system

Abstract

We provide the system. [Solution] Data collection methods for obtaining business data, A data analysis means for analyzing the collected data and identifying relevant legal information, A legal information matching means that obtains legal information from an external legal database and matches it with the identified legal information, Information presentation means for presenting information to the user based on the matching result, An automated generation and sending method for automatically generating and sending documents and emails necessary for business operations, A learning method that updates records based on user feedback and improves system accuracy, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern corporate activities, legal operations continue to become more complex, and particularly with the frequent amendment of laws and regulations and the diversification of business content, the burden on the legal department is increasing. Also, it is difficult for employees lacking legal knowledge to appropriately understand relevant laws and regulations and risk countermeasures and to perform their work, and as a result, legal risks may not be appropriately managed. There is a need for a technology to improve such a situation and enhance the accuracy and efficiency of legal responses.

Means for Solving the Problems

[0005] This invention relates to a system comprising data collection means, data analysis means, legal information matching means, information presentation means, automatic generation and transmission means, and learning means. It automatically collects business data and analyzes it using natural language processing technology to quickly identify relevant laws and risk factors. Furthermore, it acquires and matches the latest legal information by linking with an external legal database. Based on the analysis results, the information presentation means provides relevant information to the user in an easy-to-understand manner and automatically generates and sends necessary documents and emails. In addition, by learning from user feedback and improving the system's accuracy, it significantly improves the operational efficiency of the legal department.

[0006] A "data collection tool" is a component that executes the process of acquiring business data and has the function of gathering necessary information from internal systems and databases.

[0007] A "data analysis tool" is a component that analyzes collected data and extracts relevant legal information and risk factors from its content, and uses natural language processing technology to understand the meaning of documents.

[0008] A "legal information matching tool" is a component that has the function of identifying laws and regulations relevant to business operations by comparing legal information obtained from an external legal database with the analysis results.

[0009] An "information presentation tool" is a component that has an interface for displaying analysis results and legal information to the user in an appropriate format, and for providing the knowledge and countermeasures necessary for the job.

[0010] An "automatic generation and sending mechanism" is a component that has the function of automatically creating documents and emails necessary for business operations based on templates and sending them to the relevant parties.

[0011] A "learning tool" is a component that has the function of accumulating and analyzing user feedback and improving the accuracy and efficiency of the system based on that feedback. [Brief explanation of the drawing]

[0012] [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, when an emotion engine is combined. [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]

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a numbered 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, etc.

[0018] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] The AI ​​agent system of the present invention is realized by combining various advanced technologies to efficiently support a company's legal operations. This system is implemented in the following form.

[0034] First, the system initiates processes related to the task by having the user input or select data related to the task through a terminal. Next, the server accesses internal systems and databases to collect data related to the specified task. This data includes past work history and related documents, which the system analyzes.

[0035] The collected data is analyzed by the server using natural language processing technology. This analysis extracts keywords and contexts related to the business, and identifies relevant legal information and risk factors.

[0036] Simultaneously, the server accesses an external legal database to obtain the latest legal information. This information is then compared with the analysis results of the collected data, and the laws relevant to the business are extracted.

[0037] Through information presentation methods, the terminal clearly displays analysis results and relevant legal information to the user. This process helps users understand the knowledge and measures necessary to carry out their work.

[0038] Furthermore, the server automatically generates necessary documents and emails based on templates using an automated generation and transmission mechanism, and these are sent to the relevant parties. Users can preview the generated documents on their terminal, review them as needed, and then send them.

[0039] Furthermore, user feedback is sent to the server via the terminal. The server uses this feedback as a learning tool to improve the system's accuracy. Specifically, it incorporates user feedback to improve the accuracy of subsequent analyses, information presentations, and document generation.

[0040] For example, this system can be used when a company enters into a new contract. The user inputs an overview of the contract details from a terminal, and the server identifies the relevant laws and regulations based on that input. The analysis results, including legal information and risk mitigation measures, are presented to the terminal, and the necessary contract documents are automatically generated and sent. This enables efficient contract procedures while minimizing legal risks.

[0041] The system of the present invention, configured as described above, reduces the burden of legal work and supports accurate and efficient work execution.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user inputs or selects work-related data through their terminal. This sends a request for analysis of the work content to the system.

[0045] Step 2:

[0046] The server accesses internal systems and databases to collect data related to the specified tasks. The collected data includes past work history and related documents.

[0047] Step 3:

[0048] The server uses natural language processing technology to analyze the collected data. This analysis process extracts keywords and contexts relevant to the business and identifies relevant legal information and risk factors.

[0049] Step 4:

[0050] The server accesses an external legal database to retrieve the latest legal information. The retrieved legal information is then compared with the analysis results to extract the relevant laws and regulations.

[0051] Step 5:

[0052] The device presents the user with analysis results and related legal information. The information is displayed in an easy-to-understand format, enabling the user to grasp the necessary knowledge and countermeasures.

[0053] Step 6:

[0054] The server uses an automated generation and sending mechanism to generate the necessary documents and emails based on templates. The generated documents and emails are then provided to the user as a preview via their terminal.

[0055] Step 7:

[0056] The user reviews the document generated on their device and makes corrections or approvals as needed. After approval, the document or email is automatically sent to the relevant parties.

[0057] Step 8:

[0058] User feedback is sent to the server via the terminal. The server uses this feedback to learn and improve the overall accuracy and efficiency of the system.

[0059] (Example 1)

[0060] 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."

[0061] In modern business operations, it is crucial to perform legal tasks quickly and accurately. However, legal work requires collecting and analyzing a large amount of information and verifying relevant legal information based on that information, which is time-consuming and labor-intensive when done manually. Furthermore, it is difficult to keep up with the latest legal information, making it challenging to take appropriate measures to avoid legal risks. Therefore, there is a need for effective systems that streamline operations and reduce legal risks.

[0062] 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.

[0063] In this invention, the server includes data collection means for acquiring business information, data analysis means for analyzing the collected information using natural language processing technology to identify relevant normative information, and normative information matching means for acquiring normative information from an external normative database and matching it with the identified normative information. This makes it possible to automatically collect and analyze information necessary for legal work and to quickly and accurately confirm relevant legal information. As a result, legal risks are reduced and operational efficiency is improved.

[0064] "Business information" refers to information related to the daily business activities of a company or organization, and is a general term for data including contract details, their history, and related documents.

[0065] "Data collection methods" refer to the methods and techniques for collecting necessary data from various sources, and include mechanisms such as access to databases and internal records.

[0066] "Natural language processing technology" is a type of artificial intelligence technology that uses computers to analyze human language and understand its meaning and context, and is used for analyzing text data.

[0067] "Non-standard information" refers to information about laws, regulations, and rules related to business operations, and is used to determine whether a company's activities comply with the law.

[0068] "Data analysis means" refers to methods and techniques for analyzing collected data to identify important information and patterns within it, and includes natural language processing.

[0069] A "matching method" refers to a method or technology for determining a relationship between identified information and related information obtained from external sources by comparing and matching them.

[0070] "Information presentation means" refers to methods and devices for clearly presenting analysis results and related information to users, and includes visual interfaces.

[0071] "Automatic generation and transmission means" refers to methods and technologies for automatically creating necessary documents and communications based on templates and sending them to relevant parties.

[0072] "Learning methods" refer to methods and techniques for improving the accuracy and performance of a system based on feedback from users, and include machine learning.

[0073] The AI ​​agent system of the present invention is designed to support the efficiency of legal work and risk management, and is implemented as follows: First, the user inputs information related to the work via a terminal. This information includes contract details and summaries. The terminal is typically a common computer hardware such as a personal computer or tablet, and a form on a web browser is used as the input interface.

[0074] Next, the server collects relevant business information from internal systems and databases based on the input information. The server requires high-speed data processing capabilities and large-capacity storage, and uses common data management software such as SQL databases. The collected information is processed using natural language processing by a generative AI model to extract important keywords and context. This generative AI model can be implemented, for example, by implementing a Python natural language processing library.

[0075] Furthermore, the server accesses an external normative database to obtain the latest normative information. By using an API to retrieve updated legal data, the most up-to-date legal information is always reflected. By comparing this information with the analysis results, the laws and regulations relevant to the business are identified.

[0076] Subsequently, the terminal visually presents the user with analysis results and relevant legal information. The displayed information is organized in a dashboard format and designed to be easily understood by the user.

[0077] As part of this process, the server automatically generates necessary documents and communications based on templates and provides a means to send them to relevant parties. This functionality allows for the efficient processing of contractual documents and risk management notices.

[0078] Furthermore, users can review documents generated on their devices, approve their contents, and then submit them. This review process allows users to easily perform legal review tasks within the system.

[0079] As a concrete example, when a user enters a prompt such as "Identify legal information for a new contract case," the system automatically collects and analyzes information related to this case and presents the necessary legal information, thereby supporting rapid decision-making. In this way, the system described in the present invention reduces the burden on legal affairs work for companies and enables efficient work execution.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1: The user enters work-related information through the terminal. Here, the contract summary and specific requirements are entered in text format into the input fields. The entered data is sent directly to the server and becomes the basis for the next processing step.

[0082] Step 2: The server accesses the internal database based on the received business information and collects relevant records and documents. Specifically, it executes database queries to retrieve contract history and related past data. This further deepens the business information and retrieves relevant data.

[0083] Step 3: The server analyzes the collected data using a generative AI model. Here, natural language processing technology is utilized to extract keywords and context from business information. The server receives the collected data as input and outputs important business-related information as the analysis result.

[0084] Step 4: The server accesses an external standards database to retrieve the latest standards information. It sends requests to the legal database via an API to retrieve relevant legal information, ensuring that it always reflects the most up-to-date legal information. In this step, the server identifies the standards information relevant to the business based on the information obtained from the external database.

[0085] Step 5: The terminal visually presents the analysis results and normative information received from the server to the user. The presented information is organized in a dashboard format and displayed in a way that is easy for the user to understand. Here, the user can make decisions based on the necessary information.

[0086] Step 6: The server automatically generates the necessary documents and communications based on templates using an automated generation and transmission method, and prepares them for transmission to the relevant parties. Automation is achieved by utilizing a generation AI model to create draft contract documents and emails and export them in the specified format.

[0087] Step 7: The user previews the document generated on the terminal and reviews its content. They enter feedback and make corrections as needed, and then submit it to the relevant parties after final confirmation. This step is the final review, and the feedback is sent to the server and used to improve accuracy in future projects.

[0088] (Application Example 1)

[0089] 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."

[0090] In modern electronic transactions, users face challenges in maintaining compliance with frequently changing laws and regulations. While it is essential to quickly and accurately grasp relevant legal information and take appropriate action, traditional manual processes and systems have limitations, leaving users constantly at risk of inadvertently disregarding regulations. In this context, there is a need for a means to proactively identify legal risks associated with transactions and provide appropriate information in real time.

[0091] 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.

[0092] In this invention, the server includes information gathering means for acquiring business data, information analysis means for analyzing the collected information and identifying relevant regulatory information, regulatory information matching means for acquiring legal information from an external legal database and matching it with the identified regulatory information, and transaction regulatory analysis means for collecting transaction data, analyzing the regulatory risks of transactions, and providing legal information to users in advance. This enables users to grasp legal risks more accurately and take necessary actions appropriately and quickly.

[0093] "Business data" refers to information about all transactions and business processes in a company's activities, including transaction history and contract details.

[0094] "Information gathering means" refers to processes or systems for efficiently acquiring necessary data, and may include access to internal and external databases.

[0095] "Information analysis means" refers to the techniques and processes used to analyze collected data and extract important information and patterns from it.

