevant laws and recommended actions. The generated content is formatted in a user-friendly manner. The input is the searched legal information, and the output is the generated content.

[0080] Step 5:

[0081] The server customizes the generated content based on the company's specific requirements. Here, the wording is adjusted according to company policies and industry characteristics. The customized content is then ready to be presented as the final version. The input is the generated content, and the output is the adjusted content.

[0082] Step 6:

[0083] The terminal displays the finalized content to the user. The user can review this content and make corrections if necessary. This interface allows the user to verify that the generated information meets their requirements. After approval, the content proceeds to the next stage, regardless of whether corrections have been made. The input is the refined content, and the output is the content that has been reviewed by the user.

[0084] Step 7:

[0085] After the user completes the verification process, the server sends the final content to the relevant department or legal representative. This is done using methods such as email, and is efficiently delivered through an automated process. The final output is the completed content that has been sent.

[0086] (Application Example 1)

[0087] 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."

[0088] Corporate legal compliance requires significant time and effort in identifying legal information and conducting risk assessments, thus demanding efficient processes. Furthermore, compliance actions must conform to company-specific standards, necessitating the customization of generated information. Automating these processes is also crucial for timely and accurate information sharing with legal personnel and relevant departments.

[0089] 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.

[0090] In this invention, the server includes means for providing a human-machine interface for inputting business content, data processing means for retrieving relevant legal and risk information based on the input business content, means for using a generation model that presents legal risk assessment and recommended processing means based on the retrieved information, customization processing means for optimizing the generated content based on organization-specific standards, and transmission processing means for electronically distributing the generated content to relevant parties. This enables more efficient legal response and enhanced risk management.

[0091] A "human-machine interface" is an interaction method that allows users to input work details and enables information exchange between machines and humans.

[0092] "Data processing means" refers to a function that efficiently searches for relevant legal and risk information based on the entered business content and extracts appropriate information.

[0093] A "generative model" is a content generation technology that uses machine learning algorithms to provide legal risk assessments and recommended actions based on the information obtained.

[0094] "Customization processing means" refers to functions that adjust generated content based on organization-specific standards and policies, and handle it in the most optimal way.

[0095] "Transmission processing means" refers to a function for accurately and quickly distributing the finalized content to relevant parties using electronic means.

[0096] The system for implementing this invention is designed to streamline the processing of legal information within a company. Specifically, it is implemented in the following form:

[0097] Users first input their work details using the provided human-machine interface. This interface provides guidelines for inputting work details, assisting in the accurate entry of information related to specific tasks.

[0098] The entered information is transmitted by the terminal to the data processing system. The server uses the data processing system to retrieve relevant legal and risk information from the legal database based on the entered information. This process utilizes RAG technology to dynamically acquire the latest legal information.

[0099] Next, the server passes the acquired legal information to a generating AI model to generate content that presents legal risk assessments and recommended actions. For example, OpenAI's GPT series is used as the generating AI model. The generated content is optimized based on organization-specific policies and adjusted by customization processing mechanisms.

[0100] The finalized content is then electronically distributed to the relevant parties via the server's transmission processing system. This process is efficient and automated, enabling rapid assessment and management of legal risks.

[0101] For example, if a user inputs information such as "I want to check the legal procedures regarding the release of a new product," the server will collect relevant copyright law and sales license information and generate a report that includes appropriate legal risks and actions. After being customized, this report is automatically sent to the legal department.

[0102] An example of a prompt message is, "Assess the legal risks associated with the release of the new product and generate recommended actions."

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The user inputs work details using a human-machine interface. This user input consists of specific legal questions and issues related to the work. Based on this input, the terminal prepares the work details as initial data and prepares it for transmission to the server.

[0106] Step 2:

[0107] The terminal transmits the work details entered by the user to the server. The server receives the data and uses data processing tools to search the legal database for laws and risk information related to the entered information. Based on the input (work details), it generates a database query and extracts the relevant information.

[0108] Step 3:

[0109] The server passes legal information retrieved from the database to the generating AI model. The generating AI model generates content that includes a legal risk assessment and recommended actions based on the input (legal information). The generating AI model is operated using prompt statements, and content including recommended actions is output.

[0110] Step 4:

[0111] The server optimizes the generated content based on organization-specific policies. Customization processing is used to adjust the output content to corporate standards. Content adjustments and wording optimizations are performed to generate the final output.

[0112] Step 5:

[0113] The server electronically distributes the optimized final output to the relevant parties via a transmission processing system. It generates a distribution list based on the recipient's profile and sends the output (final content) via email or other electronic means.

[0114] 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.

[0115] This invention enhances the user experience by combining a system that searches for legal information related to business operations and generates suggested content with an emotion engine that recognizes user emotions. The system features a user interface for inputting business details, automatically searches for relevant legal information based on the input, and creates suggested content using a generative model. Furthermore, the generated content is customized based on company-specific requirements and ultimately presented to the user.

[0116] As users input work details into the user interface on their device, the emotion engine analyzes their facial expressions and tone of voice in real time. For example, if a user is feeling stressed, the emotion engine detects this and adjusts the system to provide feedback in a relaxed tone. This feature allows users to continue inputting work details with peace of mind.

[0117] Upon receiving user input, the terminal sends pre-processed data to the server. The server uses RAG technology to quickly search for relevant laws and risk information and generate suggested content, including legal advice. The generation model considers the user's emotional state and provides information in the most appropriate way.

[0118] Next, the server customizes the generated proposal content based on the company's policies and industry requirements. This customized information is presented to the user via a terminal for final confirmation. After the user reviews the provided information and makes any necessary modifications, the server automatically sends the email or document to the relevant department.

[0119] For example, when a project manager seeks legal advice regarding the release of new software, the emotion engine can sense the project manager's stress level, and the server can generate a more concise and easy-to-understand explanation, providing feedback in a relaxed tone. In this way, the system can provide flexible support tailored to the user's psychological state.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] The user accesses the user interface on their device and enters information related to their work. The user interface displays guidelines to clearly identify the work, and the user uses these as a reference while entering the information.

[0123] Step 2:

[0124] The terminal receives information entered through the user interface and performs preprocessing of the text data. This preprocessing includes tokenizing the input data and normalizing it as needed.

[0125] Step 3:

[0126] The device activates an emotion engine that analyzes the user's facial expressions and tone of voice in real time while they are typing. The emotion engine detects the user's emotional state and sends that information to the server for analysis.

[0127] Step 4:

[0128] The server retrieves pre-processed data and sentiment information from the sentiment engine. Next, it uses RAG technology to search the database for relevant legal and risk information. Here, the search results are filtered according to the user's emotional state.

[0129] Step 5:

[0130] The server uses a generative model to generate suggested content that includes legal information and advice on how to avoid user risks. During this process, the user's emotional state is taken into consideration, and adjustments are made to ensure the information is presented in an appropriate tone.

[0131] Step 6:

[0132] The server customizes the generated content based on the company's specific requirements and modifies it as needed to reflect the company's policies.

[0133] Step 7:

[0134] The device then presents the final suggested content to the user. The user can review the information and make further manual adjustments if necessary.

[0135] Step 8:

[0136] After user approval, the server automatically sends the final, edited content to the designated recipients. Recipients can be predefined by the user, or the system can determine the most suitable recipients.

[0137] (Example 2)

[0138] 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".

[0139] In today's work environment, efficiently accessing complex laws and risk information and utilizing it in a way that is appropriate for work is difficult. Furthermore, the lack of feedback that takes into account the user's emotional state makes it difficult to reduce user stress and improve efficiency.

[0140] 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.

[0141] In this invention, the server includes means for providing an operation screen for inputting business information, means for searching for relevant laws and regulations and risk information based on the input business information, and means for using a generation model that generates suggested content based on the searched information. This allows users to efficiently search and utilize complex information and receive feedback that takes their emotional state into consideration.

[0142] An "operation screen" is an element that provides a user interface (UI) for users to input business information and interact with the system.

[0143] "Business information" refers to information and data that users input into the system in relation to specific tasks.

[0144] "Laws and regulations" refer to a collection of laws and regulations enacted by the government or related organizations, which serve as guidelines and standards for specific tasks or actions.

[0145] "Hazard information" refers to information about risks and potential problems that may arise in connection with a particular business operation.

[0146] A "generative model" refers to artificial intelligence or algorithms used to construct proposals based on input data.

[0147] "Generated content" refers to a collection of suggestions and information that the system creates and provides based on user input and external information.

[0148] "Adaptation" refers to the act of adjusting and optimizing generated content to meet specific requirements and conditions.

[0149] "Emotional state" refers to data used by the system to recognize the user's emotions and psychological state in real time and to provide a corresponding response.

[0150] This invention is a system for users to efficiently input business information and obtain relevant laws and risk information. This system combines a user interface, an emotion recognition engine, and a generative AI model to provide information adapted to the user.

[0151] The terminal provides a user interface, offering a platform for users to input work-related information. Through this interface, users can enter prompts such as, for example, "What are the legal requirements for the new product?" The interface accepts both text and voice input.

[0152] The device is equipped with an emotion recognition engine that analyzes the user's facial expressions and tone of voice to recognize their emotional state. This engine is used to adjust the system's response in real time according to the user's stress level and attention level.

[0153] The entered business information is sent from the terminal to the server. The server uses RAG technology (a generation technology with enhanced information retrieval capabilities) to quickly retrieve necessary information from a database of relevant laws and risks. This information is then organized into suggestions for the user using a generation AI model.

[0154] The server further customizes the generated proposals according to the organization's specific policies and requirements. This customization process makes the proposals more business-oriented and presents them in an easy-to-understand format for users.

[0155] Ultimately, the terminal presents the user with customized suggestions. The user can review the suggestions and make modifications as needed. Once the information has been finalized, it is automatically sent from the server to the relevant departments via email or document.

[0156] A concrete example is a scenario where a project manager seeks legal advice regarding the release of new software. In this case, the emotion engine senses the project manager's tension, and the system generates a concise and clear explanation, providing feedback in a relaxed tone. This allows the user to use the system with confidence.

[0157] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0158] Step 1: The user enters business information through the terminal's user interface. They enter specific questions as prompts, such as "What are the legal requirements for the new product?" The terminal receives this input and converts it to text format. In this step, if voice input is used, it is converted to text data using speech recognition technology.

[0159] Step 2: The device uses an emotion recognition engine to analyze the user's emotional state. This analysis uses the camera and microphone to evaluate facial expressions and voice tone in real time to determine whether the user is experiencing stress. The user's voice and video are used as input data, and an indicator of their emotional state is obtained as output.

[0160] Step 3: The terminal sends the entered business information and analyzed sentiment data to the server. Here, the combined data package is transferred to the server, which facilitates the next processing. The data package includes the user's specific prompt text and sentiment indicators.

[0161] Step 4: The server uses RAG technology to search the database for relevant legal and risk information. In this step, a search query is executed to quickly extract highly relevant information based on the content of the prompt. A list of relevant information is generated as output.