[0096] "Regulatory information" refers to information about laws and regulations that apply to specific business operations or transactions.

[0097] "Regulatory information matching means" refers to a function or system for comparing acquired legal information with analyzed data and identifying matching sections.

[0098] "Information display means" refers to a mechanism or device for presenting analysis results in a way that is easy for users to understand.

[0099] "Automated generation and distribution means" refers to a process or system for automatically creating business-related documents and emails and sending them to relevant parties.

[0100] "Learning methods" refer to mechanisms that receive feedback from users and use that information to improve the system's performance and accuracy.

[0101] "Transaction regulatory analysis tools" refer to means for identifying legal risks from transaction data and providing users with appropriate legal information based on those findings.

[0102] To realize this invention, an AI agent system plays a central role in efficiently collecting and analyzing business data and providing users with appropriate legal information. The server acquires business data from various data sources using information collection means. Specific data sources include internal databases and external legal databases, enabling comprehensive information collection.

[0103] The server then analyzes the data collected using information analysis tools with advanced natural language processing techniques. In this process, natural language processing tools and AI models play a crucial role in identifying relevant regulatory information and potential legal risks. The analyzed information is then cross-referenced with the latest legal information obtained from external legal databases using regulatory information matching tools. This matching process confirms that the identified risk elements are consistent with applicable laws and regulations.

[0104] Furthermore, the server uses transaction regulatory analysis tools to analyze transaction data, particularly in electronic payment services, and provides users with real-time legal information related to transactions. This allows users to proactively prevent legal risks. In addition, related documents and communications are automatically generated and distributed to relevant parties using automated generation and distribution tools. All of these processes are continuously improved through user feedback, and the overall accuracy of the system is enhanced through learning mechanisms.

[0105] For example, if a user is trying to purchase an overseas product, this system will analyze information on import regulations and related tax laws for that product and present the necessary measures to the user. An example of a prompt message would be, "Transaction: Please analyze the latest import regulations and related laws based on User ID 123, Amount 5000 JPY, Product ID 456." This allows users to proceed with transactions with confidence.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The server receives transaction-related input from users using information gathering methods to acquire business data. This input includes detailed information such as user ID, amount, and product ID. Based on the collected business data, the server searches relevant internal and external databases and aggregates the necessary information.

[0109] Step 2:

[0110] The server processes the collected business data and related information using information analysis tools. This process utilizes generative AI models and natural language processing techniques to analyze the data and identify relevant regulatory information and risk factors. The analysis results are output as data and passed on to the next processing step.

[0111] Step 3:

[0112] The server uses regulatory information matching means to compare the analysis results with legal information obtained from an external legal database. In this process, the analysis data is matched with the latest regulatory information, and the relevant laws are identified. The matching regulatory information is output and ready to be provided to the user.

[0113] Step 4:

[0114] The server analyzes transaction data in detail, particularly in electronic payment services, through transaction regulatory analysis tools. Based on this analysis, legal information and risk factors related to the user's transactions are identified, and the data is processed so that users can receive this information in real time.

[0115] Step 5:

[0116] The server uses information display means to present analysis results and legal information to the user. In this step, advice regarding legal risks and regulations is displayed on the user's terminal, and the user can receive this information in an easy-to-understand format.

[0117] Step 6:

[0118] The server utilizes automated generation and distribution methods to generate necessary documents and emails for business operations and send them to relevant parties. In this process, appropriate documents are automatically created based on collected and analyzed information and securely delivered to the specified recipients.

[0119] Step 7:

[0120] The user reviews the presented information and generated documents, and sends their feedback to the server. The server uses learning mechanisms to perform data calculations based on the received feedback to improve the system's accuracy, and uses this information for future analyses.

[0121] 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.

[0122] This invention is implemented in the form of an AI agent system that aims to streamline corporate legal operations, combined with an emotion engine that recognizes user emotions. This system not only analyzes business data and provides legal information, but also analyzes user emotions and presents information based on those emotions, thereby providing more personalized support.

[0123] First, the user inputs or selects business data through a terminal. The server then uses this data to collect relevant business information from internal systems and databases.

[0124] Once the data is collected, the server analyzes it using natural language processing technology. In this process, relevant legal information and risk factors are identified, and the latest legal information is retrieved from an external legal database and compared with the collected data.

[0125] Furthermore, the server analyzes user input and interaction data obtained from the terminal to recognize the user's emotions. The emotion engine identifies emotions from the user's text input, voice data, and behavioral data, and adjusts what information the system presents and how.

[0126] Through the information presentation mechanism, the terminal presents the user with analysis results and related legal information. In this process, the information provided to the user is adjusted by an emotion engine. For example, for users experiencing stress, the system optimizes the display to show particularly important information concisely.

[0127] Furthermore, using automated generation and transmission methods, the server automatically generates necessary documents and emails. These generated documents are provided to the user as a preview on their terminal, allowing the user to review the content and make any necessary corrections or approvals.

[0128] User feedback is collected through the device and sent to the server along with sentiment data. The server uses this feedback to reinforce the learning process and improve the system's accuracy and effectiveness.

[0129] As a concrete example, consider a scenario where a legal professional uses this system to conduct a risk assessment before the market launch of a new product. When the professional inputs business data, the server immediately identifies relevant laws and regulations, and the emotion engine senses the user's stress level. Accordingly, information that concisely summarizes explanations of complex laws and regulations is provided to the terminal. Necessary approval documents are automatically generated and sent to the relevant parties after user confirmation. Through this process, the present invention supports the rapid execution of legal work and improves the user experience.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The user inputs or selects business data using a terminal. This action sends the business information to the system, initiating the analysis process.

[0133] Step 2:

[0134] The server accesses internal systems and databases to collect data related to the specified task. This data includes past work history and related documents.

[0135] Step 3:

[0136] The server uses natural language processing technology to analyze the collected data. This analysis extracts keywords and contexts related to the business, and based on this, legal information and risk factors are identified.

[0137] Step 4:

[0138] The server accesses an external legal database to retrieve the latest legal information. The retrieved legal information is then compared with the analysis results, and the laws relevant to the business are extracted.

[0139] Step 5:

[0140] The device sends user input data to the emotion engine, which analyzes the user's emotions. The emotion engine identifies the user's emotional state and adjusts the information presented based on that state.

[0141] Step 6:

[0142] The information presentation mechanism allows the terminal to present analysis results and related legal information to the user. This information is displayed in a format adjusted by the emotion engine; for example, important information is presented concisely to users experiencing high stress levels.

[0143] Step 7:

[0144] The server utilizes automated generation and sending mechanisms to generate necessary documents and emails based on templates. The generated documents and emails are then provided to the user as a preview via their terminal.

[0145] Step 8:

[0146] The user reviews the generated document on their device and makes corrections or approvals as needed. Approved documents and emails are then sent to the relevant parties by the server.

[0147] Step 9:

[0148] The device collects user feedback and sends it to the server along with sentiment data. The server uses this feedback as a learning tool to improve the overall accuracy of the system.

[0149] (Example 2)

[0150] 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".

[0151] Traditional business support systems have faced challenges in legal work, where large amounts of information must be processed. These systems are inefficient in organizing and presenting information, and they fail to consider the user's feelings when providing information. As a result, users often experience stress when understanding information or making decisions.

[0152] 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.

[0153] In this invention, the server includes data collection means for acquiring information related to business operations, data analysis means for analyzing the collected information and identifying relevant regulatory information, and emotion recognition means for recognizing the user's emotions and adjusting the analysis results. This enables efficient information presentation that takes the user's emotions into consideration.

[0154] "Information related to business operations" refers to all data and information related to the operations carried out by a company or organization, and this includes information for compliance with laws and regulations.

[0155] "Data collection means" refers to the means by which a system acquires information entered or selected by a user and aggregates the necessary business data.

[0156] "Data analysis methods" refer to means of analyzing collected information using natural language processing technology and other methods to identify relevant regulatory information and risk factors.

[0157] A "regulatory information matching means" is a means for matching regulatory information obtained from an external regulatory database with information identified internally.

[0158] An "information presentation means" is a means of displaying information to the user based on analysis results and providing necessary documents and communications in an easy-to-understand format.

[0159] An "automatic generation and transmission method" is a means of automatically creating documents and emails necessary for business operations and sending them to relevant parties.

[0160] "Learning methods" are means of improving the functionality and accuracy of a system based on feedback obtained from users.

[0161] "Emotion recognition means" are tools for analyzing a user's emotional state based on their text input and interaction data, and for adjusting the information presented accordingly.

[0162] This invention aims to streamline information management in business support systems and reduce the burden on users. The system operates primarily through the interaction of servers, terminals, and users.

[0163] First, the user uses their device to input or select work-related information. In this process, the work-related information is imported into the system and proceeds to the next analysis step. The devices used are expected to be general-purpose computers or smartphones.

[0164] The server receives collected business information and performs analysis using natural language processing techniques with data analysis tools. Specifically, it uses libraries such as Python's NLTK and spaCy to perform semantic analysis and keyword extraction of the information, and then identifies relevant regulatory information based on the identified information. The server also accesses external regulatory databases (e.g., government legal databases) to obtain the latest information and compare it with internal data.

[0165] The emotion recognition system also functions within the server, analyzing user input and interaction data. The user's text and voice data are analyzed using Google Cloud's Natural Language API to identify stress levels and emotional states. Based on these results, the server provides the user with tailored information through an information presentation system. This information is presented on the terminal in a clear and organized manner, incorporating features designed to reduce stress.

[0166] Furthermore, the server uses an automated generation and transmission mechanism to automatically create and finalize the necessary documents and emails for business operations. Users preview the generated documents on their terminals, review the details, make any necessary changes, and finally send them.

[0167] In this invention, user feedback is crucial. This feedback is collected via a terminal, transmitted to a server, and used to improve the system through a learning mechanism.

[0168] As a concrete example, consider the case of verifying legal compliance when launching a new product into the market. The user enters instructions into the system as prompts, such as "Identify the laws and regulations related to the market launch risk assessment of the new product," or "Present important legal information concisely, taking into consideration the user's feelings," and starts the process. This form of system achieves both the rapid execution of legal tasks and an improved user experience.

[0169] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0170] Step 1:

[0171] Users input business-related data using a terminal. In this input process, users provide specific information to the terminal according to their business objectives, such as information regarding the market launch of a new product. The output is business data necessary for the server to use in subsequent processing.

[0172] Step 2:

[0173] The server receives the input data and, via data collection mechanisms, gathers relevant internal information and similar past case data from the internal database. Specifically, it executes database queries to obtain the necessary data and outputs it as an initial list of data to be analyzed.

[0174] Step 3:

[0175] The server analyzes business data using natural language processing techniques. In this step, Python's NLTK and spaCy libraries are used to analyze the input data and extract important keywords. The input is business data provided by the user, and the output is the analyzed information and related regulatory information.

[0176] Step 4:

[0177] The server accesses an external regulatory database to retrieve the latest legal information and compares it with the previously identified information. Specifically, it uses an API to query information from the external database and retrieve regulatory information. After the comparison, the output is the legal information that has been adjusted.

[0178] Step 5:

[0179] To recognize a user's emotions, the server performs sentiment analysis using user text and interaction data obtained from the device. It uses Google Cloud's Natural Language API to determine the user's stress level and emotions. The input is user text and interaction data, and the output is the user's emotional state.

[0180] Step 6:

[0181] The device analyzes and recognizes emotions from the data, then presents the user with adjusted information. Here, information presentation methods are used to display the information in a way that is easy for the user to understand. The input consists of analyzed data and emotion data, while the output is concise and personalized information presented to the user.