[0162] Step 5: The server uses a generative AI model to create suggested content based on the extracted information. In this process, the generative model organizes the information and constructs suggested content in a user-friendly format. The output content is presented in an appropriate tone based on sentiment indicators.

[0163] Step 6: The server customizes the generated proposed content according to the organization's specific requirements. Here, the content is adjusted to conform to corporate policies and industry standards. The adjusted content becomes the final output.

[0164] Step 7: The device presents the customized content to the user. The user can review the presented information and make corrections as needed. In this process, the information reviewed by the user is finalized after a final evaluation.

[0165] Step 8: The server automatically sends the user-approved information to the relevant departments via email or document. Here, the confirmed content is output in the appropriate format and provided to the relevant parties.

[0166] (Application Example 2)

[0167] 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".

[0168] In today's business environment, it is essential to quickly and accurately grasp relevant legal and risk information. However, doing so manually is time-consuming, and its efficiency is significantly reduced, especially in situations of high emotional stress. Furthermore, there is a risk of misinterpretation of legal information, and it is necessary to avoid the resulting negative impact on business activities. Therefore, there is a need for methods of providing legal information that take into account the psychological state of the user.

[0169] 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.

[0170] In this invention, the server includes means for providing an interface for inputting business operations, means for searching for relevant legal and risk information based on the input business operations, means for using a generative model to generate suggested content based on the searched information, means for modifying the generated content based on company-specific requirements, means for analyzing the user's psychological state and adjusting feedback based on emotions, and means for communicating the final generated content. This makes it possible to provide legal information that is adapted to the user's emotional state in diverse business environments.

[0171] "Business operations" refer to a series of activities or tasks performed to achieve a specific objective.

[0172] An "interface" refers to the point of contact or method by which humans and machines exchange information.

[0173] "Legal information" refers to specific data and knowledge related to laws and regulations.

[0174] "Risk information" refers to information about potential dangers or uncertainties that may arise in specific circumstances.

[0175] A "generative model" is a computational method that creates new output data based on specific input data.

[0176] "Company-specific requirements" refer to the unique standards and requirements that a particular company possesses.

[0177] "Psychological state" refers to the state of an individual's inner emotions and thoughts.

[0178] "Feedback" means providing a response or evaluation to an action or its outcome.

[0179] "Communication" refers to the act or process of sending and receiving data and information.

[0180] To implement this invention, a system using a server and a terminal is first constructed. The user inputs work details through the terminal, and this input is transmitted to the server via an interface. At this time, the terminal is equipped with an emotion engine that analyzes the user's psychological state in real time, analyzing the user's facial expressions and voice tone. The server analyzes this data and uses the latest search technology to quickly retrieve relevant legal information and risk information.

[0181] The server utilizes RAG (Retrieval-Augmented Generation) technology to retrieve information related to the entered business content from a legal database in real time, and uses a generative AI model to generate appropriate suggestion content. This generative model provides information while considering the user's psychological state, and the generated content is further customized based on the company's specific requirements.

[0182] As a concrete example, a factory safety monitoring robot analyzes the emotional state of workers and presents instructions regarding safety procedures in an appropriate tone, thereby reducing worker stress and supporting efficient work. In this way, the system provides flexible legal information that adapts to the user's emotions.

[0183] Furthermore, the platform for realizing this system uses the Python programming language, with the speech_recognition module for speech recognition and a virtual EmotionEngine for sentiment analysis. The generated suggestion content is adjusted according to the company's policies, and the final content is provided as feedback to the user.

[0184] An example of a prompt to input into a generative AI model is: "Perform sentiment analysis during factory work and provide feedback to alleviate worker tension. Please provide specific examples of instructions."

[0185] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0186] Step 1:

[0187] The user inputs work details through the terminal's interface. The input work details are received by the terminal in text format. Here, the user inputs specific work details and questions in text.

[0188] Step 2:

[0189] The terminal transmits the entered work content to the emotion analysis engine in real time, analyzing the user's facial expressions and tone of voice. The emotion engine identifies the user's emotional state based on the input audio and text, and outputs the stress level and type of emotion. In this step, the user's psychological state is output as data from the engine as an analysis result.

[0190] Step 3:

[0191] The terminal transmits the user's psychological state, obtained through sentiment analysis, and the text-entered work content to the server. The server receives the work content and searches its database for relevant legal and risk information. Here, RAG technology is used to acquire and search for legal data. The input is the text of the work content, and the output is text data of the relevant legal information.

[0192] Step 4:

[0193] The server generates suggested content using a generative AI model based on the acquired information. This generative model derives the optimal content while considering the user's psychological state. The input here is legal data and the user's emotional information, and the output is customized suggested content for the user.

[0194] Step 5:

[0195] The server adjusts the generated suggestion content based on the company's specific requirements to create the final feedback content. This step utilizes the company's policy data for content customization. The input is the suggestion content, and the output is the company-tailored feedback content.

[0196] Step 6:

[0197] The server sends the final feedback content to the terminal and presents it to the user. The terminal displays the data received as feedback in the user interface, allowing the user to review it. In this step, the adjusted feedback is visually presented to the user as the final output.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] [Second Embodiment]

[0202] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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".

[0214] This invention is a system for streamlining legal affairs within companies. The system automatically searches for relevant legal and risk information when a user inputs their work details, and then provides suggestions to the user through content generation using a generative model. Furthermore, the generated content is customized according to the company's specific requirements and distributed via appropriate transmission methods.

[0215] The user first accesses the user interface using a terminal and specifies the nature of their request. For example, if the user is seeking legal advice regarding copyright issues for a new product, they input the relevant information. The terminal receives this input, performs the necessary preprocessing, and then sends it to the server.

[0216] The server uses RAG technology to search for relevant legal information from databases. This process utilizes internal knowledge systems and up-to-date external legal databases. Based on the retrieved data, a generative model generates content that includes legal risks and recommended actions.

[0217] Next, the server customizes the generated content based on the company's standards. This includes adjusting company policies and industry-specific wording. Once the final adjustments are complete, the terminal displays the submitted content to the user, providing an opportunity for review and correction.

[0218] As a concrete example, when a new software product is released, a user might enter "I want to review the legal procedures associated with the release" into the interface. The server gathers the latest information related to copyright law and sales licenses, and a generative AI model creates a report containing the best course of action. The user reviews this information and makes any necessary manual corrections, after which the server automatically sends the completed email or documents to the relevant departments or legal representatives.

[0219] This significantly simplifies the process of legal compliance in business operations, while minimizing legal risks and improving operational efficiency.

[0220] The following describes the processing flow.

[0221] Step 1:

[0222] Users access a dedicated user interface and input the business details that need to be analyzed. Users can freely input specific information, such as legal matters related to the release of a new product.

[0223] Step 2:

[0224] The terminal receives the work details from the user and preprocesses the text data. Specifically, it tokenizes the input text and divides it into words and phrases. It also normalizes the data as needed and converts it into a standard format.

[0225] Step 3:

[0226] The server uses RAG technology to search for relevant legal and risk information from databases based on pre-processed data. The server accesses internal knowledge systems and external legal databases to collect the latest legal information.

[0227] Step 4:

[0228] The server inputs the collected information into a generative model and generates suggested content related to the user's work. At this stage, the generative model is adjusted to include specific advice regarding legal compliance and risk management.

[0229] Step 5:

[0230] The server customizes the generated content to meet the company's needs. A process is carried out to adjust the format and content of output documents and emails based on company policies and industry-specific requirements.

[0231] Step 6:

[0232] The device presents the user with customized generated content. The user reviews this content and makes manual corrections if necessary. A mechanism is provided on the interface to request approval of the content.

[0233] Step 7:

[0234] The server automatically sends the final content, once confirmed by the user, to the designated recipient. The email or document recipient is either pre-specified by the user or automatically determined by the system.

[0235] (Example 1)

[0236] 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."

[0237] Modern business activities require companies to conduct business quickly while complying with a wide range of laws and regulations. However, because legal information is frequently updated and its scope of application is broad, it is difficult to implement appropriate risk countermeasures based on the latest information. As a result, the burden of legal compliance increases, and operational efficiency declines. There is a need for effective solutions to these problems and streamline corporate legal affairs.

[0238] 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.

[0239] In this invention, the server includes means for providing a user screen for inputting business details, means for searching relevant legal and risk information based on the input business details, means for using a generation model to generate suggested content based on the acquired information, means for adjusting the generated content based on organization-specific requirements, and means including an interface for users to review and modify the content. This enables companies to comply with laws and regulations quickly and efficiently, reduces the burden of legal work, and improves operational efficiency.

[0240] The "user screen" refers to the interface used for inputting work details and is used when users provide work-related information to the system.

[0241] "Legal information" refers to information about various laws and regulations, and is data that should be referenced in order to meet the legal requirements related to business operations.

[0242] "Risk information" refers to information about risks that should be recognized in order to prevent violations of laws and regulations related to the performance of business operations.

[0243] A "generative model" is an algorithm or technology that is used to automatically create suggested content based on input information.

[0244] "Organization-specific requirements" refer to the unique standards and rules that a particular company or organization must adhere to, in accordance with its policies and industry characteristics.

[0245] "Means including an interface" means technical means that allow users to review and modify generated content as needed.

[0246] This invention provides a system that streamlines legal affairs for companies. Users begin using the system by inputting their work details using a terminal. The terminal receives input from the user through an interactive screen called a user interface, organizes the information, and sends it to the server.

[0247] The server has the functionality to search for legal and risk information from multiple databases. Utilizing RAG technology, it acquires information from internal knowledge systems and external, up-to-date legal databases. Based on the acquired information, the server uses a generative AI model to generate content that suggests to the user. This suggested content includes risk assessments and recommended actions related to the law.

[0248] Furthermore, the server adjusts the generated content based on specific company-specific policies and requirements. This process reflects the company's unique language and industry characteristics. The adjusted content is then presented to the user via their device, where they can review and modify it as needed.

[0249] As a concrete example, when releasing a new software product, a user might input "I want to check the legal procedures associated with the release" into their terminal. The server collects relevant copyright law and sales license information and generates a report summarizing countermeasures through a generative AI model. Based on this report, the user can proceed smoothly with the new product release procedures while minimizing legal risks.

[0250] This system simplifies the process of legal compliance in business operations, reduces the legal risks faced by companies, and enables efficient business execution.

[0251] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0252] Step 1:

[0253] The user accesses the terminal's user interface and inputs specific details about their work. This input is sent to the system as a prompt message. For example, the input might be in the format of, "I would like to confirm the legal procedures regarding the copyright of the new product." The purpose of the input is to accurately identify the relevant information needed for subsequent processing.

[0254] Step 2:

[0255] After receiving input from the user, the terminal performs necessary data formatting and checks. This preprocessing verifies that the input data is in the correct format and content to ensure it can be processed correctly on the server. Once verification is complete, the formatted data is sent to the server. The output is the preprocessed data.

[0256] Step 3:

[0257] The server uses RAG technology to search for legal and risk information based on the received data. Specifically, it accesses internal knowledge systems and external legal databases to quickly collect relevant information. The input for this step is pre-processed user input data, and the output is the retrieved legal and risk information.

[0258] Step 4:

[0259] The server generates suggested content using a generative AI model based on the search results. This generation process automatically creates content that includes risk assessments of relevant laws and recommended actions. The generated content is formatted in a user-friendly manner. The input is the searched legal information, and the output is the generated content.

[0260] Step 5:

[0261] The server customizes the generated content based on the company's specific requirements. Here, the wording is adjusted according to company policies and industry characteristics. The customized content is then ready to be presented as the final version. The input is the generated content, and the output is the adjusted content.

[0262] Step 6:

[0263] The terminal displays the finalized content to the user. The user can review this content and make corrections if necessary. This interface allows the user to verify that the generated information meets their requirements. After approval, the content proceeds to the next stage, regardless of whether corrections have been made. The input is the refined content, and the output is the content that has been reviewed by the user.

[0264] Step 7:

[0265] After the user completes the verification process, the server sends the final content to the relevant department or legal representative. This is done using methods such as email, and is efficiently delivered through an automated process. The final output is the completed content that has been sent.

[0266] (Application Example 1)

[0267] 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."

[0268] Corporate legal compliance requires significant time and effort in identifying legal information and conducting risk assessments, thus demanding efficient processes. Furthermore, compliance actions must conform to company-specific standards, necessitating the customization of generated information. Automating these processes is also crucial for timely and accurate information sharing with legal personnel and relevant departments.

[0269] 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.

[0270] In this invention, the server includes means for providing a human-machine interface for inputting business content, data processing means for retrieving relevant legal and risk information based on the input business content, means for using a generation model that presents legal risk assessment and recommended processing means based on the retrieved information, customization processing means for optimizing the generated content based on organization-specific standards, and transmission processing means for electronically distributing the generated content to relevant parties. This enables more efficient legal response and enhanced risk management.

[0271] A "human-machine interface" is an interaction method that allows users to input work details and enables information exchange between machines and humans.

[0272] "Data processing means" refers to a function that efficiently searches for relevant legal and risk information based on the entered business content and extracts appropriate information.

[0273] A "generative model" is a content generation technology that uses machine learning algorithms to provide legal risk assessments and recommended actions based on the information obtained.

[0274] "Customization processing means" refers to functions that adjust generated content based on organization-specific standards and policies, and handle it in the most optimal way.

[0275] "Transmission processing means" refers to a function for accurately and quickly distributing the finalized content to relevant parties using electronic means.

[0276] The system for implementing this invention is designed to streamline the processing of legal information within a company. Specifically, it is implemented in the following form:

[0277] Users first input their work details using the provided human-machine interface. This interface provides guidelines for inputting work details, assisting in the accurate entry of information related to specific tasks.

[0278] The entered information is transmitted by the terminal to the data processing system. The server uses the data processing system to retrieve relevant legal and risk information from the legal database based on the entered information. This process utilizes RAG technology to dynamically acquire the latest legal information.

[0279] Next, the server passes the acquired legal information to a generating AI model to generate content that presents legal risk assessments and recommended actions. For example, OpenAI's GPT series is used as the generating AI model. The generated content is optimized based on organization-specific policies and adjusted by customization processing mechanisms.

[0280] The finally adjusted content is electronically distributed to the relevant parties by the server's transmission processing means. Since this process is carried out efficiently and automatically, the evaluation and management of legal risks can be carried out quickly.

[0281] As a specific example, when a user inputs information such as "want to check the legal procedures regarding the release of a new product", the server collects relevant copyright laws and sales license information and generates a report including appropriate legal risks and actions. After the report is customized, it is automatically sent to the legal department staff.

[0282] An example of a prompt sentence is "Please evaluate the legal risks associated with the release of a new product and generate recommended actions."

[0283] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0284] Step 1:

[0285] The user uses the human-machine interface to input the business content. The user input is a specific legal question or issue related to the business. Based on this input, the terminal prepares the business content as initial data and prepares for transmission to the server.

[0286] Step 2:

[0287] The terminal transmits the business content input by the user to the server. The server receives the data and uses the data processing means to search the legal database for laws and risk information related to the input information. A database query is generated based on the input (business content) and relevant information is extracted.

[0288] Step 3:

[0289] The server passes legal information retrieved from the database to the generating AI model. The generating AI model generates content that includes a legal risk assessment and recommended actions based on the input (legal information). The generating AI model is operated using prompt statements, and content including recommended actions is output.

[0290] Step 4:

[0291] The server optimizes the generated content based on organization-specific policies. Customization processing is used to adjust the output content to corporate standards. Content adjustments and wording optimizations are performed to generate the final output.

[0292] Step 5:

[0293] The server electronically distributes the optimized final output to the relevant parties via a transmission processing system. It generates a distribution list based on the recipient's profile and sends the output (final content) via email or other electronic means.

[0294] 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.

[0295] This invention enhances the user experience by combining a system that searches for legal information related to business operations and generates suggested content with an emotion engine that recognizes user emotions. The system features a user interface for inputting business details, automatically searches for relevant legal information based on the input, and creates suggested content using a generative model. Furthermore, the generated content is customized based on company-specific requirements and ultimately presented to the user.

[0296] As users input work details into the user interface on their device, the emotion engine analyzes their facial expressions and tone of voice in real time. For example, if a user is feeling stressed, the emotion engine detects this and adjusts the system to provide feedback in a relaxed tone. This feature allows users to continue inputting work details with peace of mind.

[0297] Upon receiving user input, the terminal sends pre-processed data to the server. The server uses RAG technology to quickly search for relevant laws and risk information and generate suggested content, including legal advice. The generation model considers the user's emotional state and provides information in the most appropriate way.

[0298] Next, the server customizes the generated proposal content based on the company's policies and industry requirements. This customized information is presented to the user via a terminal for final confirmation. After the user reviews the provided information and makes any necessary modifications, the server automatically sends the email or document to the relevant department.

[0299] For example, when a project manager seeks legal advice regarding the release of new software, the emotion engine can sense the project manager's stress level, and the server can generate a more concise and easy-to-understand explanation, providing feedback in a relaxed tone. In this way, the system can provide flexible support tailored to the user's psychological state.

[0300] The following describes the processing flow.

[0301] Step 1:

[0302] The user accesses the user interface on their device and enters information related to their work. The user interface displays guidelines to clearly identify the work, and the user uses these as a reference while entering the information.

[0303] Step 2:

[0304] The terminal receives the information input through the user interface and performs preprocessing on the text data. The preprocessing includes the process of tokenizing the input data and further normalizing it if necessary.

[0305] Step 3:

[0306] The terminal activates the emotion engine and analyzes the facial expressions and voice tones in real time during the user's input. The emotion engine detects the user's emotional state and sends that information to the server for analysis.

[0307] Step 4:

[0308] The server obtains the preprocessed data and the emotion information from the emotion engine. Next, it uses the RAG technology to search for relevant legal information and risk information from the database. Here, the search results are filtered according to the user's emotional state.

[0309] Step 5:

[0310] The server uses a generation model to generate proposed content including legal information and advice on the user's risk avoidance. At this time, the user's emotional state is considered and adjustments are made so that the information is provided in an appropriate tone.

[0311] Step 6:

[0312] The server customizes the generated content based on the company's specific requirements and changes it to information that reflects the company's policies if necessary.

[0313] Step 7:

[0314] The terminal presents the final proposed content to the user. Here, the user can check the information and manually adjust it again if necessary.

[0315] Step 8:

[0316] After user approval, the server automatically sends the final, edited content to the designated recipients. Recipients can be predefined by the user, or the system can determine the most suitable recipients.

[0317] (Example 2)

[0318] 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".

[0319] In today's work environment, efficiently accessing complex laws and risk information and utilizing it in a way that is appropriate for work is difficult. Furthermore, the lack of feedback that takes into account the user's emotional state makes it difficult to reduce user stress and improve efficiency.

[0320] 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.

[0321] In this invention, the server includes means for providing an operation screen for inputting business information, means for searching for relevant laws and regulations and risk information based on the input business information, and means for using a generation model that generates suggested content based on the searched information. This allows users to efficiently search and utilize complex information and receive feedback that takes their emotional state into consideration.

[0322] An "operation screen" is an element that provides a user interface (UI) for users to input business information and interact with the system.

[0323] "Business information" refers to information and data that users input into the system in relation to specific tasks.

[0324] "Laws and regulations" refer to a collection of laws and regulations enacted by the government or related organizations, which serve as guidelines and standards for specific tasks or actions.

[0325] "Hazard information" refers to information about risks and potential problems that may arise in connection with a particular business operation.

[0326] A "generative model" refers to artificial intelligence or algorithms used to construct proposals based on input data.

[0327] "Generated content" refers to a collection of suggestions and information that the system creates and provides based on user input and external information.

[0328] "Adaptation" refers to the act of adjusting and optimizing generated content to meet specific requirements and conditions.

[0329] "Emotional state" refers to data used by the system to recognize the user's emotions and psychological state in real time and to provide a corresponding response.

[0330] This invention is a system for users to efficiently input business information and obtain relevant laws and risk information. This system combines a user interface, an emotion recognition engine, and a generative AI model to provide information adapted to the user.

[0331] The terminal provides a user interface, offering a platform for users to input work-related information. Through this interface, users can enter prompts such as, for example, "What are the legal requirements for the new product?" The interface accepts both text and voice input.

[0332] The device is equipped with an emotion recognition engine that analyzes the user's facial expressions and tone of voice to recognize their emotional state. This engine is used to adjust the system's response in real time according to the user's stress level and attention level.

[0333] The entered business information is sent from the terminal to the server. The server uses RAG technology (a generation technology with enhanced information retrieval capabilities) to quickly retrieve necessary information from a database of relevant laws and risks. This information is then organized into suggestions for the user using a generation AI model.

[0334] The server further customizes the generated proposals according to the organization's specific policies and requirements. This customization process makes the proposals more business-oriented and presents them in an easy-to-understand format for users.

[0335] Ultimately, the terminal presents the user with customized suggestions. The user can review the suggestions and make modifications as needed. Once the information has been finalized, it is automatically sent from the server to the relevant departments via email or document.

[0336] A concrete example is a scenario where a project manager seeks legal advice regarding the release of new software. In this case, the emotion engine senses the project manager's tension, and the system generates a concise and clear explanation, providing feedback in a relaxed tone. This allows the user to use the system with confidence.

[0337] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0338] Step 1: The user enters business information through the terminal's user interface. They enter specific questions as prompts, such as "What are the legal requirements for the new product?" The terminal receives this input and converts it to text format. In this step, if voice input is used, it is converted to text data using speech recognition technology.

[0339] Step 2: The device uses an emotion recognition engine to analyze the user's emotional state. This analysis uses the camera and microphone to evaluate facial expressions and voice tone in real time to determine whether the user is experiencing stress. The user's voice and video are used as input data, and an indicator of their emotional state is obtained as output.