[0182] Step 7:

[0183] The server uses an automated generation and sending mechanism to generate the necessary documents and emails, and provides a preview on the user's terminal. The user reviews this on the terminal, makes any necessary corrections, and then issues a final sending instruction. The input is the generated document data, and the output is the reviewed and approved document.

[0184] Step 8:

[0185] Feedback is collected from users via their devices and sent to a server. The server uses this feedback to improve the accuracy and effectiveness of the system through learning mechanisms. The input is user feedback and sentiment data, and the output is an augmented learning algorithm.

[0186] (Application Example 2)

[0187] 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".

[0188] In current in-store customer service, it is difficult to properly understand customer emotions and provide services accordingly. This can lead to decreased customer satisfaction and lost sales opportunities. The goal is to solve this problem and achieve more personalized customer service.

[0189] 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.

[0190] In this invention, the server includes data acquisition means for obtaining business information, data analysis means for analyzing the acquired information and identifying relevant rule information, matching means for obtaining rule information from an external rule database and aligning it with the identified rule information, and emotion analysis means for analyzing the user's emotions and optimizing information provision. This makes it possible to grasp the customer's emotional state in real time and provide information and customer service accordingly.

[0191] "Data acquisition means" refers to the mechanisms and techniques used to acquire business information.

[0192] "Data analysis means" refers to mechanisms and techniques for analyzing acquired business information and identifying relevant rule information.

[0193] A "matching method" refers to a mechanism or technique for aligning regulatory information obtained from an external regulatory database with specified rule information.

[0194] "Information display means" refers to mechanisms or techniques that display relevant information to users based on matching results.

[0195] "Generation and transmission means" refers to mechanisms and techniques for automatically generating and transmitting documents and communications necessary for business operations.

[0196] A "learning method" refers to a mechanism or technique aimed at improving system accuracy by updating records based on user feedback.

[0197] "Emotional analysis tools" refer to mechanisms and techniques for analyzing users' emotions and optimizing information provision.

[0198] A system for carrying out this invention includes a smart device and a server to support customer service operations in a physical store. As an example of a smart device, smart glasses are used. These smart glasses are equipped with a camera and a microphone to capture the customer's facial expressions and voice.

[0199] The server receives data transmitted from smart glasses and collects business information using data acquisition means. Subsequently, data analysis means analyze this data to identify relevant regulatory information. Natural language processing technology is used for the analysis. After obtaining the necessary regulatory information from an external regulatory database, matching means integrate this information.

[0200] To analyze user emotions, the server uses emotion analysis tools to identify customer emotions from acquired data. For example, it may use facial recognition and emotion analysis libraries (e.g., OpenCV, Affectiva API) to identify emotions from facial expressions and voice.

[0201] The information display system shows relevant information on smart glasses based on matching and sentiment analysis results. For example, it displays product information and campaign information that the customer might be interested in.

[0202] Furthermore, the generation and transmission mechanism allows for the automatic generation of necessary documents and communications for business operations and their transmission to relevant parties. User feedback is recorded through a learning mechanism, and the system's accuracy is improved based on this feedback.

[0203] As a concrete example, consider a scenario where a customer shows interest in a new electronic product. In this case, the smart glasses display detailed specifications and reviews of the product. If the system analyzes that the customer is excited or interested, it will also provide information that highlights the product's special features and limited-time offers.

[0204] An example of a prompt for the AI ​​model is: "Consider the customer's current emotions and decide what information should be displayed on the smart glasses. The customer's area of ​​interest is new electronic products. Emotion analysis result: Excitement, curiosity." Using this prompt, more effective customer service can be provided.

[0205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0206] Step 1:

[0207] The server receives customer facial expression and voice data transmitted from smart glasses. Based on this input data, it uses data acquisition methods to extract business information such as basic customer information and past purchase history from the company's internal database. The output consists of various business-related information necessary for analysis.

[0208] Step 2:

[0209] The server uses data analysis tools to perform natural language processing and sentiment analysis on the received facial expression and voice data. The input data is analyzed using sentiment recognition libraries (e.g., OpenCV, Affectiva API) to identify the customer's current emotional state. The output is the customer's emotion, such as excitement or curiosity.

[0210] Step 3:

[0211] The server uses data analysis tools to extract relevant regulatory information from the acquired business data. Next, it retrieves the latest regulatory information from an external regulatory database and matches it using matching tools. The input data consists of business information and regulatory information, and the output is the result of identifying the relevant information.

[0212] Step 4:

[0213] The server uses an information display mechanism to transmit information based on the identified results and sentiment analysis results to the smart glasses. Using the prompt text generated at this stage, the generating AI model presents information optimized for the customer. The input is sentiment and rule information, and the output is a customized information display for the customer.

[0214] Step 5:

[0215] The user (store clerk) interacts with customers based on information displayed on smart glasses. Customer reactions and feedback become new inputs, which are sent to the server and recorded by a generation and transmission system. The output is feedback data that helps improve the service.

[0216] Step 6:

[0217] The server learns from feedback to improve the accuracy of sentiment analysis and information presentation. The learning process is used to enhance the overall system performance for future customer interactions. Input consists of feedback data and feedback content, while output is the updated analysis model.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] [Second Embodiment]

[0222] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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".

[0234] The AI ​​agent system of the present invention is realized by combining various advanced technologies to efficiently support a company's legal operations. This system is implemented in the following form.

[0235] First, the system initiates processes related to the task by having the user input or select data related to the task through a terminal. Next, the server accesses internal systems and databases to collect data related to the specified task. This data includes past work history and related documents, which the system analyzes.

[0236] The collected data is analyzed by the server using natural language processing technology. This analysis extracts keywords and contexts related to the business, and identifies relevant legal information and risk factors.

[0237] Simultaneously, the server accesses an external legal database to obtain the latest legal information. This information is then compared with the analysis results of the collected data, and the laws relevant to the business are extracted.

[0238] Through information presentation methods, the terminal clearly displays analysis results and relevant legal information to the user. This process helps users understand the knowledge and measures necessary to carry out their work.

[0239] Furthermore, the server automatically generates necessary documents and emails based on templates using an automated generation and transmission mechanism, and these are sent to the relevant parties. Users can preview the generated documents on their terminal, review them as needed, and then send them.

[0240] Furthermore, user feedback is sent to the server via the terminal. The server uses this feedback as a learning tool to improve the system's accuracy. Specifically, it incorporates user feedback to improve the accuracy of subsequent analyses, information presentations, and document generation.

[0241] For example, this system can be used when a company enters into a new contract. The user inputs an overview of the contract details from a terminal, and the server identifies the relevant laws and regulations based on that input. The analysis results, including legal information and risk mitigation measures, are presented to the terminal, and the necessary contract documents are automatically generated and sent. This enables efficient contract procedures while minimizing legal risks.

[0242] The system of the present invention, configured as described above, reduces the burden of legal work and supports accurate and efficient work execution.

[0243] The following describes the processing flow.

[0244] Step 1:

[0245] The user inputs or selects work-related data through their terminal. This sends a request for analysis of the work content to the system.

[0246] Step 2:

[0247] The server accesses internal systems and databases to collect data related to the specified tasks. The collected data includes past work history and related documents.

[0248] Step 3:

[0249] The server uses natural language processing technology to analyze the collected data. This analysis process extracts keywords and contexts relevant to the business and identifies relevant legal information and risk factors.

[0250] Step 4:

[0251] The server accesses an external legal database to retrieve the latest legal information. The retrieved legal information is then compared with the analysis results to extract the relevant laws and regulations.

[0252] Step 5:

[0253] The device presents the user with analysis results and related legal information. The information is displayed in an easy-to-understand format, enabling the user to grasp the necessary knowledge and countermeasures.

[0254] Step 6:

[0255] The server uses an automated generation and sending mechanism to generate the necessary documents and emails based on templates. The generated documents and emails are then provided to the user as a preview via their terminal.

[0256] Step 7:

[0257] The user reviews the document generated on their device and makes corrections or approvals as needed. After approval, the document or email is automatically sent to the relevant parties.

[0258] Step 8:

[0259] User feedback is sent to the server via the terminal. The server uses this feedback to learn and improve the overall accuracy and efficiency of the system.

[0260] (Example 1)

[0261] 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."

[0262] In modern business operations, it is crucial to perform legal tasks quickly and accurately. However, legal work requires collecting and analyzing a large amount of information and verifying relevant legal information based on that information, which is time-consuming and labor-intensive when done manually. Furthermore, it is difficult to keep up with the latest legal information, making it challenging to take appropriate measures to avoid legal risks. Therefore, there is a need for effective systems that streamline operations and reduce legal risks.

[0263] 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.

[0264] In this invention, the server includes data collection means for acquiring business information, data analysis means for analyzing the collected information using natural language processing technology to identify relevant normative information, and normative information matching means for acquiring normative information from an external normative database and matching it with the identified normative information. This makes it possible to automatically collect and analyze information necessary for legal work and to quickly and accurately confirm relevant legal information. As a result, legal risks are reduced and operational efficiency is improved.

[0265] "Business information" refers to information related to the daily business activities of a company or organization, and is a general term for data including contract details, their history, and related documents.

[0266] "Data collection methods" refer to the methods and techniques for collecting necessary data from various sources, and include mechanisms such as access to databases and internal records.

[0267] "Natural language processing technology" is a type of artificial intelligence technology that uses computers to analyze human language and understand its meaning and context, and is used for analyzing text data.

[0268] "Non-standard information" refers to information about laws, regulations, and rules related to business operations, and is used to determine whether a company's activities comply with the law.

[0269] "Data analysis means" refers to methods and techniques for analyzing collected data to identify important information and patterns within it, and includes natural language processing.

[0270] A "matching method" refers to a method or technology for determining a relationship between identified information and related information obtained from external sources by comparing and matching them.

[0271] "Information presentation means" refers to methods and devices for clearly presenting analysis results and related information to users, and includes visual interfaces.

[0272] "Automatic generation and transmission means" refers to methods and technologies for automatically creating necessary documents and communications based on templates and sending them to relevant parties.

[0273] "Learning methods" refer to methods and techniques for improving the accuracy and performance of a system based on feedback from users, and include machine learning.

[0274] The AI ​​agent system of the present invention is designed to support the efficiency of legal work and risk management, and is implemented as follows: First, the user inputs information related to the work via a terminal. This information includes contract details and summaries. The terminal is typically a common computer hardware such as a personal computer or tablet, and a form on a web browser is used as the input interface.

[0275] Next, the server collects relevant business information from internal systems and databases based on the input information. The server requires high-speed data processing capabilities and large-capacity storage, and uses common data management software such as SQL databases. The collected information is processed using natural language processing by a generative AI model to extract important keywords and context. This generative AI model can be implemented, for example, by implementing a Python natural language processing library.

[0276] Furthermore, the server accesses an external normative database to obtain the latest normative information. By using an API to retrieve updated legal data, the most up-to-date legal information is always reflected. By comparing this information with the analysis results, the laws and regulations relevant to the business are identified.

[0277] Subsequently, the terminal visually presents the user with analysis results and relevant legal information. The displayed information is organized in a dashboard format and designed to be easily understood by the user.

[0278] As part of this process, the server automatically generates necessary documents and communications based on templates and provides a means to send them to relevant parties. This functionality allows for the efficient processing of contractual documents and risk management notices.

[0279] Furthermore, users can review documents generated on their devices, approve their contents, and then submit them. This review process allows users to easily perform legal review tasks within the system.