[0340] Step 3: The terminal sends the entered business information and analyzed sentiment data to the server. Here, the combined data package is transferred to the server, which facilitates the next processing. The data package includes the user's specific prompt text and sentiment indicators.

[0341] Step 4: The server uses RAG technology to search the database for relevant legal and risk information. In this step, a search query is executed to quickly extract highly relevant information based on the content of the prompt. A list of relevant information is generated as output.

[0342] Step 5: The server uses a generative AI model to create suggested content based on the extracted information. In this process, the generative model organizes the information and constructs suggested content in a user-friendly format. The output content is presented in an appropriate tone based on sentiment indicators.

[0343] Step 6: The server customizes the generated proposed content according to the organization's specific requirements. Here, the content is adjusted to conform to corporate policies and industry standards. The adjusted content becomes the final output.

[0344] Step 7: The device presents the customized content to the user. The user can review the presented information and make corrections as needed. In this process, the information reviewed by the user is finalized after a final evaluation.

[0345] Step 8: The server automatically sends the user-approved information to the relevant departments via email or document. Here, the confirmed content is output in the appropriate format and provided to the relevant parties.

[0346] (Application Example 2)

[0347] 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."

[0348] In today's business environment, it is essential to quickly and accurately grasp relevant legal and risk information. However, doing so manually is time-consuming, and its efficiency is significantly reduced, especially in situations of high emotional stress. Furthermore, there is a risk of misinterpretation of legal information, and it is necessary to avoid the resulting negative impact on business activities. Therefore, there is a need for methods of providing legal information that take into account the psychological state of the user.

[0349] 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.

[0350] In this invention, the server includes means for providing an interface for inputting business operations, means for searching for relevant legal and risk information based on the input business operations, means for using a generative model to generate suggested content based on the searched information, means for modifying the generated content based on company-specific requirements, means for analyzing the user's psychological state and adjusting feedback based on emotions, and means for communicating the final generated content. This makes it possible to provide legal information that is adapted to the user's emotional state in diverse business environments.

[0351] "Business operations" refer to a series of activities or tasks performed to achieve a specific objective.

[0352] An "interface" refers to the point of contact or method by which humans and machines exchange information.

[0353] "Legal information" refers to specific data and knowledge related to laws and regulations.

[0354] "Risk information" refers to information about potential dangers or uncertainties that may arise in specific circumstances.

[0355] A "generative model" is a computational method that creates new output data based on specific input data.

[0356] "Company-specific requirements" refer to the unique standards and requirements that a particular company possesses.

[0357] "Psychological state" refers to the state of an individual's inner emotions and thoughts.

[0358] "Feedback" means providing a response or evaluation to an action or its outcome.

[0359] "Communication" refers to the act or process of sending and receiving data and information.

[0360] To implement this invention, a system using a server and a terminal is first constructed. The user inputs work details through the terminal, and this input is transmitted to the server via an interface. At this time, the terminal is equipped with an emotion engine that analyzes the user's psychological state in real time, analyzing the user's facial expressions and voice tone. The server analyzes this data and uses the latest search technology to quickly retrieve relevant legal information and risk information.

[0361] The server utilizes RAG (Retrieval-Augmented Generation) technology to retrieve information related to the entered business content from a legal database in real time, and uses a generative AI model to generate appropriate suggestion content. This generative model provides information while considering the user's psychological state, and the generated content is further customized based on the company's specific requirements.

[0362] As a concrete example, a factory safety monitoring robot analyzes the emotional state of workers and presents instructions regarding safety procedures in an appropriate tone, thereby reducing worker stress and supporting efficient work. In this way, the system provides flexible legal information that adapts to the user's emotions.

[0363] Furthermore, the platform for realizing this system uses the Python programming language, with the speech_recognition module for speech recognition and a virtual EmotionEngine for sentiment analysis. The generated suggestion content is adjusted according to the company's policies, and the final content is provided as feedback to the user.

[0364] An example of a prompt to input into a generative AI model is: "Perform sentiment analysis during factory work and provide feedback to alleviate worker tension. Please provide specific examples of instructions."

[0365] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0366] Step 1:

[0367] The user inputs work details through the terminal's interface. The input work details are received by the terminal in text format. Here, the user inputs specific work details and questions in text.

[0368] Step 2:

[0369] The terminal transmits the entered work content to the emotion analysis engine in real time, analyzing the user's facial expressions and tone of voice. The emotion engine identifies the user's emotional state based on the input audio and text, and outputs the stress level and type of emotion. In this step, the user's psychological state is output as data from the engine as an analysis result.

[0370] Step 3:

[0371] The terminal transmits the user's psychological state, obtained through sentiment analysis, and the text-entered work content to the server. The server receives the work content and searches its database for relevant legal and risk information. Here, RAG technology is used to acquire and search for legal data. The input is the text of the work content, and the output is text data of the relevant legal information.

[0372] Step 4:

[0373] The server generates suggested content using a generative AI model based on the acquired information. This generative model derives the optimal content while considering the user's psychological state. The input here is legal data and the user's emotional information, and the output is customized suggested content for the user.

[0374] Step 5:

[0375] The server adjusts the generated suggestion content based on the company's specific requirements to create the final feedback content. This step utilizes the company's policy data for content customization. The input is the suggestion content, and the output is the company-tailored feedback content.

[0376] Step 6:

[0377] The server sends the final feedback content to the terminal and presents it to the user. The terminal displays the data received as feedback in the user interface, allowing the user to review it. In this step, the adjusted feedback is visually presented to the user as the final output.

[0378] 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.

[0379] 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.

[0380] 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.

[0381] [Third Embodiment]

[0382] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0383] 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.

[0384] 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).

[0385] 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.

[0386] 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.

[0387] 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).

[0388] 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.

[0389] 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.

[0390] 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.

[0391] 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.

[0392] 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.

[0393] 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".

[0394] This invention is a system for streamlining legal affairs within companies. The system automatically searches for relevant legal and risk information when a user inputs their work details, and then provides suggestions to the user through content generation using a generative model. Furthermore, the generated content is customized according to the company's specific requirements and distributed via appropriate transmission methods.

[0395] The user first accesses the user interface using a terminal and specifies the nature of their request. For example, if the user is seeking legal advice regarding copyright issues for a new product, they input the relevant information. The terminal receives this input, performs the necessary preprocessing, and then sends it to the server.

[0396] The server uses RAG technology to search for relevant legal information from databases. This process utilizes internal knowledge systems and up-to-date external legal databases. Based on the retrieved data, a generative model generates content that includes legal risks and recommended actions.

[0397] Next, the server customizes the generated content based on the company's standards. This includes adjusting company policies and industry-specific wording. Once the final adjustments are complete, the terminal displays the submitted content to the user, providing an opportunity for review and correction.

[0398] As a concrete example, when a new software product is released, a user might enter "I want to review the legal procedures associated with the release" into the interface. The server gathers the latest information related to copyright law and sales licenses, and a generative AI model creates a report containing the best course of action. The user reviews this information and makes any necessary manual corrections, after which the server automatically sends the completed email or documents to the relevant departments or legal representatives.

[0399] This significantly simplifies the process of legal compliance in business operations, while minimizing legal risks and improving operational efficiency.

[0400] The following describes the processing flow.

[0401] Step 1:

[0402] Users access a dedicated user interface and input the business details that need to be analyzed. Users can freely input specific information, such as legal matters related to the release of a new product.

[0403] Step 2:

[0404] The terminal receives the work details from the user and preprocesses the text data. Specifically, it tokenizes the input text and divides it into words and phrases. It also normalizes the data as needed and converts it into a standard format.

[0405] Step 3:

[0406] The server uses RAG technology to search for relevant legal and risk information from databases based on pre-processed data. The server accesses internal knowledge systems and external legal databases to collect the latest legal information.

[0407] Step 4:

[0408] The server inputs the collected information into a generative model and generates suggested content related to the user's work. At this stage, the generative model is adjusted to include specific advice regarding legal compliance and risk management.

[0409] Step 5:

[0410] The server customizes the generated content to meet the company's needs. A process is carried out to adjust the format and content of output documents and emails based on company policies and industry-specific requirements.

[0411] Step 6:

[0412] The device presents the user with customized generated content. The user reviews this content and makes manual corrections if necessary. A mechanism is provided on the interface to request approval of the content.

[0413] Step 7:

[0414] The server automatically sends the final content, once confirmed by the user, to the designated recipient. The email or document recipient is either pre-specified by the user or automatically determined by the system.

[0415] (Example 1)

[0416] 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."

[0417] Modern business activities require companies to conduct business quickly while complying with a wide range of laws and regulations. However, because legal information is frequently updated and its scope of application is broad, it is difficult to implement appropriate risk countermeasures based on the latest information. As a result, the burden of legal compliance increases, and operational efficiency declines. There is a need for effective solutions to these problems and streamline corporate legal affairs.

[0418] 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.

[0419] In this invention, the server includes means for providing a user screen for inputting business details, means for searching relevant legal and risk information based on the input business details, means for using a generation model to generate suggested content based on the acquired information, means for adjusting the generated content based on organization-specific requirements, and means including an interface for users to review and modify the content. This enables companies to comply with laws and regulations quickly and efficiently, reduces the burden of legal work, and improves operational efficiency.

[0420] The "user screen" refers to the interface used for inputting work details and is used when users provide work-related information to the system.

[0421] "Legal information" refers to information about various laws and regulations, and is data that should be referenced in order to meet the legal requirements related to business operations.

[0422] "Risk information" refers to information about risks that should be recognized in order to prevent violations of laws and regulations related to the performance of business operations.

[0423] A "generative model" is an algorithm or technology that is used to automatically create suggested content based on input information.

[0424] "Organization-specific requirements" refer to the unique standards and rules that a particular company or organization must adhere to, in accordance with its policies and industry characteristics.

[0425] "Means including an interface" means technical means that allow users to review and modify generated content as needed.

[0426] This invention provides a system that streamlines legal affairs for companies. Users begin using the system by inputting their work details using a terminal. The terminal receives input from the user through an interactive screen called a user interface, organizes the information, and sends it to the server.

[0427] The server has the functionality to search for legal and risk information from multiple databases. Utilizing RAG technology, it acquires information from internal knowledge systems and external, up-to-date legal databases. Based on the acquired information, the server uses a generative AI model to generate content that suggests to the user. This suggested content includes risk assessments and recommended actions related to the law.

[0428] Furthermore, the server adjusts the generated content based on specific company-specific policies and requirements. This process reflects the company's unique language and industry characteristics. The adjusted content is then presented to the user via their device, where they can review and modify it as needed.

[0429] As a concrete example, when releasing a new software product, a user might input "I want to check the legal procedures associated with the release" into their terminal. The server collects relevant copyright law and sales license information and generates a report summarizing countermeasures through a generative AI model. Based on this report, the user can proceed smoothly with the new product release procedures while minimizing legal risks.

[0430] This system simplifies the process of legal compliance in business operations, reduces the legal risks faced by companies, and enables efficient business execution.

[0431] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0432] Step 1:

[0433] The user accesses the terminal's user interface and inputs specific details about their work. This input is sent to the system as a prompt message. For example, the input might be in the format of, "I would like to confirm the legal procedures regarding the copyright of the new product." The purpose of the input is to accurately identify the relevant information needed for subsequent processing.

[0434] Step 2:

[0435] After receiving input from the user, the terminal performs necessary data formatting and checks. This preprocessing verifies that the input data is in the correct format and content to ensure it can be processed correctly on the server. Once verification is complete, the formatted data is sent to the server. The output is the preprocessed data.

[0436] Step 3:

[0437] The server uses RAG technology to search for legal and risk information based on the received data. Specifically, it accesses internal knowledge systems and external legal databases to quickly collect relevant information. The input for this step is pre-processed user input data, and the output is the retrieved legal and risk information.

[0438] Step 4:

[0439] The server generates suggested content using a generative AI model based on the search results. This generation process automatically creates content that includes risk assessments of relevant laws and recommended actions. The generated content is formatted in a user-friendly manner. The input is the searched legal information, and the output is the generated content.

[0440] Step 5:

[0441] The server customizes the generated content based on the company's specific requirements. Here, the wording is adjusted according to company policies and industry characteristics. The customized content is then ready to be presented as the final version. The input is the generated content, and the output is the adjusted content.

[0442] Step 6:

[0443] The terminal displays the finalized content to the user. The user can review this content and make corrections if necessary. This interface allows the user to verify that the generated information meets their requirements. After approval, the content proceeds to the next stage, regardless of whether corrections have been made. The input is the refined content, and the output is the content that has been reviewed by the user.

[0444] Step 7:

[0445] After the user completes the verification process, the server sends the final content to the relevant department or legal representative. This is done using methods such as email, and is efficiently delivered through an automated process. The final output is the completed content that has been sent.

[0446] (Application Example 1)

[0447] 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."

[0448] Corporate legal compliance requires significant time and effort in identifying legal information and conducting risk assessments, thus demanding efficient processes. Furthermore, compliance actions must conform to company-specific standards, necessitating the customization of generated information. Automating these processes is also crucial for timely and accurate information sharing with legal personnel and relevant departments.

[0449] 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.

[0450] In this invention, the server includes means for providing a human-machine interface for inputting business content, data processing means for retrieving relevant legal and risk information based on the input business content, means for using a generation model that presents legal risk assessment and recommended processing means based on the retrieved information, customization processing means for optimizing the generated content based on organization-specific standards, and transmission processing means for electronically distributing the generated content to relevant parties. This enables more efficient legal response and enhanced risk management.

[0451] A "human-machine interface" is an interaction method that allows users to input work details and enables information exchange between machines and humans.

[0452] "Data processing means" refers to a function that efficiently searches for relevant legal and risk information based on the entered business content and extracts appropriate information.

[0453] A "generative model" is a content generation technology that uses machine learning algorithms to provide legal risk assessments and recommended actions based on the information obtained.

[0454] "Customization processing means" refers to functions that adjust generated content based on organization-specific standards and policies, and handle it in the most optimal way.

[0455] "Transmission processing means" refers to a function for accurately and quickly distributing the finalized content to relevant parties using electronic means.

[0456] The system for implementing this invention is designed to streamline the processing of legal information within a company. Specifically, it is implemented in the following form:

[0457] Users first input their work details using the provided human-machine interface. This interface provides guidelines for inputting work details, assisting in the accurate entry of information related to specific tasks.

[0458] The entered information is transmitted by the terminal to the data processing system. The server uses the data processing system to retrieve relevant legal and risk information from the legal database based on the entered information. This process utilizes RAG technology to dynamically acquire the latest legal information.

[0459] Next, the server passes the acquired legal information to a generating AI model to generate content that presents legal risk assessments and recommended actions. For example, OpenAI's GPT series is used as the generating AI model. The generated content is optimized based on organization-specific policies and adjusted by customization processing mechanisms.

[0460] The finalized content is then electronically distributed to the relevant parties via the server's transmission processing system. This process is efficient and automated, enabling rapid assessment and management of legal risks.

[0461] For example, if a user inputs information such as "I want to check the legal procedures regarding the release of a new product," the server will collect relevant copyright law and sales license information and generate a report that includes appropriate legal risks and actions. After being customized, this report is automatically sent to the legal department.

[0462] An example of a prompt message is, "Assess the legal risks associated with the release of the new product and generate recommended actions."

[0463] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0464] Step 1:

[0465] The user inputs work details using a human-machine interface. This user input consists of specific legal questions and issues related to the work. Based on this input, the terminal prepares the work details as initial data and prepares it for transmission to the server.

[0466] Step 2:

[0467] The terminal transmits the work details entered by the user to the server. The server receives the data and uses data processing tools to search the legal database for laws and risk information related to the entered information. Based on the input (work details), it generates a database query and extracts the relevant information.

[0468] Step 3:

[0469] The server passes legal information retrieved from the database to the generating AI model. The generating AI model generates content that includes a legal risk assessment and recommended actions based on the input (legal information). The generating AI model is operated using prompt statements, and content including recommended actions is output.

[0470] Step 4:

[0471] The server optimizes the generated content based on organization-specific policies. Customization processing is used to adjust the output content to corporate standards. Content adjustments and wording optimizations are performed to generate the final output.

[0472] Step 5:

[0473] The server electronically distributes the optimized final output to the relevant parties via a transmission processing system. It generates a distribution list based on the recipient's profile and sends the output (final content) via email or other electronic means.

[0474] 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.

[0475] This invention enhances the user experience by combining a system that searches for legal information related to business operations and generates suggested content with an emotion engine that recognizes user emotions. The system features a user interface for inputting business details, automatically searches for relevant legal information based on the input, and creates suggested content using a generative model. Furthermore, the generated content is customized based on company-specific requirements and ultimately presented to the user.

[0476] As users input work details into the user interface on their device, the emotion engine analyzes their facial expressions and tone of voice in real time. For example, if a user is feeling stressed, the emotion engine detects this and adjusts the system to provide feedback in a relaxed tone. This feature allows users to continue inputting work details with peace of mind.

[0477] Upon receiving user input, the terminal sends pre-processed data to the server. The server uses RAG technology to quickly search for relevant laws and risk information and generate suggested content, including legal advice. The generation model considers the user's emotional state and provides information in the most appropriate way.

[0478] Next, the server customizes the generated proposal content based on the company's policies and industry requirements. This customized information is presented to the user via a terminal for final confirmation. After the user reviews the provided information and makes any necessary modifications, the server automatically sends the email or document to the relevant department.

[0479] For example, when a project manager seeks legal advice regarding the release of new software, the emotion engine can sense the project manager's stress level, and the server can generate a more concise and easy-to-understand explanation, providing feedback in a relaxed tone. In this way, the system can provide flexible support tailored to the user's psychological state.

[0480] The following describes the processing flow.

[0481] Step 1:

[0482] The user accesses the user interface on their device and enters information related to their work. The user interface displays guidelines to clearly identify the work, and the user uses these as a reference while entering the information.

[0483] Step 2:

[0484] The terminal receives information entered through the user interface and performs preprocessing of the text data. This preprocessing includes tokenizing the input data and normalizing it as needed.

[0485] Step 3:

[0486] The device activates an emotion engine that analyzes the user's facial expressions and tone of voice in real time while they are typing. The emotion engine detects the user's emotional state and sends that information to the server for analysis.

[0487] Step 4:

[0488] The server retrieves pre-processed data and sentiment information from the sentiment engine. Next, it uses RAG technology to search the database for relevant legal and risk information. Here, the search results are filtered according to the user's emotional state.

[0489] Step 5:

[0490] The server uses a generative model to generate suggested content that includes legal information and advice on how to avoid user risks. During this process, the user's emotional state is taken into consideration, and adjustments are made to ensure the information is presented in an appropriate tone.

[0491] Step 6:

[0492] The server customizes the generated content based on the company's specific requirements and modifies it as needed to reflect the company's policies.

[0493] Step 7:

[0494] The device then presents the final suggested content to the user. The user can review the information and make further manual adjustments if necessary.

[0495] Step 8:

[0496] After user approval, the server automatically sends the final, edited content to the designated recipients. Recipients can be predefined by the user, or the system can determine the most suitable recipients.

[0497] (Example 2)

[0498] 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."

[0499] In today's work environment, efficiently accessing complex laws and risk information and utilizing it in a way that is appropriate for work is difficult. Furthermore, the lack of feedback that takes into account the user's emotional state makes it difficult to reduce user stress and improve efficiency.

[0500] 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.

[0501] In this invention, the server includes means for providing an operation screen for inputting business information, means for searching for relevant laws and regulations and risk information based on the input business information, and means for using a generation model that generates suggested content based on the searched information. This allows users to efficiently search and utilize complex information and receive feedback that takes their emotional state into consideration.

[0502] An "operation screen" is an element that provides a user interface (UI) for users to input business information and interact with the system.

[0503] "Business information" refers to information and data that users input into the system in relation to specific tasks.

[0504] "Laws and regulations" refer to a collection of laws and regulations enacted by the government or related organizations, which serve as guidelines and standards for specific tasks or actions.

[0505] "Hazard information" refers to information about risks and potential problems that may arise in connection with a particular business operation.

[0506] A "generative model" refers to artificial intelligence or algorithms used to construct proposals based on input data.

[0507] "Generated content" refers to a collection of suggestions and information that the system creates and provides based on user input and external information.

[0508] "Adaptation" refers to the act of adjusting and optimizing generated content to meet specific requirements and conditions.

[0509] "Emotional state" refers to data used by the system to recognize the user's emotions and psychological state in real time and to provide a corresponding response.

[0510] This invention is a system for users to efficiently input business information and obtain relevant laws and risk information. This system combines a user interface, an emotion recognition engine, and a generative AI model to provide information adapted to the user.

[0511] The terminal provides a user interface, offering a platform for users to input work-related information. Through this interface, users can enter prompts such as, for example, "What are the legal requirements for the new product?" The interface accepts both text and voice input.

[0512] The device is equipped with an emotion recognition engine that analyzes the user's facial expressions and tone of voice to recognize their emotional state. This engine is used to adjust the system's response in real time according to the user's stress level and attention level.

[0513] The entered business information is sent from the terminal to the server. The server uses RAG technology (a generation technology with enhanced information retrieval capabilities) to quickly retrieve necessary information from a database of relevant laws and risks. This information is then organized into suggestions for the user using a generation AI model.

[0514] The server further customizes the generated proposals according to the organization's specific policies and requirements. This customization process makes the proposals more business-oriented and presents them in an easy-to-understand format for users.

[0515] Ultimately, the terminal presents the user with customized suggestions. The user can review the suggestions and make modifications as needed. Once the information has been finalized, it is automatically sent from the server to the relevant departments via email or document.