[0280] As a specific example, when a user inputs a prompt sentence "Identify the legal information of a new contract case", the system automatically collects and analyzes the information related to this case, and presents the necessary legal information, thereby supporting rapid decision-making. In this way, the system shown in the present invention realizes the reduction of the burden in the legal affairs of enterprises and the efficient implementation of business operations.

[0281] The flow of the specific process in Example 1 will be described with reference to FIG. 11.

[0282] Step 1: The user inputs information related to the business through the terminal. Here, the outline and specific requirements of the contract are input in text form in the input field. The input data is directly transmitted to the server, which serves as the basic data for the next processing step.

[0283] Step 2: Based on the received business information, the server accesses the in-house database and collects relevant records and documents. Specifically, it executes a database query to obtain contract history and past related data. As a result, the business information is further explored and related data is obtained.

[0284] Step 3: The server analyzes the collected data using a generative AI model. Here, natural language processing technology is utilized to extract keywords and context from the business information. Taking the collected data as input, important information related to the business is output as the analysis result.

[0285] Step 4: The server accesses the external regulatory database to obtain the latest regulatory information. By sending a request to the legal database through the API and obtaining relevant regulatory information, the latest legal information is always reflected. In this step, based on the information obtained from the external database, the regulatory information related to the business is identified.

[0286] Step 5: The terminal visually presents the analysis results and the specification information received from the server to the user. The presented information is organized in a dashboard format and displayed so that the user can easily understand it. Here, the user can make a judgment based on the necessary information.

[0287] Step 6: The server uses the automatic generation and transmission means to automatically generate the necessary documents and communications based on templates and prepares to send them to the relevant parties. By leveraging the generation AI model to create contract documents and email drafts and exporting them in a specified format, automation is achieved.

[0288] Step 7: The user previews the document generated on the terminal, checks the content. Inputs feedback or corrections as needed, and sends it to the relevant parties after confirmation. In this step, the final confirmation is made, the feedback is sent to the server, and it is used to improve the accuracy in subsequent times.

[0289] (Application Example 1)

[0290] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0291] In modern electronic transactions, it is difficult for users to maintain compliance based on frequently changing laws and regulations. Although it is required to quickly and accurately grasp the relevant legal information and take appropriate measures, there are limitations in conventional manual operations and systems, so there is always a risk that users will accidentally ignore the regulations. In such a situation, a means to recognize the legal risks associated with transactions in advance and provide appropriate information in real time is necessary.

[0292] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0293] In this invention, the server includes information gathering means for acquiring business data, information analysis means for analyzing the collected information and identifying relevant regulatory information, regulatory information matching means for acquiring legal information from an external legal database and matching it with the identified regulatory information, and transaction regulatory analysis means for collecting transaction data, analyzing the regulatory risks of transactions, and providing legal information to users in advance. This enables users to grasp legal risks more accurately and take necessary actions appropriately and quickly.

[0294] "Business data" refers to information about all transactions and business processes in a company's activities, including transaction history and contract details.

[0295] "Information gathering means" refers to processes or systems for efficiently acquiring necessary data, and may include access to internal and external databases.

[0296] "Information analysis means" refers to the techniques and processes used to analyze collected data and extract important information and patterns from it.

[0297] "Regulatory information" refers to information about laws and regulations that apply to specific business operations or transactions.

[0298] "Regulatory information matching means" refers to a function or system for comparing acquired legal information with analyzed data and identifying matching sections.

[0299] "Information display means" refers to a mechanism or device for presenting analysis results in a way that is easy for users to understand.

[0300] "Automated generation and distribution means" refers to a process or system for automatically creating business-related documents and emails and sending them to relevant parties.

[0301] "Learning methods" refer to mechanisms that receive feedback from users and use that information to improve the system's performance and accuracy.

[0302] "Transaction regulatory analysis tools" refer to means for identifying legal risks from transaction data and providing users with appropriate legal information based on those findings.

[0303] To realize this invention, an AI agent system plays a central role in efficiently collecting and analyzing business data and providing users with appropriate legal information. The server acquires business data from various data sources using information collection means. Specific data sources include internal databases and external legal databases, enabling comprehensive information collection.

[0304] The server then analyzes the data collected using information analysis tools with advanced natural language processing techniques. In this process, natural language processing tools and AI models play a crucial role in identifying relevant regulatory information and potential legal risks. The analyzed information is then cross-referenced with the latest legal information obtained from external legal databases using regulatory information matching tools. This matching process confirms that the identified risk elements are consistent with applicable laws and regulations.

[0305] Furthermore, the server uses transaction regulatory analysis tools to analyze transaction data, particularly in electronic payment services, and provides users with real-time legal information related to transactions. This allows users to proactively prevent legal risks. In addition, related documents and communications are automatically generated and distributed to relevant parties using automated generation and distribution tools. All of these processes are continuously improved through user feedback, and the overall accuracy of the system is enhanced through learning mechanisms.

[0306] As a specific example, when there is a user who is trying to purchase an overseas product, this system analyzes information on import regulations for the relevant product and related tax laws, and presents necessary measures to the user. As an example of a prompt sentence, it is input in the form of "Transaction: Analyze the latest import regulations and related laws based on User ID 123, amount 5000 JPY, and Product ID 456." This enables the user to proceed with the transaction with confidence.

[0307] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0308] Step 1:

[0309] The server receives an input related to a transaction from the user using an information collection means for acquiring business data. This input includes detailed information such as user ID, amount, and product ID. Based on the collected business data, it searches related internal and external databases and aggregates the necessary information.

[0310] Step 2:

[0311] The server processes the collected business data and related information using an information analysis means. In this process, a generated AI model is used to analyze the data with natural language processing technology and identify relevant regulatory information and risk factors. The analysis result is output as data and passed to the next processing step.

[0312] Step 3:

[0313] The server uses a regulatory information verification means to verify the analysis result against the regulatory information obtained from an external legal database. In this process, the analysis data is matched with the latest regulatory information, and the corresponding laws are identified. The matched regulatory information is output, and preparations are made to provide it to the user.

[0314] Step 4:

[0315] The server analyzes transaction data in detail, particularly in electronic payment services, through transaction regulatory analysis tools. Based on this analysis, legal information and risk factors related to the user's transactions are identified, and the data is processed so that users can receive this information in real time.

[0316] Step 5:

[0317] The server uses information display means to present analysis results and legal information to the user. In this step, advice regarding legal risks and regulations is displayed on the user's terminal, and the user can receive this information in an easy-to-understand format.

[0318] Step 6:

[0319] The server utilizes automated generation and distribution methods to generate necessary documents and emails for business operations and send them to relevant parties. In this process, appropriate documents are automatically created based on collected and analyzed information and securely delivered to the specified recipients.

[0320] Step 7:

[0321] The user reviews the presented information and generated documents, and sends their feedback to the server. The server uses learning mechanisms to perform data calculations based on the received feedback to improve the system's accuracy, and uses this information for future analyses.

[0322] 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.

[0323] This invention is implemented in the form of an AI agent system that aims to streamline corporate legal operations, combined with an emotion engine that recognizes user emotions. This system not only analyzes business data and provides legal information, but also analyzes user emotions and presents information based on those emotions, thereby providing more personalized support.

[0324] First, the user inputs or selects business data through a terminal. The server then uses this data to collect relevant business information from internal systems and databases.

[0325] Once the data is collected, the server analyzes it using natural language processing technology. In this process, relevant legal information and risk factors are identified, and the latest legal information is retrieved from an external legal database and compared with the collected data.

[0326] Furthermore, the server analyzes user input and interaction data obtained from the terminal to recognize the user's emotions. The emotion engine identifies emotions from the user's text input, voice data, and behavioral data, and adjusts what information the system presents and how.

[0327] Through the information presentation mechanism, the terminal presents the user with analysis results and related legal information. In this process, the information provided to the user is adjusted by an emotion engine. For example, for users experiencing stress, the system optimizes the display to show particularly important information concisely.

[0328] Furthermore, using automated generation and transmission methods, the server automatically generates necessary documents and emails. These generated documents are provided to the user as a preview on their terminal, allowing the user to review the content and make any necessary corrections or approvals.

[0329] User feedback is collected through the device and sent to the server along with sentiment data. The server uses this feedback to reinforce the learning process and improve the system's accuracy and effectiveness.

[0330] As a concrete example, consider a scenario where a legal professional uses this system to conduct a risk assessment before the market launch of a new product. When the professional inputs business data, the server immediately identifies relevant laws and regulations, and the emotion engine senses the user's stress level. Accordingly, information that concisely summarizes explanations of complex laws and regulations is provided to the terminal. Necessary approval documents are automatically generated and sent to the relevant parties after user confirmation. Through this process, the present invention supports the rapid execution of legal work and improves the user experience.

[0331] The following describes the processing flow.

[0332] Step 1:

[0333] The user inputs or selects business data using a terminal. This action sends the business information to the system, initiating the analysis process.

[0334] Step 2:

[0335] The server accesses internal systems and databases to collect data related to the specified task. This data includes past work history and related documents.

[0336] Step 3:

[0337] The server uses natural language processing technology to analyze the collected data. This analysis extracts keywords and contexts related to the business, and based on this, legal information and risk factors are identified.

[0338] Step 4:

[0339] The server accesses an external legal database to retrieve the latest legal information. The retrieved legal information is then compared with the analysis results, and the laws relevant to the business are extracted.

[0340] Step 5:

[0341] The device sends user input data to the emotion engine, which analyzes the user's emotions. The emotion engine identifies the user's emotional state and adjusts the information presented based on that state.

[0342] Step 6:

[0343] The information presentation mechanism allows the terminal to present analysis results and related legal information to the user. This information is displayed in a format adjusted by the emotion engine; for example, important information is presented concisely to users experiencing high stress levels.

[0344] Step 7:

[0345] The server utilizes automated generation and sending mechanisms to generate necessary documents and emails based on templates. The generated documents and emails are then provided to the user as a preview via their terminal.

[0346] Step 8:

[0347] The user reviews the generated document on their device and makes corrections or approvals as needed. Approved documents and emails are then sent to the relevant parties by the server.

[0348] Step 9:

[0349] The device collects user feedback and sends it to the server along with sentiment data. The server uses this feedback as a learning tool to improve the overall accuracy of the system.

[0350] (Example 2)

[0351] 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".

[0352] Traditional business support systems have faced challenges in legal work, where large amounts of information must be processed. These systems are inefficient in organizing and presenting information, and they fail to consider the user's feelings when providing information. As a result, users often experience stress when understanding information or making decisions.

[0353] 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.

[0354] In this invention, the server includes data collection means for acquiring information related to business operations, data analysis means for analyzing the collected information and identifying relevant regulatory information, and emotion recognition means for recognizing the user's emotions and adjusting the analysis results. This enables efficient information presentation that takes the user's emotions into consideration.

[0355] "Information related to business operations" refers to all data and information related to the operations carried out by a company or organization, and this includes information for compliance with laws and regulations.

[0356] "Data collection means" refers to the means by which a system acquires information entered or selected by a user and aggregates the necessary business data.

[0357] "Data analysis methods" refer to means of analyzing collected information using natural language processing technology and other methods to identify relevant regulatory information and risk factors.

[0358] A "regulatory information matching means" is a means for matching regulatory information obtained from an external regulatory database with information identified internally.

[0359] An "information presentation means" is a means of displaying information to the user based on analysis results and providing necessary documents and communications in an easy-to-understand format.

[0360] An "automatic generation and transmission method" is a means of automatically creating documents and emails necessary for business operations and sending them to relevant parties.

[0361] "Learning methods" are means of improving the functionality and accuracy of a system based on feedback obtained from users.

[0362] "Emotion recognition means" are tools for analyzing a user's emotional state based on their text input and interaction data, and for adjusting the information presented accordingly.

[0363] This invention aims to streamline information management in business support systems and reduce the burden on users. The system operates primarily through the interaction of servers, terminals, and users.

[0364] First, the user uses their device to input or select work-related information. In this process, the work-related information is imported into the system and proceeds to the next analysis step. The devices used are expected to be general-purpose computers or smartphones.

[0365] The server receives collected business information and performs analysis using natural language processing techniques with data analysis tools. Specifically, it uses libraries such as Python's NLTK and spaCy to perform semantic analysis and keyword extraction of the information, and then identifies relevant regulatory information based on the identified information. The server also accesses external regulatory databases (e.g., government legal databases) to obtain the latest information and compare it with internal data.

[0366] The emotion recognition system also functions within the server, analyzing user input and interaction data. The user's text and voice data are analyzed using Google Cloud's Natural Language API to identify stress levels and emotional states. Based on these results, the server provides the user with tailored information through an information presentation system. This information is presented on the device in a clear and organized manner, incorporating features designed to reduce stress.

[0367] Furthermore, the server uses an automated generation and transmission mechanism to automatically create and finalize the necessary documents and emails for business operations. Users preview the generated documents on their terminals, review the details, make any necessary changes, and finally send them.

[0368] In this invention, user feedback is crucial. This feedback is collected via a terminal, transmitted to a server, and used to improve the system through a learning mechanism.

[0369] As a concrete example, consider the case of verifying legal compliance when launching a new product into the market. The user enters instructions into the system as prompts, such as "Identify the laws and regulations related to the market launch risk assessment of the new product," or "Present important legal information concisely, taking into consideration the user's feelings," and starts the process. This form of system achieves both the rapid execution of legal tasks and an improved user experience.

[0370] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0371] Step 1:

[0372] Users input business-related data using a terminal. In this input process, users provide specific information to the terminal according to their business objectives, such as information regarding the market launch of a new product. The output is business data necessary for the server to use in subsequent processing.

[0373] Step 2:

[0374] The server receives the input data and, via data collection mechanisms, gathers relevant internal information and similar past case data from the internal database. Specifically, it executes database queries to obtain the necessary data and outputs it as an initial list of data to be analyzed.

[0375] Step 3:

[0376] The server analyzes business data using natural language processing techniques. In this step, Python's NLTK and spaCy libraries are used to analyze the input data and extract important keywords. The input is business data provided by the user, and the output is the analyzed information and related regulatory information.

[0377] Step 4:

[0378] The server accesses an external regulatory database to retrieve the latest legal information and compares it with the previously identified information. Specifically, it uses an API to query information from the external database and retrieve regulatory information. After the comparison, the output is the legal information that has been adjusted.

[0379] Step 5:

[0380] To recognize a user's emotions, the server performs sentiment analysis using user text and interaction data obtained from the device. It uses Google Cloud's Natural Language API to determine the user's stress level and emotions. The input is user text and interaction data, and the output is the user's emotional state.

[0381] Step 6:

[0382] The device analyzes and recognizes emotions from the data, then presents the user with adjusted information. Here, information presentation methods are used to display the information in a way that is easy for the user to understand. The input consists of analyzed data and emotion data, while the output is concise and personalized information presented to the user.

[0383] Step 7:

[0384] The server uses an automated generation and sending mechanism to generate the necessary documents and emails, and provides a preview on the user's terminal. The user reviews this on the terminal, makes any necessary corrections, and then issues a final sending instruction. The input is the generated document data, and the output is the reviewed and approved document.

[0385] Step 8:

[0386] Feedback is collected from users via their devices and sent to a server. The server uses this feedback to improve the accuracy and effectiveness of the system through learning mechanisms. The input is user feedback and sentiment data, and the output is an augmented learning algorithm.

[0387] (Application Example 2)

[0388] 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 as the "terminal".

[0389] In current in-store customer service, it is difficult to properly understand customer emotions and provide services accordingly. This can lead to decreased customer satisfaction and lost sales opportunities. The goal is to solve this problem and achieve more personalized customer service.

[0390] 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.

[0391] In this invention, the server includes data acquisition means for obtaining business information, data analysis means for analyzing the acquired information and identifying relevant rule information, matching means for obtaining rule information from an external rule database and aligning it with the identified rule information, and emotion analysis means for analyzing the user's emotions and optimizing information provision. This makes it possible to grasp the customer's emotional state in real time and provide information and customer service accordingly.

[0392] "Data acquisition means" refers to the mechanisms and techniques used to acquire business information.

[0393] "Data analysis means" refers to mechanisms and techniques for analyzing acquired business information and identifying relevant rule information.

[0394] A "matching method" refers to a mechanism or technique for aligning regulatory information obtained from an external regulatory database with specified rule information.

[0395] "Information display means" refers to mechanisms or techniques that display relevant information to users based on matching results.

[0396] "Generation and transmission means" refers to mechanisms and techniques for automatically generating and transmitting documents and communications necessary for business operations.

[0397] A "learning method" refers to a mechanism or technique aimed at improving system accuracy by updating records based on user feedback.

[0398] "Emotional analysis tools" refer to mechanisms and techniques for analyzing users' emotions and optimizing information provision.

[0399] A system for carrying out this invention includes a smart device and a server to support customer service operations in a physical store. As an example of a smart device, smart glasses are used. These smart glasses are equipped with a camera and a microphone to capture the customer's facial expressions and voice.

[0400] The server receives data transmitted from smart glasses and collects business information using data acquisition means. Subsequently, data analysis means analyze this data to identify relevant regulatory information. Natural language processing technology is used for the analysis. After obtaining the necessary regulatory information from an external regulatory database, matching means integrate this information.

[0401] To analyze user emotions, the server uses emotion analysis tools to identify customer emotions from acquired data. For example, it may use facial recognition and emotion analysis libraries (e.g., OpenCV, Affectiva API) to identify emotions from facial expressions and voice.

[0402] The information display system shows relevant information on smart glasses based on matching and sentiment analysis results. For example, it displays product information and campaign information that the customer might be interested in.

[0403] Furthermore, the generation and transmission mechanism allows for the automatic generation of necessary documents and communications for business operations and their transmission to relevant parties. User feedback is recorded through a learning mechanism, and the system's accuracy is improved based on this feedback.

[0404] As a concrete example, consider a scenario where a customer shows interest in a new electronic product. In this case, the smart glasses display detailed specifications and reviews of the product. If the system analyzes that the customer is excited or interested, it will also provide information that highlights the product's special features and limited-time offers.

[0405] An example of a prompt for the AI ​​model is: "Consider the customer's current emotions and decide what information should be displayed on the smart glasses. The customer's area of ​​interest is new electronic products. Emotion analysis result: Excitement, curiosity." Using this prompt, more effective customer service can be provided.

[0406] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0407] Step 1:

[0408] The server receives customer facial expression and voice data transmitted from smart glasses. Based on this input data, it uses data acquisition methods to extract business information such as basic customer information and past purchase history from the company's internal database. The output consists of various business-related information necessary for analysis.

[0409] Step 2:

[0410] The server uses data analysis tools to perform natural language processing and sentiment analysis on the received facial expression and voice data. The input data is analyzed using sentiment recognition libraries (e.g., OpenCV, Affectiva API) to identify the customer's current emotional state. The output is the customer's emotion, such as excitement or curiosity.

[0411] Step 3:

[0412] The server uses data analysis tools to extract relevant regulatory information from the acquired business data. Next, it retrieves the latest regulatory information from an external regulatory database and matches it using matching tools. The input data consists of business information and regulatory information, and the output is the result of identifying the relevant information.

[0413] Step 4:

[0414] The server uses an information display mechanism to transmit information based on the identified results and sentiment analysis results to the smart glasses. Using the prompt text generated at this stage, the generating AI model presents information optimized for the customer. The input is sentiment and rule information, and the output is a customized information display for the customer.

[0415] Step 5:

[0416] The user (store clerk) interacts with customers based on information displayed on smart glasses. Customer reactions and feedback become new inputs, which are sent to the server and recorded by a generation and transmission system. The output is feedback data that helps improve the service.

[0417] Step 6:

[0418] The server learns from feedback to improve the accuracy of sentiment analysis and information presentation. The learning process is used to enhance the overall system performance for future customer interactions. Input consists of feedback data and feedback content, while output is the updated analysis model.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] [Third Embodiment]

[0423] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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).

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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".

[0435] The AI ​​agent system of the present invention is realized by combining various advanced technologies to efficiently support a company's legal operations. This system is implemented in the following form.

[0436] First, the system initiates processes related to the task by having the user input or select data related to the task through a terminal. Next, the server accesses internal systems and databases to collect data related to the specified task. This data includes past work history and related documents, which the system analyzes.

[0437] The collected data is analyzed by the server using natural language processing technology. This analysis extracts keywords and contexts related to the business, and identifies relevant legal information and risk factors.

[0438] Simultaneously, the server accesses an external legal database to obtain the latest legal information. This information is then compared with the analysis results of the collected data, and the laws relevant to the business are extracted.

[0439] Through information presentation methods, the terminal clearly displays analysis results and relevant legal information to the user. This process helps users understand the knowledge and measures necessary to carry out their work.

[0440] Furthermore, the server automatically generates necessary documents and emails based on templates using an automated generation and transmission mechanism, and these are sent to the relevant parties. Users can preview the generated documents on their terminal, review them as needed, and then send them.

[0441] Furthermore, user feedback is sent to the server via the terminal. The server uses this feedback as a learning tool to improve the system's accuracy. Specifically, it incorporates user feedback to improve the accuracy of subsequent analyses, information presentations, and document generation.

[0442] For example, this system can be used when a company enters into a new contract. The user inputs an overview of the contract details from a terminal, and the server identifies the relevant laws and regulations based on that input. The analysis results, including legal information and risk mitigation measures, are presented to the terminal, and the necessary contract documents are automatically generated and sent. This enables efficient contract procedures while minimizing legal risks.

[0443] The system of the present invention, configured as described above, reduces the burden of legal work and supports accurate and efficient work execution.

[0444] The following describes the processing flow.

[0445] Step 1:

[0446] The user inputs or selects work-related data through their terminal. This sends a request for analysis of the work content to the system.

[0447] Step 2:

[0448] The server accesses internal systems and databases to collect data related to the specified tasks. The collected data includes past work history and related documents.

[0449] Step 3:

[0450] The server uses natural language processing technology to analyze the collected data. This analysis process extracts keywords and contexts relevant to the business and identifies relevant legal information and risk factors.

[0451] Step 4:

[0452] The server accesses an external legal database to retrieve the latest legal information. The retrieved legal information is then compared with the analysis results to extract the relevant laws and regulations.

[0453] Step 5:

[0454] The device presents the user with analysis results and related legal information. The information is displayed in an easy-to-understand format, enabling the user to grasp the necessary knowledge and countermeasures.

[0455] Step 6:

[0456] The server uses an automated generation and sending mechanism to generate the necessary documents and emails based on templates. The generated documents and emails are then provided to the user as a preview via their terminal.

[0457] Step 7:

[0458] The user reviews the document generated on their device and makes corrections or approvals as needed. After approval, the document or email is automatically sent to the relevant parties.

[0459] Step 8:

[0460] User feedback is sent to the server via the terminal. The server uses this feedback to learn and improve the overall accuracy and efficiency of the system.

[0461] (Example 1)

[0462] 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."