[0516] A concrete example is a scenario where a project manager seeks legal advice regarding the release of new software. In this case, the emotion engine senses the project manager's tension, and the system generates a concise and clear explanation, providing feedback in a relaxed tone. This allows the user to use the system with confidence.

[0517] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0518] Step 1: The user enters business information through the terminal's user interface. They enter specific questions as prompts, such as "What are the legal requirements for the new product?" The terminal receives this input and converts it to text format. In this step, if voice input is used, it is converted to text data using speech recognition technology.

[0519] Step 2: The device uses an emotion recognition engine to analyze the user's emotional state. This analysis uses the camera and microphone to evaluate facial expressions and voice tone in real time to determine whether the user is experiencing stress. The user's voice and video are used as input data, and an indicator of their emotional state is obtained as output.

[0520] Step 3: The terminal sends the entered business information and analyzed sentiment data to the server. Here, the combined data package is transferred to the server, which facilitates the next processing. The data package includes the user's specific prompt text and sentiment indicators.

[0521] Step 4: The server uses RAG technology to search the database for relevant legal and risk information. In this step, a search query is executed to quickly extract highly relevant information based on the content of the prompt. A list of relevant information is generated as output.

[0522] Step 5: The server uses a generative AI model to create suggested content based on the extracted information. In this process, the generative model organizes the information and constructs suggested content in a user-friendly format. The output content is presented in an appropriate tone based on sentiment indicators.

[0523] Step 6: The server customizes the generated proposed content according to the organization's specific requirements. Here, the content is adjusted to conform to corporate policies and industry standards. The adjusted content becomes the final output.

[0524] Step 7: The device presents the customized content to the user. The user can review the presented information and make corrections as needed. In this process, the information reviewed by the user is finalized after a final evaluation.

[0525] Step 8: The server automatically sends the user-approved information to the relevant departments via email or document. Here, the confirmed content is output in the appropriate format and provided to the relevant parties.

[0526] (Application Example 2)

[0527] 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."

[0528] In today's business environment, it is essential to quickly and accurately grasp relevant legal and risk information. However, doing so manually is time-consuming, and its efficiency is significantly reduced, especially in situations of high emotional stress. Furthermore, there is a risk of misinterpretation of legal information, and it is necessary to avoid the resulting negative impact on business activities. Therefore, there is a need for methods of providing legal information that take into account the psychological state of the user.

[0529] 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.

[0530] In this invention, the server includes means for providing an interface for inputting business operations, means for searching for relevant legal and risk information based on the input business operations, means for using a generative model to generate suggested content based on the searched information, means for modifying the generated content based on company-specific requirements, means for analyzing the user's psychological state and adjusting feedback based on emotions, and means for communicating the final generated content. This makes it possible to provide legal information that is adapted to the user's emotional state in diverse business environments.

[0531] "Business operations" refer to a series of activities or tasks performed to achieve a specific objective.

[0532] An "interface" refers to the point of contact or method by which humans and machines exchange information.

[0533] "Legal information" refers to specific data and knowledge related to laws and regulations.

[0534] "Risk information" refers to information about potential dangers or uncertainties that may arise in specific circumstances.

[0535] A "generative model" is a computational method that creates new output data based on specific input data.

[0536] "Company-specific requirements" refer to the unique standards and requirements that a particular company possesses.

[0537] "Psychological state" refers to the state of an individual's inner emotions and thoughts.

[0538] "Feedback" means providing a response or evaluation to an action or its outcome.

[0539] "Communication" refers to the act or process of sending and receiving data and information.

[0540] To implement this invention, a system using a server and a terminal is first constructed. The user inputs work details through the terminal, and this input is transmitted to the server via an interface. At this time, the terminal is equipped with an emotion engine that analyzes the user's psychological state in real time, analyzing the user's facial expressions and voice tone. The server analyzes this data and uses the latest search technology to quickly retrieve relevant legal information and risk information.

[0541] The server utilizes RAG (Retrieval-Augmented Generation) technology to retrieve information related to the entered business content from a legal database in real time, and uses a generative AI model to generate appropriate suggestion content. This generative model provides information while considering the user's psychological state, and the generated content is further customized based on the company's specific requirements.

[0542] As a concrete example, a factory safety monitoring robot analyzes the emotional state of workers and presents instructions regarding safety procedures in an appropriate tone, thereby reducing worker stress and supporting efficient work. In this way, the system provides flexible legal information that adapts to the user's emotions.

[0543] Furthermore, the platform for realizing this system uses the Python programming language, with the speech_recognition module for speech recognition and a virtual EmotionEngine for sentiment analysis. The generated suggestion content is adjusted according to the company's policies, and the final content is provided as feedback to the user.

[0544] An example of a prompt to input into a generative AI model is: "Perform sentiment analysis during factory work and provide feedback to alleviate worker tension. Please provide specific examples of instructions."

[0545] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0546] Step 1:

[0547] The user inputs work details through the terminal's interface. The input work details are received by the terminal in text format. Here, the user inputs specific work details and questions in text.

[0548] Step 2:

[0549] The terminal transmits the entered work content to the emotion analysis engine in real time, analyzing the user's facial expressions and tone of voice. The emotion engine identifies the user's emotional state based on the input audio and text, and outputs the stress level and type of emotion. In this step, the user's psychological state is output as data from the engine as an analysis result.

[0550] Step 3:

[0551] The terminal transmits the user's psychological state, obtained through sentiment analysis, and the text-entered work content to the server. The server receives the work content and searches its database for relevant legal and risk information. Here, RAG technology is used to acquire and search for legal data. The input is the text of the work content, and the output is text data of the relevant legal information.

[0552] Step 4:

[0553] The server generates suggested content using a generative AI model based on the acquired information. This generative model derives the optimal content while considering the user's psychological state. The input here is legal data and the user's emotional information, and the output is customized suggested content for the user.

[0554] Step 5:

[0555] The server adjusts the generated suggestion content based on the company's specific requirements to create the final feedback content. This step utilizes the company's policy data for content customization. The input is the suggestion content, and the output is the company-tailored feedback content.

[0556] Step 6:

[0557] The server sends the final feedback content to the terminal and presents it to the user. The terminal displays the data received as feedback in the user interface, allowing the user to review it. In this step, the adjusted feedback is visually presented to the user as the final output.

[0558] 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.

[0559] 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.

[0560] 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.

[0561] [Fourth Embodiment]

[0562] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0563] 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.

[0564] 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).

[0565] 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.

[0566] 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.

[0567] 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).

[0568] 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.

[0569] 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.

[0570] 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.

[0571] 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.

[0572] 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.

[0573] 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.

[0574] 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".

[0575] This invention is a system for streamlining legal affairs within companies. The system automatically searches for relevant legal and risk information when a user inputs their work details, and then provides suggestions to the user through content generation using a generative model. Furthermore, the generated content is customized according to the company's specific requirements and distributed via appropriate transmission methods.

[0576] The user first accesses the user interface using a terminal and specifies the nature of their request. For example, if the user is seeking legal advice regarding copyright issues for a new product, they input the relevant information. The terminal receives this input, performs the necessary preprocessing, and then sends it to the server.

[0577] The server uses RAG technology to search for relevant legal information from databases. This process utilizes internal knowledge systems and up-to-date external legal databases. Based on the retrieved data, a generative model generates content that includes legal risks and recommended actions.

[0578] Next, the server customizes the generated content based on the company's standards. This includes adjusting company policies and industry-specific wording. Once the final adjustments are complete, the terminal displays the submitted content to the user, providing an opportunity for review and correction.

[0579] As a concrete example, when a new software product is released, a user might enter "I want to review the legal procedures associated with the release" into the interface. The server gathers the latest information related to copyright law and sales licenses, and a generative AI model creates a report containing the best course of action. The user reviews this information and makes any necessary manual corrections, after which the server automatically sends the completed email or documents to the relevant departments or legal representatives.

[0580] This significantly simplifies the process of legal compliance in business operations, while minimizing legal risks and improving operational efficiency.

[0581] The following describes the processing flow.

[0582] Step 1:

[0583] Users access a dedicated user interface and input the business details that need to be analyzed. Users can freely input specific information, such as legal matters related to the release of a new product.

[0584] Step 2:

[0585] The terminal receives the work details from the user and preprocesses the text data. Specifically, it tokenizes the input text and divides it into words and phrases. It also normalizes the data as needed and converts it into a standard format.

[0586] Step 3:

[0587] The server uses RAG technology to search for relevant legal and risk information from databases based on pre-processed data. The server accesses internal knowledge systems and external legal databases to collect the latest legal information.

[0588] Step 4:

[0589] The server inputs the collected information into a generative model and generates suggested content related to the user's work. At this stage, the generative model is adjusted to include specific advice regarding legal compliance and risk management.

[0590] Step 5:

[0591] The server customizes the generated content to meet the company's needs. A process is carried out to adjust the format and content of output documents and emails based on company policies and industry-specific requirements.

[0592] Step 6:

[0593] The device presents the user with customized generated content. The user reviews this content and makes manual corrections if necessary. A mechanism is provided on the interface to request approval of the content.

[0594] Step 7:

[0595] The server automatically sends the final content, once confirmed by the user, to the designated recipient. The email or document recipient is either pre-specified by the user or automatically determined by the system.

[0596] (Example 1)

[0597] 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".

[0598] Modern business activities require companies to conduct business quickly while complying with a wide range of laws and regulations. However, because legal information is frequently updated and its scope of application is broad, it is difficult to implement appropriate risk countermeasures based on the latest information. As a result, the burden of legal compliance increases, and operational efficiency declines. There is a need for effective solutions to these problems and streamline corporate legal affairs.

[0599] 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.

[0600] In this invention, the server includes means for providing a user screen for inputting business details, means for searching relevant legal and risk information based on the input business details, means for using a generation model to generate suggested content based on the acquired information, means for adjusting the generated content based on organization-specific requirements, and means including an interface for users to review and modify the content. This enables companies to comply with laws and regulations quickly and efficiently, reduces the burden of legal work, and improves operational efficiency.

[0601] The "user screen" refers to the interface used for inputting work details and is used when users provide work-related information to the system.

[0602] "Legal information" refers to information about various laws and regulations, and is data that should be referenced in order to meet the legal requirements related to business operations.

[0603] "Risk information" refers to information about risks that should be recognized in order to prevent violations of laws and regulations related to the performance of business operations.

[0604] A "generative model" is an algorithm or technology that is used to automatically create suggested content based on input information.

[0605] "Organization-specific requirements" refer to the unique standards and rules that a particular company or organization must adhere to, in accordance with its policies and industry characteristics.

[0606] "Means including an interface" means technical means that allow users to review and modify generated content as needed.

[0607] This invention provides a system that streamlines legal affairs for companies. Users begin using the system by inputting their work details using a terminal. The terminal receives input from the user through an interactive screen called a user interface, organizes the information, and sends it to the server.

[0608] The server has the functionality to search for legal and risk information from multiple databases. Utilizing RAG technology, it acquires information from internal knowledge systems and external, up-to-date legal databases. Based on the acquired information, the server uses a generative AI model to generate content that suggests to the user. This suggested content includes risk assessments and recommended actions related to the law.