[0463] In modern business operations, it is crucial to perform legal tasks quickly and accurately. However, legal work requires collecting and analyzing a large amount of information and verifying relevant legal information based on that information, which is time-consuming and labor-intensive when done manually. Furthermore, it is difficult to keep up with the latest legal information, making it challenging to take appropriate measures to avoid legal risks. Therefore, there is a need for effective systems that streamline operations and reduce legal risks.

[0464] 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.

[0465] In this invention, the server includes data collection means for acquiring business information, data analysis means for analyzing the collected information using natural language processing technology to identify relevant normative information, and normative information matching means for acquiring normative information from an external normative database and matching it with the identified normative information. This makes it possible to automatically collect and analyze information necessary for legal work and to quickly and accurately confirm relevant legal information. As a result, legal risks are reduced and operational efficiency is improved.

[0466] "Business information" refers to information related to the daily business activities of a company or organization, and is a general term for data including contract details, their history, and related documents.

[0467] "Data collection methods" refer to the methods and techniques for collecting necessary data from various sources, and include mechanisms such as access to databases and internal records.

[0468] "Natural language processing technology" is a type of artificial intelligence technology that uses computers to analyze human language and understand its meaning and context, and is used for analyzing text data.

[0469] "Non-standard information" refers to information about laws, regulations, and rules related to business operations, and is used to determine whether a company's activities comply with the law.

[0470] "Data analysis means" refers to methods and techniques for analyzing collected data to identify important information and patterns within it, and includes natural language processing.

[0471] A "matching method" refers to a method or technology for determining a relationship between identified information and related information obtained from external sources by comparing and matching them.

[0472] "Information presentation means" refers to methods and devices for clearly presenting analysis results and related information to users, and includes visual interfaces.

[0473] "Automatic generation and transmission means" refers to methods and technologies for automatically creating necessary documents and communications based on templates and sending them to relevant parties.

[0474] "Learning methods" refer to methods and techniques for improving the accuracy and performance of a system based on feedback from users, and include machine learning.

[0475] The AI ​​agent system of the present invention is designed to support the efficiency of legal work and risk management, and is implemented as follows: First, the user inputs information related to the work via a terminal. This information includes contract details and summaries. The terminal is typically a common computer hardware such as a personal computer or tablet, and a form on a web browser is used as the input interface.

[0476] Next, the server collects relevant business information from internal systems and databases based on the input information. The server requires high-speed data processing capabilities and large-capacity storage, and uses common data management software such as SQL databases. The collected information is processed using natural language processing by a generative AI model to extract important keywords and context. This generative AI model can be implemented, for example, by implementing a Python natural language processing library.

[0477] Furthermore, the server accesses an external normative database to obtain the latest normative information. By using an API to retrieve updated legal data, the most up-to-date legal information is always reflected. By comparing this information with the analysis results, the laws and regulations relevant to the business are identified.

[0478] Subsequently, the terminal visually presents the user with analysis results and relevant legal information. The displayed information is organized in a dashboard format and designed to be easily understood by the user.

[0479] As part of this process, the server automatically generates necessary documents and communications based on templates and provides a means to send them to relevant parties. This functionality allows for the efficient processing of contractual documents and risk management notices.

[0480] Furthermore, users can review documents generated on their devices, approve their contents, and then submit them. This review process allows users to easily perform legal review tasks within the system.

[0481] As a concrete example, when a user enters a prompt such as "Identify legal information for a new contract case," the system automatically collects and analyzes information related to this case and presents the necessary legal information, thereby supporting rapid decision-making. In this way, the system described in the present invention reduces the burden on legal affairs work for companies and enables efficient work execution.

[0482] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0483] Step 1: The user enters work-related information through the terminal. Here, the contract summary and specific requirements are entered in text format into the input fields. The entered data is sent directly to the server and becomes the basis for the next processing step.

[0484] Step 2: The server accesses the internal database based on the received business information and collects relevant records and documents. Specifically, it executes database queries to retrieve contract history and related past data. This further deepens the business information and retrieves relevant data.

[0485] Step 3: The server analyzes the collected data using a generative AI model. Here, natural language processing technology is utilized to extract keywords and context from business information. The server receives the collected data as input and outputs important business-related information as the analysis result.

[0486] Step 4: The server accesses an external standards database to retrieve the latest standards information. It sends requests to the legal database via an API to retrieve relevant legal information, ensuring that it always reflects the most up-to-date legal information. In this step, the server identifies the standards information relevant to the business based on the information obtained from the external database.

[0487] Step 5: The terminal visually presents the analysis results and normative information received from the server to the user. The presented information is organized in a dashboard format and displayed in a way that is easy for the user to understand. Here, the user can make decisions based on the necessary information.

[0488] Step 6: The server automatically generates the necessary documents and communications based on templates using an automated generation and transmission method, and prepares them for transmission to the relevant parties. Automation is achieved by utilizing a generation AI model to create draft contract documents and emails and export them in the specified format.

[0489] Step 7: The user previews the document generated on the terminal and reviews its content. They enter feedback and make corrections as needed, and then submit it to the relevant parties after final confirmation. This step is the final review, and the feedback is sent to the server and used to improve accuracy in future projects.

[0490] (Application Example 1)

[0491] 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."

[0492] In modern electronic transactions, users face challenges in maintaining compliance with frequently changing laws and regulations. While it is essential to quickly and accurately grasp relevant legal information and take appropriate action, traditional manual processes and systems have limitations, leaving users constantly at risk of inadvertently disregarding regulations. In this context, there is a need for a means to proactively identify legal risks associated with transactions and provide appropriate information in real time.

[0493] 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.

[0494] In this invention, the server includes information gathering means for acquiring business data, information analysis means for analyzing the collected information and identifying relevant regulatory information, regulatory information matching means for acquiring legal information from an external legal database and matching it with the identified regulatory information, and transaction regulatory analysis means for collecting transaction data, analyzing the regulatory risks of transactions, and providing legal information to users in advance. This enables users to grasp legal risks more accurately and take necessary actions appropriately and quickly.

[0495] "Business data" refers to information about all transactions and business processes in a company's activities, including transaction history and contract details.

[0496] "Information gathering means" refers to processes or systems for efficiently acquiring necessary data, and may include access to internal and external databases.

[0497] "Information analysis means" refers to the techniques and processes used to analyze collected data and extract important information and patterns from it.

[0498] "Regulatory information" refers to information about laws and regulations that apply to specific business operations or transactions.

[0499] "Regulatory information matching means" refers to a function or system for comparing acquired legal information with analyzed data and identifying matching sections.

[0500] "Information display means" refers to a mechanism or device for presenting analysis results in a way that is easy for users to understand.

[0501] "Automated generation and distribution means" refers to a process or system for automatically creating business-related documents and emails and sending them to relevant parties.

[0502] "Learning methods" refer to mechanisms that receive feedback from users and use that information to improve the system's performance and accuracy.

[0503] "Transaction regulatory analysis tools" refer to means for identifying legal risks from transaction data and providing users with appropriate legal information based on those findings.

[0504] To realize this invention, an AI agent system plays a central role in efficiently collecting and analyzing business data and providing users with appropriate legal information. The server acquires business data from various data sources using information collection means. Specific data sources include internal databases and external legal databases, enabling comprehensive information collection.

[0505] The server then analyzes the data collected using information analysis tools with advanced natural language processing techniques. In this process, natural language processing tools and AI models play a crucial role in identifying relevant regulatory information and potential legal risks. The analyzed information is then cross-referenced with the latest legal information obtained from external legal databases using regulatory information matching tools. This matching process confirms that the identified risk elements are consistent with applicable laws and regulations.

[0506] Furthermore, the server uses transaction regulatory analysis tools to analyze transaction data, particularly in electronic payment services, and provides users with real-time legal information related to transactions. This allows users to proactively prevent legal risks. In addition, related documents and communications are automatically generated and distributed to relevant parties using automated generation and distribution tools. All of these processes are continuously improved through user feedback, and the overall accuracy of the system is enhanced through learning mechanisms.

[0507] For example, if a user is trying to purchase an overseas product, this system will analyze information on import regulations and related tax laws for that product and present the necessary measures to the user. An example of a prompt message would be, "Transaction: Please analyze the latest import regulations and related laws based on User ID 123, Amount 5000 JPY, Product ID 456." This allows users to proceed with transactions with confidence.

[0508] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0509] Step 1:

[0510] The server receives transaction-related input from users using information gathering methods to acquire business data. This input includes detailed information such as user ID, amount, and product ID. Based on the collected business data, the server searches relevant internal and external databases and aggregates the necessary information.

[0511] Step 2:

[0512] The server processes the collected business data and related information using information analysis tools. This process utilizes generative AI models and natural language processing techniques to analyze the data and identify relevant regulatory information and risk factors. The analysis results are output as data and passed on to the next processing step.

[0513] Step 3:

[0514] The server uses regulatory information matching means to compare the analysis results with legal information obtained from an external legal database. In this process, the analysis data is matched with the latest regulatory information, and the relevant laws are identified. The matching regulatory information is output and ready to be provided to the user.

[0515] Step 4:

[0516] The server analyzes transaction data in detail, particularly in electronic payment services, through transaction regulatory analysis tools. Based on this analysis, legal information and risk factors related to the user's transactions are identified, and the data is processed so that users can receive this information in real time.

[0517] Step 5:

[0518] The server uses information display means to present analysis results and legal information to the user. In this step, advice regarding legal risks and regulations is displayed on the user's terminal, and the user can receive this information in an easy-to-understand format.

[0519] Step 6:

[0520] The server utilizes automated generation and distribution methods to generate necessary documents and emails for business operations and send them to relevant parties. In this process, appropriate documents are automatically created based on collected and analyzed information and securely delivered to the specified recipients.

[0521] Step 7:

[0522] The user reviews the presented information and generated documents, and sends their feedback to the server. The server uses learning mechanisms to perform data calculations based on the received feedback to improve the system's accuracy, and uses this information for future analyses.

[0523] 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.

[0524] This invention is implemented in the form of an AI agent system that aims to streamline corporate legal operations, combined with an emotion engine that recognizes user emotions. This system not only analyzes business data and provides legal information, but also analyzes user emotions and presents information based on those emotions, thereby providing more personalized support.

[0525] First, the user inputs or selects business data through a terminal. The server then uses this data to collect relevant business information from internal systems and databases.

[0526] Once the data is collected, the server analyzes it using natural language processing technology. In this process, relevant legal information and risk factors are identified, and the latest legal information is retrieved from an external legal database and compared with the collected data.

[0527] Furthermore, the server analyzes user input and interaction data obtained from the terminal to recognize the user's emotions. The emotion engine identifies emotions from the user's text input, voice data, and behavioral data, and adjusts what information the system presents and how.

[0528] Through the information presentation mechanism, the terminal presents the user with analysis results and related legal information. In this process, the information provided to the user is adjusted by an emotion engine. For example, for users experiencing stress, the system optimizes the display to show particularly important information concisely.

[0529] Furthermore, using automated generation and transmission methods, the server automatically generates necessary documents and emails. These generated documents are provided to the user as a preview on their terminal, allowing the user to review the content and make any necessary corrections or approvals.

[0530] User feedback is collected through the device and sent to the server along with sentiment data. The server uses this feedback to reinforce the learning process and improve the system's accuracy and effectiveness.

[0531] As a concrete example, consider a scenario where a legal professional uses this system to conduct a risk assessment before the market launch of a new product. When the professional inputs business data, the server immediately identifies relevant laws and regulations, and the emotion engine senses the user's stress level. Accordingly, information that concisely summarizes explanations of complex laws and regulations is provided to the terminal. Necessary approval documents are automatically generated and sent to the relevant parties after user confirmation. Through this process, the present invention supports the rapid execution of legal work and improves the user experience.

[0532] The following describes the processing flow.

[0533] Step 1:

[0534] The user inputs or selects business data using a terminal. This action sends the business information to the system, initiating the analysis process.

[0535] Step 2:

[0536] The server accesses internal systems and databases to collect data related to the specified task. This data includes past work history and related documents.

[0537] Step 3:

[0538] The server uses natural language processing technology to analyze the collected data. This analysis extracts keywords and contexts related to the business, and based on this, legal information and risk factors are identified.

[0539] Step 4:

[0540] The server accesses an external legal database to retrieve the latest legal information. The retrieved legal information is then compared with the analysis results, and the laws relevant to the business are extracted.

[0541] Step 5:

[0542] The device sends user input data to the emotion engine, which analyzes the user's emotions. The emotion engine identifies the user's emotional state and adjusts the information presented based on that state.

[0543] Step 6:

[0544] The information presentation mechanism allows the terminal to present analysis results and related legal information to the user. This information is displayed in a format adjusted by the emotion engine; for example, important information is presented concisely to users experiencing high stress levels.

[0545] Step 7:

[0546] The server utilizes automated generation and sending mechanisms to generate necessary documents and emails based on templates. The generated documents and emails are then provided to the user as a preview via their terminal.

[0547] Step 8:

[0548] The user reviews the generated document on their device and makes corrections or approvals as needed. Approved documents and emails are then sent to the relevant parties by the server.

[0549] Step 9:

[0550] The device collects user feedback and sends it to the server along with sentiment data. The server uses this feedback as a learning tool to improve the overall accuracy of the system.

[0551] (Example 2)

[0552] 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."

[0553] Traditional business support systems have faced challenges in legal work, where large amounts of information must be processed. These systems are inefficient in organizing and presenting information, and they fail to consider the user's feelings when providing information. As a result, users often experience stress when understanding information or making decisions.

[0554] 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.

[0555] In this invention, the server includes data collection means for acquiring information related to business operations, data analysis means for analyzing the collected information and identifying relevant regulatory information, and emotion recognition means for recognizing the user's emotions and adjusting the analysis results. This enables efficient information presentation that takes the user's emotions into consideration.

[0556] "Information related to business operations" refers to all data and information related to the operations carried out by a company or organization, and this includes information for compliance with laws and regulations.

[0557] "Data collection means" refers to the means by which a system acquires information entered or selected by a user and aggregates the necessary business data.

[0558] "Data analysis methods" refer to means of analyzing collected information using natural language processing technology and other methods to identify relevant regulatory information and risk factors.

[0559] A "regulatory information matching means" is a means for matching regulatory information obtained from an external regulatory database with information identified internally.

[0560] An "information presentation means" is a means of displaying information to the user based on analysis results and providing necessary documents and communications in an easy-to-understand format.

[0561] An "automatic generation and transmission method" is a means of automatically creating documents and emails necessary for business operations and sending them to relevant parties.

[0562] "Learning methods" are means of improving the functionality and accuracy of a system based on feedback obtained from users.

[0563] "Emotion recognition means" are tools for analyzing a user's emotional state based on their text input and interaction data, and for adjusting the information presented accordingly.

[0564] This invention aims to streamline information management in business support systems and reduce the burden on users. The system operates primarily through the interaction of servers, terminals, and users.

[0565] First, the user uses their device to input or select work-related information. In this process, the work-related information is imported into the system and proceeds to the next analysis step. The devices used are expected to be general-purpose computers or smartphones.

[0566] The server receives collected business information and performs analysis using natural language processing techniques with data analysis tools. Specifically, it uses libraries such as Python's NLTK and spaCy to perform semantic analysis and keyword extraction of the information, and then identifies relevant regulatory information based on the identified information. The server also accesses external regulatory databases (e.g., government legal databases) to obtain the latest information and compare it with internal data.

[0567] The emotion recognition system also functions within the server, analyzing user input and interaction data. The user's text and voice data are analyzed using Google Cloud's Natural Language API to identify stress levels and emotional states. Based on these results, the server provides the user with tailored information through an information presentation system. This information is presented on the device in a clear and organized manner, incorporating features designed to reduce stress.

[0568] Furthermore, the server uses an automated generation and transmission mechanism to automatically create and finalize the necessary documents and emails for business operations. Users preview the generated documents on their terminals, review the details, make any necessary changes, and finally send them.

[0569] In this invention, user feedback is crucial. This feedback is collected via a terminal, transmitted to a server, and used to improve the system through a learning mechanism.

[0570] As a concrete example, consider the case of verifying legal compliance when launching a new product into the market. The user enters instructions into the system as prompts, such as "Identify the laws and regulations related to the market launch risk assessment of the new product," or "Present important legal information concisely, taking into consideration the user's feelings," and starts the process. This form of system achieves both the rapid execution of legal tasks and an improved user experience.

[0571] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0572] Step 1:

[0573] Users input business-related data using a terminal. In this input process, users provide specific information to the terminal according to their business objectives, such as information regarding the market launch of a new product. The output is business data necessary for the server to use in subsequent processing.

[0574] Step 2:

[0575] The server receives the input data and, via data collection mechanisms, gathers relevant internal information and similar past case data from the internal database. Specifically, it executes database queries to obtain the necessary data and outputs it as an initial list of data to be analyzed.

[0576] Step 3:

[0577] The server analyzes business data using natural language processing techniques. In this step, Python's NLTK and spaCy libraries are used to analyze the input data and extract important keywords. The input is business data provided by the user, and the output is the analyzed information and related regulatory information.

[0578] Step 4:

[0579] The server accesses an external regulatory database to retrieve the latest legal information and compares it with the previously identified information. Specifically, it uses an API to query information from the external database and retrieve regulatory information. After the comparison, the output is the legal information that has been adjusted.

[0580] Step 5:

[0581] To recognize a user's emotions, the server performs sentiment analysis using user text and interaction data obtained from the device. It uses Google Cloud's Natural Language API to determine the user's stress level and emotions. The input is user text and interaction data, and the output is the user's emotional state.

[0582] Step 6:

[0583] The device analyzes and recognizes emotions from the data, then presents the user with adjusted information. Here, information presentation methods are used to display the information in a way that is easy for the user to understand. The input consists of analyzed data and emotion data, while the output is concise and personalized information presented to the user.

[0584] Step 7:

[0585] The server uses an automated generation and sending mechanism to generate the necessary documents and emails, and provides a preview on the user's terminal. The user reviews this on the terminal, makes any necessary corrections, and then issues a final sending instruction. The input is the generated document data, and the output is the reviewed and approved document.

[0586] Step 8:

[0587] Feedback is collected from users via their devices and sent to a server. The server uses this feedback to improve the accuracy and effectiveness of the system through learning mechanisms. The input is user feedback and sentiment data, and the output is an augmented learning algorithm.

[0588] (Application Example 2)

[0589] 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."

[0590] In current in-store customer service, it is difficult to properly understand customer emotions and provide services accordingly. This can lead to decreased customer satisfaction and lost sales opportunities. The goal is to solve this problem and achieve more personalized customer service.

[0591] 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.

[0592] In this invention, the server includes data acquisition means for obtaining business information, data analysis means for analyzing the acquired information and identifying relevant rule information, matching means for obtaining rule information from an external rule database and aligning it with the identified rule information, and emotion analysis means for analyzing the user's emotions and optimizing information provision. This makes it possible to grasp the customer's emotional state in real time and provide information and customer service accordingly.

[0593] "Data acquisition means" refers to the mechanisms and techniques used to acquire business information.

[0594] "Data analysis means" refers to mechanisms and techniques for analyzing acquired business information and identifying relevant rule information.

[0595] A "matching method" refers to a mechanism or technique for aligning regulatory information obtained from an external regulatory database with specified rule information.

[0596] "Information display means" refers to mechanisms or techniques that display relevant information to users based on matching results.

[0597] "Generation and transmission means" refers to mechanisms and techniques for automatically generating and transmitting documents and communications necessary for business operations.

[0598] A "learning method" refers to a mechanism or technique aimed at improving system accuracy by updating records based on user feedback.

[0599] "Emotional analysis tools" refer to mechanisms and techniques for analyzing users' emotions and optimizing information provision.

[0600] A system for carrying out this invention includes a smart device and a server to support customer service operations in a physical store. As an example of a smart device, smart glasses are used. These smart glasses are equipped with a camera and a microphone to capture the customer's facial expressions and voice.

[0601] The server receives data transmitted from smart glasses and collects business information using data acquisition means. Subsequently, data analysis means analyze this data to identify relevant regulatory information. Natural language processing technology is used for the analysis. After obtaining the necessary regulatory information from an external regulatory database, matching means integrate this information.

[0602] To analyze user emotions, the server uses emotion analysis tools to identify customer emotions from acquired data. For example, it may use facial recognition and emotion analysis libraries (e.g., OpenCV, Affectiva API) to identify emotions from facial expressions and voice.

[0603] The information display system shows relevant information on smart glasses based on matching and sentiment analysis results. For example, it displays product information and campaign information that the customer might be interested in.

[0604] Furthermore, the generation and transmission mechanism allows for the automatic generation of necessary documents and communications for business operations and their transmission to relevant parties. User feedback is recorded through a learning mechanism, and the system's accuracy is improved based on this feedback.

[0605] As a concrete example, consider a scenario where a customer shows interest in a new electronic product. In this case, the smart glasses display detailed specifications and reviews of the product. If the system analyzes that the customer is excited or interested, it will also provide information that highlights the product's special features and limited-time offers.

[0606] An example of a prompt for the AI ​​model is: "Consider the customer's current emotions and decide what information should be displayed on the smart glasses. The customer's area of ​​interest is new electronic products. Emotion analysis result: Excitement, curiosity." Using this prompt, more effective customer service can be provided.

[0607] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0608] Step 1:

[0609] The server receives customer facial expression and voice data transmitted from smart glasses. Based on this input data, it uses data acquisition methods to extract business information such as basic customer information and past purchase history from the company's internal database. The output consists of various business-related information necessary for analysis.

[0610] Step 2:

[0611] The server uses data analysis tools to perform natural language processing and sentiment analysis on the received facial expression and voice data. The input data is analyzed using sentiment recognition libraries (e.g., OpenCV, Affectiva API) to identify the customer's current emotional state. The output is the customer's emotion, such as excitement or curiosity.

[0612] Step 3:

[0613] The server uses data analysis tools to extract relevant regulatory information from the acquired business data. Next, it retrieves the latest regulatory information from an external regulatory database and matches it using matching tools. The input data consists of business information and regulatory information, and the output is the result of identifying the relevant information.

[0614] Step 4:

[0615] The server uses an information display mechanism to transmit information based on the identified results and sentiment analysis results to the smart glasses. Using the prompt text generated at this stage, the generating AI model presents information optimized for the customer. The input is sentiment and rule information, and the output is a customized information display for the customer.

[0616] Step 5:

[0617] The user (store clerk) interacts with customers based on information displayed on smart glasses. Customer reactions and feedback become new inputs, which are sent to the server and recorded by a generation and transmission system. The output is feedback data that helps improve the service.

[0618] Step 6:

[0619] The server learns from feedback to improve the accuracy of sentiment analysis and information presentation. The learning process is used to enhance the overall system performance for future customer interactions. Input consists of feedback data and feedback content, while output is the updated analysis model.

[0620] 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.

[0621] 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.

[0622] 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.

[0623] [Fourth Embodiment]

[0624] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0625] 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.

[0626] 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).

[0627] 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.

[0628] 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.

[0629] 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).

[0630] 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.

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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.

[0635] 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.