[0609] Furthermore, the server adjusts the generated content based on specific company-specific policies and requirements. This process reflects the company's unique language and industry characteristics. The adjusted content is then presented to the user via their device, where they can review and modify it as needed.

[0610] As a concrete example, when releasing a new software product, a user might input "I want to check the legal procedures associated with the release" into their terminal. The server collects relevant copyright law and sales license information and generates a report summarizing countermeasures through a generative AI model. Based on this report, the user can proceed smoothly with the new product release procedures while minimizing legal risks.

[0611] This system simplifies the process of legal compliance in business operations, reduces the legal risks faced by companies, and enables efficient business execution.

[0612] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0613] Step 1:

[0614] The user accesses the terminal's user interface and inputs specific details about their work. This input is sent to the system as a prompt message. For example, the input might be in the format of, "I would like to confirm the legal procedures regarding the copyright of the new product." The purpose of the input is to accurately identify the relevant information needed for subsequent processing.

[0615] Step 2:

[0616] After receiving input from the user, the terminal performs necessary data formatting and checks. This preprocessing verifies that the input data is in the correct format and content to ensure it can be processed correctly on the server. Once verification is complete, the formatted data is sent to the server. The output is the preprocessed data.

[0617] Step 3:

[0618] The server uses RAG technology to search for legal and risk information based on the received data. Specifically, it accesses internal knowledge systems and external legal databases to quickly collect relevant information. The input for this step is pre-processed user input data, and the output is the retrieved legal and risk information.

[0619] Step 4:

[0620] The server generates suggested content using a generative AI model based on the search results. This generation process automatically creates content that includes risk assessments of relevant laws and recommended actions. The generated content is formatted in a user-friendly manner. The input is the searched legal information, and the output is the generated content.

[0621] Step 5:

[0622] The server customizes the generated content based on the company's specific requirements. Here, the wording is adjusted according to company policies and industry characteristics. The customized content is then ready to be presented as the final version. The input is the generated content, and the output is the adjusted content.

[0623] Step 6:

[0624] The terminal displays the finalized content to the user. The user can review this content and make corrections if necessary. This interface allows the user to verify that the generated information meets their requirements. After approval, the content proceeds to the next stage, regardless of whether corrections have been made. The input is the refined content, and the output is the content that has been reviewed by the user.

[0625] Step 7:

[0626] After the user completes the verification process, the server sends the final content to the relevant department or legal representative. This is done using methods such as email, and is efficiently delivered through an automated process. The final output is the completed content that has been sent.

[0627] (Application Example 1)

[0628] 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".

[0629] Corporate legal compliance requires significant time and effort in identifying legal information and conducting risk assessments, thus demanding efficient processes. Furthermore, compliance actions must conform to company-specific standards, necessitating the customization of generated information. Automating these processes is also crucial for timely and accurate information sharing with legal personnel and relevant departments.

[0630] 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.

[0631] In this invention, the server includes means for providing a human-machine interface for inputting business content, data processing means for retrieving relevant legal and risk information based on the input business content, means for using a generation model that presents legal risk assessment and recommended processing means based on the retrieved information, customization processing means for optimizing the generated content based on organization-specific standards, and transmission processing means for electronically distributing the generated content to relevant parties. This enables more efficient legal response and enhanced risk management.

[0632] A "human-machine interface" is an interaction method that allows users to input work details and enables information exchange between machines and humans.

[0633] "Data processing means" refers to a function that efficiently searches for relevant legal and risk information based on the entered business content and extracts appropriate information.

[0634] A "generative model" is a content generation technology that uses machine learning algorithms to provide legal risk assessments and recommended actions based on the information obtained.

[0635] "Customization processing means" refers to functions that adjust generated content based on organization-specific standards and policies, and handle it in the most optimal way.

[0636] "Transmission processing means" refers to a function for accurately and quickly distributing the finalized content to relevant parties using electronic means.

[0637] The system for implementing this invention is designed to streamline the processing of legal information within a company. Specifically, it is implemented in the following form:

[0638] Users first input their work details using the provided human-machine interface. This interface provides guidelines for inputting work details, assisting in the accurate entry of information related to specific tasks.

[0639] The entered information is transmitted by the terminal to the data processing system. The server uses the data processing system to retrieve relevant legal and risk information from the legal database based on the entered information. This process utilizes RAG technology to dynamically acquire the latest legal information.

[0640] Next, the server passes the acquired legal information to a generating AI model to generate content that presents legal risk assessments and recommended actions. For example, OpenAI's GPT series is used as the generating AI model. The generated content is optimized based on organization-specific policies and adjusted by customization processing mechanisms.

[0641] The finalized content is then electronically distributed to the relevant parties via the server's transmission processing system. This process is efficient and automated, enabling rapid assessment and management of legal risks.

[0642] For example, if a user inputs information such as "I want to check the legal procedures regarding the release of a new product," the server will collect relevant copyright law and sales license information and generate a report that includes appropriate legal risks and actions. After being customized, this report is automatically sent to the legal department.

[0643] An example of a prompt message is, "Assess the legal risks associated with the release of the new product and generate recommended actions."

[0644] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0645] Step 1:

[0646] The user inputs work details using a human-machine interface. This user input consists of specific legal questions and issues related to the work. Based on this input, the terminal prepares the work details as initial data and prepares it for transmission to the server.

[0647] Step 2:

[0648] The terminal transmits the work details entered by the user to the server. The server receives the data and uses data processing tools to search the legal database for laws and risk information related to the entered information. Based on the input (work details), it generates a database query and extracts the relevant information.

[0649] Step 3:

[0650] The server passes legal information retrieved from the database to the generating AI model. The generating AI model generates content that includes a legal risk assessment and recommended actions based on the input (legal information). The generating AI model is operated using prompt statements, and content including recommended actions is output.

[0651] Step 4:

[0652] The server optimizes the generated content based on organization-specific policies. Customization processing is used to adjust the output content to corporate standards. Content adjustments and wording optimizations are performed to generate the final output.

[0653] Step 5:

[0654] The server electronically distributes the optimized final output to the relevant parties via a transmission processing system. It generates a distribution list based on the recipient's profile and sends the output (final content) via email or other electronic means.

[0655] 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.

[0656] This invention enhances the user experience by combining a system that searches for legal information related to business operations and generates suggested content with an emotion engine that recognizes user emotions. The system features a user interface for inputting business details, automatically searches for relevant legal information based on the input, and creates suggested content using a generative model. Furthermore, the generated content is customized based on company-specific requirements and ultimately presented to the user.

[0657] As users input work details into the user interface on their device, the emotion engine analyzes their facial expressions and tone of voice in real time. For example, if a user is feeling stressed, the emotion engine detects this and adjusts the system to provide feedback in a relaxed tone. This feature allows users to continue inputting work details with peace of mind.

[0658] Upon receiving user input, the terminal sends pre-processed data to the server. The server uses RAG technology to quickly search for relevant laws and risk information and generate suggested content, including legal advice. The generation model considers the user's emotional state and provides information in the most appropriate way.

[0659] Next, the server customizes the generated proposal content based on the company's policies and industry requirements. This customized information is presented to the user via a terminal for final confirmation. After the user reviews the provided information and makes any necessary modifications, the server automatically sends the email or document to the relevant department.

[0660] For example, when a project manager seeks legal advice regarding the release of new software, the emotion engine can sense the project manager's stress level, and the server can generate a more concise and easy-to-understand explanation, providing feedback in a relaxed tone. In this way, the system can provide flexible support tailored to the user's psychological state.

[0661] The following describes the processing flow.

[0662] Step 1:

[0663] The user accesses the user interface on their device and enters information related to their work. The user interface displays guidelines to clearly identify the work, and the user uses these as a reference while entering the information.

[0664] Step 2:

[0665] The terminal receives information entered through the user interface and performs preprocessing of the text data. This preprocessing includes tokenizing the input data and normalizing it as needed.

[0666] Step 3:

[0667] The device activates an emotion engine that analyzes the user's facial expressions and tone of voice in real time while they are typing. The emotion engine detects the user's emotional state and sends that information to the server for analysis.

[0668] Step 4:

[0669] The server retrieves pre-processed data and sentiment information from the sentiment engine. Next, it uses RAG technology to search the database for relevant legal and risk information. Here, the search results are filtered according to the user's emotional state.

[0670] Step 5:

[0671] The server uses a generative model to generate suggested content that includes legal information and advice on how to avoid user risks. During this process, the user's emotional state is taken into consideration, and adjustments are made to ensure the information is presented in an appropriate tone.

[0672] Step 6:

[0673] The server customizes the generated content based on the company's specific requirements and modifies it as needed to reflect the company's policies.

[0674] Step 7:

[0675] The device then presents the final suggested content to the user. The user can review the information and make further manual adjustments if necessary.

[0676] Step 8:

[0677] After user approval, the server automatically sends the final, edited content to the designated recipients. Recipients can be predefined by the user, or the system can determine the most suitable recipients.

[0678] (Example 2)

[0679] 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".

[0680] In today's work environment, efficiently accessing complex laws and risk information and utilizing it in a way that is appropriate for work is difficult. Furthermore, the lack of feedback that takes into account the user's emotional state makes it difficult to reduce user stress and improve efficiency.

[0681] 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.

[0682] In this invention, the server includes means for providing an operation screen for inputting business information, means for searching for relevant laws and regulations and risk information based on the input business information, and means for using a generation model that generates suggested content based on the searched information. This allows users to efficiently search and utilize complex information and receive feedback that takes their emotional state into consideration.

[0683] An "operation screen" is an element that provides a user interface (UI) for users to input business information and interact with the system.

[0684] "Business information" refers to information and data that users input into the system in relation to specific tasks.

[0685] "Laws and regulations" refer to a collection of laws and regulations enacted by the government or related organizations, which serve as guidelines and standards for specific tasks or actions.

[0686] "Hazard information" refers to information about risks and potential problems that may arise in connection with a particular business operation.

[0687] A "generative model" refers to artificial intelligence or algorithms used to construct proposals based on input data.

[0688] "Generated content" refers to a collection of suggestions and information that the system creates and provides based on user input and external information.

[0689] "Adaptation" refers to the act of adjusting and optimizing generated content to meet specific requirements and conditions.

[0690] "Emotional state" refers to data used by the system to recognize the user's emotions and psychological state in real time and to provide a corresponding response.

[0691] This invention provides a system for users to efficiently input business information and obtain relevant laws and risk information. This system combines a user interface, an emotion recognition engine, and a generative AI model to provide information tailored to the user.

[0692] The terminal provides a user interface, offering a platform for users to input work-related information. Through this interface, users can enter prompts such as, for example, "What are the legal requirements for the new product?" The interface accepts both text and voice input.

[0693] The device is equipped with an emotion recognition engine that analyzes the user's facial expressions and tone of voice to recognize their emotional state. This engine is used to adjust the system's response in real time according to the user's stress level and attention level.

[0694] The entered business information is sent from the terminal to the server. The server uses RAG technology (a generation technology with enhanced information retrieval capabilities) to quickly retrieve necessary information from a database of relevant laws and risks. This information is then organized into suggestions for the user using a generation AI model.

[0695] The server further customizes the generated proposals according to the organization's specific policies and requirements. This customization process makes the proposals more business-oriented and presents them in an easy-to-understand format for users.

[0696] Ultimately, the terminal presents the user with customized suggestions. The user can review the suggestions and make modifications as needed. Once the information has been finalized, it is automatically sent from the server to the relevant departments via email or document.

[0697] A concrete example is a scenario where a project manager seeks legal advice regarding the release of new software. In this case, the emotion engine senses the project manager's tension, and the system generates a concise and clear explanation, providing feedback in a relaxed tone. This allows the user to use the system with confidence.

[0698] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0699] Step 1: The user enters business information through the terminal's user interface. They enter specific questions as prompts, such as "What are the legal requirements for the new product?" The terminal receives this input and converts it to text format. In this step, if voice input is used, it is converted to text data using speech recognition technology.

[0700] Step 2: The device uses an emotion recognition engine to analyze the user's emotional state. This analysis uses the camera and microphone to evaluate facial expressions and voice tone in real time to determine whether the user is experiencing stress. The user's voice and video are used as input data, and an indicator of their emotional state is obtained as output.

[0701] Step 3: The terminal sends the entered business information and analyzed sentiment data to the server. Here, the combined data package is transferred to the server, which facilitates the next processing. The data package includes the user's specific prompt text and sentiment indicators.

[0702] Step 4: The server uses RAG technology to search the database for relevant legal and risk information. In this step, a search query is executed to quickly extract highly relevant information based on the content of the prompt. A list of relevant information is generated as output.

[0703] Step 5: The server uses a generative AI model to create suggested content based on the extracted information. In this process, the generative model organizes the information and constructs suggested content in a user-friendly format. The output content is presented in an appropriate tone based on sentiment indicators.

[0704] Step 6: The server customizes the generated proposed content according to the organization's specific requirements. Here, the content is adjusted to conform to corporate policies and industry standards. The adjusted content becomes the final output.

[0705] Step 7: The device presents the customized content to the user. The user can review the presented information and make corrections as needed. In this process, the information reviewed by the user is finalized after a final evaluation.

[0706] Step 8: The server automatically sends the user-approved information to the relevant departments via email or document. Here, the confirmed content is output in the appropriate format and provided to the relevant parties.

[0707] (Application Example 2)

[0708] 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".

[0709] In today's business environment, it is essential to quickly and accurately grasp relevant legal and risk information. However, doing so manually is time-consuming, and its efficiency is significantly reduced, especially in situations of high emotional stress. Furthermore, there is a risk of misinterpretation of legal information, and it is necessary to avoid the resulting negative impact on business activities. Therefore, there is a need for methods of providing legal information that take into account the psychological state of the user.

[0710] 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.

[0711] In this invention, the server includes means for providing an interface for inputting business operations, means for searching for relevant legal and risk information based on the input business operations, means for using a generative model to generate suggested content based on the searched information, means for modifying the generated content based on company-specific requirements, means for analyzing the user's psychological state and adjusting feedback based on emotions, and means for communicating the final generated content. This makes it possible to provide legal information that is adapted to the user's emotional state in diverse business environments.

[0712] "Business operations" refer to a series of activities or tasks performed to achieve a specific objective.

[0713] An "interface" refers to the point of contact or method by which humans and machines exchange information.

[0714] "Legal information" refers to specific data and knowledge related to laws and regulations.

[0715] "Risk information" refers to information about potential dangers or uncertainties that may arise in specific circumstances.

[0716] A "generative model" is a computational method that creates new output data based on specific input data.

[0717] "Company-specific requirements" refer to the unique standards and requirements that a particular company possesses.

[0718] "Psychological state" refers to the state of an individual's inner emotions and thoughts.

[0719] "Feedback" means providing a response or evaluation to an action or its outcome.

[0720] "Communication" refers to the act or process of sending and receiving data and information.

[0721] To implement this invention, a system using a server and a terminal is first constructed. The user inputs work details through the terminal, and this input is transmitted to the server via an interface. At this time, the terminal is equipped with an emotion engine that analyzes the user's psychological state in real time, analyzing the user's facial expressions and voice tone. The server analyzes this data and uses the latest search technology to quickly retrieve relevant legal information and risk information.

[0722] The server utilizes RAG (Retrieval-Augmented Generation) technology to retrieve information related to the entered business content from a legal database in real time, and uses a generative AI model to generate appropriate suggestion content. This generative model provides information while considering the user's psychological state, and the generated content is further customized based on the company's specific requirements.

[0723] As a concrete example, a factory safety monitoring robot analyzes the emotional state of workers and presents instructions regarding safety procedures in an appropriate tone, thereby reducing worker stress and supporting efficient work. In this way, the system provides flexible legal information that adapts to the user's emotions.

[0724] Furthermore, the platform for realizing this system uses the Python programming language, with the speech_recognition module for speech recognition and a virtual EmotionEngine for sentiment analysis. The generated suggestion content is adjusted according to the company's policies, and the final content is provided as feedback to the user.

[0725] An example of a prompt to input into a generative AI model is: "Perform sentiment analysis during factory work and provide feedback to alleviate worker tension. Please provide specific examples of instructions."

[0726] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0727] Step 1:

[0728] The user inputs work details through the terminal's interface. The input work details are received by the terminal in text format. Here, the user inputs specific work details and questions in text.

[0729] Step 2:

[0730] The terminal transmits the entered work content to the emotion analysis engine in real time, analyzing the user's facial expressions and tone of voice. The emotion engine identifies the user's emotional state based on the input audio and text, and outputs the stress level and type of emotion. In this step, the user's psychological state is output as data from the engine as an analysis result.

[0731] Step 3:

[0732] The terminal transmits the user's psychological state, obtained through sentiment analysis, and the text-entered work content to the server. The server receives the work content and searches its database for relevant legal and risk information. Here, RAG technology is used to acquire and search for legal data. The input is the text of the work content, and the output is text data of the relevant legal information.

[0733] Step 4:

[0734] The server generates suggested content using a generative AI model based on the acquired information. This generative model derives the optimal content while considering the user's psychological state. The input here is legal data and the user's emotional information, and the output is customized suggested content for the user.

[0735] Step 5:

[0736] The server adjusts the generated suggestion content based on the company's specific requirements to create the final feedback content. This step utilizes the company's policy data for content customization. The input is the suggestion content, and the output is the company-tailored feedback content.

[0737] Step 6:

[0738] The server sends the final feedback content to the terminal and presents it to the user. The terminal displays the data received as feedback in the user interface, allowing the user to review it. In this step, the adjusted feedback is visually presented to the user as the final output.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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."

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] The following is further disclosed regarding the embodiments described above.

[0761] (Claim 1)

[0762] A means of providing a user interface for inputting work details,

[0763] A means of searching for relevant legal information and risk information based on the entered business details,

[0764] A means of using a generative model that generates suggested content based on the searched information,

[0765] Means for customizing generated content based on company-specific requirements,

[0766] A means of sending the final generated content,

[0767] A system that includes this.

[0768] (Claim 2)

[0769] The system according to claim 1, comprising an interface engine that provides guidelines for inputting work content, thereby assisting in the identification of work content.

[0770] (Claim 3)

[0771] The generative model is the system described in claim 1, which uses RAG technology to dynamically acquire information from the latest legal database.

[0772] "Example 1"

[0773] (Claim 1)

[0774] A means of providing a user screen for inputting work details,

[0775] A means of searching for relevant legal and risk information based on the entered business details,

[0776] A method using a generative model that generates suggested content based on acquired information,

[0777] Means for tailoring the generated content based on organization-specific requirements,

[0778] A means of sending the final generated content to the appropriate destination,

[0779] Means including an interface for users to review and modify content,

[0780] A system that includes this.

[0781] (Claim 2)

[0782] The system according to claim 1, wherein the user screen includes a screen engine that provides criteria for inputting work content, thereby assisting in the identification of work content.

[0783] (Claim 3)

[0784] The generation model is the system according to claim 1, which uses information acquisition technology to dynamically obtain information from the latest legal database.

[0785] "Application Example 1"

[0786] (Claim 1)

[0787] A means of providing a human-machine interface for inputting work details,

[0788] A data processing means for searching for relevant legal information and risk information based on the entered business content,

[0789] A method using a generative model that presents legal risk assessments and recommended action measures based on retrieved information,

[0790] A customization processing means for optimizing the generated content based on organization-specific standards,

[0791] A transmission processing means for electronically distributing generated content to relevant parties,

[0792] An information processing system that includes this.

[0793] (Claim 2)

[0794] The human-machine interface includes a user support engine that provides guidance for inputting work content, and the information processing system according to claim 1 assists in the precise identification of work content.

[0795] (Claim 3)

[0796] The generative model is an information processing system according to claim 1, which uses RAG technology to dynamically acquire digital information from the latest legal database.

[0797] "Example 2 of combining an emotion engine"

[0798] (Claim 1)

[0799] A means of providing an operation screen for inputting business information,

[0800] A means of searching for relevant laws and regulations and risk information based on the entered business information,

[0801] A method using a generative model that generates proposals based on the retrieved information,

[0802] Means for adapting the generated content based on the organization's specific requirements,

[0803] A means of recognizing the user's emotional state and adjusting the content to adapt to it,

[0804] A means of communicating the final generated content,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, wherein the operation screen is equipped with an engine that provides criteria for assisting in the input of business information.

[0808] (Claim 3)

[0809] The generative model is the system according to claim 1, which uses a technology to dynamically acquire information from the latest legal information sources.

[0810] "Application example 2 when combining with an emotional engine"

[0811] (Claim 1)

[0812] A means of providing an interface for inputting business operations,

[0813] A means of searching for relevant legal information and risk information based on the entered business data,

[0814] A means of using a generative model to generate proposals based on the information explored,

[0815] Means for modifying the generated content based on company-specific requirements,

[0816] A means of analyzing the user's psychological state and adjusting feedback based on emotions,

[0817] A means of communicating the final generated content,

[0818] A system that includes this.

[0819] (Claim 2)

[0820] The system according to claim 1, which includes an interface engine that provides guidance for business input and assists in identifying business operations.

[0821] (Claim 3)

[0822] The generative model is the system according to claim 1, which uses a search technique to dynamically acquire information from the latest legal data set. [Explanation of Symbols]

[0823] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of providing a user interface for inputting work details, A means of searching for relevant legal information and risk information based on the entered business details, A means of using a generative model that generates suggested content based on the searched information, Means for customizing generated content based on company-specific requirements, A means of sending the final generated content, A system that includes this.

2. The system according to claim 1, comprising an interface engine that provides guidelines for inputting work content, thereby assisting in the identification of work content.

3. The generative model is the system according to claim 1, which uses RAG technology to dynamically acquire information from the latest legal database.

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