[0636] 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".

[0637] The AI ​​agent system of the present invention is realized by combining various advanced technologies to efficiently support a company's legal operations. This system is implemented in the following form.

[0638] First, the system initiates processes related to the task by having the user input or select data related to the task through a terminal. Next, the server accesses internal systems and databases to collect data related to the specified task. This data includes past work history and related documents, which the system analyzes.

[0639] The collected data is analyzed by the server using natural language processing technology. This analysis extracts keywords and contexts related to the business, and identifies relevant legal information and risk factors.

[0640] Simultaneously, the server accesses an external legal database to obtain the latest legal information. This information is then compared with the analysis results of the collected data, and the laws relevant to the business are extracted.

[0641] Through information presentation methods, the terminal clearly displays analysis results and relevant legal information to the user. This process helps users understand the knowledge and measures necessary to carry out their work.

[0642] Furthermore, the server automatically generates necessary documents and emails based on templates using an automated generation and transmission mechanism, and these are sent to the relevant parties. Users can preview the generated documents on their terminal, review them as needed, and then send them.

[0643] Furthermore, user feedback is sent to the server via the terminal. The server uses this feedback as a learning tool to improve the system's accuracy. Specifically, it incorporates user feedback to improve the accuracy of subsequent analyses, information presentations, and document generation.

[0644] For example, this system can be used when a company enters into a new contract. The user inputs an overview of the contract details from a terminal, and the server identifies the relevant laws and regulations based on that input. The analysis results, including legal information and risk mitigation measures, are presented to the terminal, and the necessary contract documents are automatically generated and sent. This enables efficient contract procedures while minimizing legal risks.

[0645] The system of the present invention, configured as described above, reduces the burden of legal work and supports accurate and efficient work execution.

[0646] The following describes the processing flow.

[0647] Step 1:

[0648] The user inputs or selects work-related data through their terminal. This sends a request for analysis of the work content to the system.

[0649] Step 2:

[0650] The server accesses internal systems and databases to collect data related to the specified tasks. The collected data includes past work history and related documents.

[0651] Step 3:

[0652] The server uses natural language processing technology to analyze the collected data. This analysis process extracts keywords and contexts relevant to the business and identifies relevant legal information and risk factors.

[0653] Step 4:

[0654] The server accesses an external legal database to retrieve the latest legal information. The retrieved legal information is then compared with the analysis results to extract the relevant laws and regulations.

[0655] Step 5:

[0656] The device presents the user with analysis results and related legal information. The information is displayed in an easy-to-understand format, enabling the user to grasp the necessary knowledge and countermeasures.

[0657] Step 6:

[0658] The server uses an automated generation and sending mechanism to generate the necessary documents and emails based on templates. The generated documents and emails are then provided to the user as a preview via their terminal.

[0659] Step 7:

[0660] The user reviews the document generated on their device and makes corrections or approvals as needed. After approval, the document or email is automatically sent to the relevant parties.

[0661] Step 8:

[0662] User feedback is sent to the server via the terminal. The server uses this feedback to learn and improve the overall accuracy and efficiency of the system.

[0663] (Example 1)

[0664] 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".

[0665] In modern business operations, it is crucial to perform legal tasks quickly and accurately. However, legal work requires collecting and analyzing a large amount of information and verifying relevant legal information based on that information, which is time-consuming and labor-intensive when done manually. Furthermore, it is difficult to keep up with the latest legal information, making it challenging to take appropriate measures to avoid legal risks. Therefore, there is a need for effective systems that streamline operations and reduce legal risks.

[0666] 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.

[0667] In this invention, the server includes data collection means for acquiring business information, data analysis means for analyzing the collected information using natural language processing technology to identify relevant normative information, and normative information matching means for acquiring normative information from an external normative database and matching it with the identified normative information. This makes it possible to automatically collect and analyze information necessary for legal work and to quickly and accurately confirm relevant legal information. As a result, legal risks are reduced and operational efficiency is improved.

[0668] "Business information" refers to information related to the daily business activities of a company or organization, and is a general term for data including contract details, their history, and related documents.

[0669] "Data collection methods" refer to the methods and techniques for collecting necessary data from various sources, and include mechanisms such as access to databases and internal records.

[0670] "Natural language processing technology" is a type of artificial intelligence technology that uses computers to analyze human language and understand its meaning and context, and is used for analyzing text data.

[0671] "Non-standard information" refers to information about laws, regulations, and rules related to business operations, and is used to determine whether a company's activities comply with the law.

[0672] "Data analysis means" refers to methods and techniques for analyzing collected data to identify important information and patterns within it, and includes natural language processing.

[0673] A "matching method" refers to a method or technology for determining a relationship between identified information and related information obtained from external sources by comparing and matching them.

[0674] "Information presentation means" refers to methods and devices for clearly presenting analysis results and related information to users, and includes visual interfaces.

[0675] "Automatic generation and transmission means" refers to methods and technologies for automatically creating necessary documents and communications based on templates and sending them to relevant parties.

[0676] "Learning methods" refer to methods and techniques for improving the accuracy and performance of a system based on feedback from users, and include machine learning.

[0677] The AI ​​agent system of the present invention is designed to support the efficiency of legal work and risk management, and is implemented as follows: First, the user inputs information related to the work via a terminal. This information includes contract details and summaries. The terminal is typically a common computer hardware such as a personal computer or tablet, and a form on a web browser is used as the input interface.

[0678] Next, the server collects relevant business information from internal systems and databases based on the input information. The server requires high-speed data processing capabilities and large-capacity storage, and uses common data management software such as SQL databases. The collected information is processed using natural language processing by a generative AI model to extract important keywords and context. This generative AI model can be implemented, for example, by implementing a Python natural language processing library.

[0679] Furthermore, the server accesses an external normative database to obtain the latest normative information. By using an API to retrieve updated legal data, the most up-to-date legal information is always reflected. By comparing this information with the analysis results, the laws and regulations relevant to the business are identified.

[0680] Subsequently, the terminal visually presents the user with analysis results and relevant legal information. The displayed information is organized in a dashboard format and designed to be easily understood by the user.

[0681] As part of this process, the server automatically generates necessary documents and communications based on templates and provides a means to send them to relevant parties. This functionality allows for the efficient processing of contractual documents and risk management notices.

[0682] Furthermore, users can review documents generated on their devices, approve their contents, and then submit them. This review process allows users to easily perform legal review tasks within the system.

[0683] As a concrete example, when a user enters a prompt such as "Identify legal information for a new contract case," the system automatically collects and analyzes information related to this case and presents the necessary legal information, thereby supporting rapid decision-making. In this way, the system described in the present invention reduces the burden on legal affairs work for companies and enables efficient work execution.

[0684] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0685] Step 1: The user enters work-related information through the terminal. Here, the contract summary and specific requirements are entered in text format into the input fields. The entered data is sent directly to the server and becomes the basis for the next processing step.

[0686] Step 2: The server accesses the internal database based on the received business information and collects relevant records and documents. Specifically, it executes database queries to retrieve contract history and related past data. This further deepens the business information and retrieves relevant data.

[0687] Step 3: The server analyzes the collected data using a generative AI model. Here, natural language processing technology is utilized to extract keywords and context from business information. The server receives the collected data as input and outputs important business-related information as the analysis result.

[0688] Step 4: The server accesses an external standards database to retrieve the latest standards information. It sends requests to the legal database via an API to retrieve relevant legal information, ensuring that it always reflects the most up-to-date legal information. In this step, the server identifies the standards information relevant to the business based on the information obtained from the external database.

[0689] Step 5: The terminal visually presents the analysis results and normative information received from the server to the user. The presented information is organized in a dashboard format and displayed in a way that is easy for the user to understand. Here, the user can make decisions based on the necessary information.

[0690] Step 6: The server automatically generates the necessary documents and communications based on templates using an automated generation and transmission method, and prepares them for transmission to the relevant parties. Automation is achieved by utilizing a generation AI model to create draft contract documents and emails and export them in the specified format.

[0691] Step 7: The user previews the document generated on the terminal and reviews its content. They enter feedback and make corrections as needed, and then submit it to the relevant parties after final confirmation. This step is the final review, and the feedback is sent to the server and used to improve accuracy in future projects.

[0692] (Application Example 1)

[0693] 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".

[0694] In modern electronic transactions, users face challenges in maintaining compliance with frequently changing laws and regulations. While it is essential to quickly and accurately grasp relevant legal information and take appropriate action, traditional manual processes and systems have limitations, leaving users constantly at risk of inadvertently disregarding regulations. In this context, there is a need for a means to proactively identify legal risks associated with transactions and provide appropriate information in real time.

[0695] 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.

[0696] In this invention, the server includes information gathering means for acquiring business data, information analysis means for analyzing the collected information and identifying relevant regulatory information, regulatory information matching means for acquiring legal information from an external legal database and matching it with the identified regulatory information, and transaction regulatory analysis means for collecting transaction data, analyzing the regulatory risks of transactions, and providing legal information to users in advance. This enables users to grasp legal risks more accurately and take necessary actions appropriately and quickly.

[0697] "Business data" refers to information about all transactions and business processes in a company's activities, including transaction history and contract details.

[0698] "Information gathering means" refers to processes or systems for efficiently acquiring necessary data, and may include access to internal and external databases.

[0699] "Information analysis means" refers to the techniques and processes used to analyze collected data and extract important information and patterns from it.

[0700] "Regulatory information" refers to information about laws and regulations that apply to specific business operations or transactions.

[0701] "Regulatory information matching means" refers to a function or system for comparing acquired legal information with analyzed data and identifying matching sections.

[0702] "Information display means" refers to a mechanism or device for presenting analysis results in a way that is easy for users to understand.

[0703] "Automated generation and distribution means" refers to a process or system for automatically creating business-related documents and emails and sending them to relevant parties.

[0704] "Learning methods" refer to mechanisms that receive feedback from users and use that information to improve the system's performance and accuracy.

[0705] "Transaction regulatory analysis tools" refer to means for identifying legal risks from transaction data and providing users with appropriate legal information based on those findings.

[0706] To realize this invention, an AI agent system plays a central role in efficiently collecting and analyzing business data and providing users with appropriate legal information. The server acquires business data from various data sources using information collection means. Specific data sources include internal databases and external legal databases, enabling comprehensive information collection.

[0707] The server then analyzes the data collected using information analysis tools with advanced natural language processing techniques. In this process, natural language processing tools and AI models play a crucial role in identifying relevant regulatory information and potential legal risks. The analyzed information is then cross-referenced with the latest legal information obtained from external legal databases using regulatory information matching tools. This matching process confirms that the identified risk elements are consistent with applicable laws and regulations.

[0708] Furthermore, the server uses transaction regulatory analysis tools to analyze transaction data, particularly in electronic payment services, and provides users with real-time legal information related to transactions. This allows users to proactively prevent legal risks. In addition, related documents and communications are automatically generated and distributed to relevant parties using automated generation and distribution tools. All of these processes are continuously improved through user feedback, and the overall accuracy of the system is enhanced through learning mechanisms.

[0709] For example, if a user is trying to purchase an overseas product, this system will analyze information on import regulations and related tax laws for that product and present the necessary measures to the user. An example of a prompt message would be, "Transaction: Please analyze the latest import regulations and related laws based on User ID 123, Amount 5000 JPY, Product ID 456." This allows users to proceed with transactions with confidence.

[0710] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0711] Step 1:

[0712] The server receives transaction-related input from users using information gathering methods to acquire business data. This input includes detailed information such as user ID, amount, and product ID. Based on the collected business data, the server searches relevant internal and external databases and aggregates the necessary information.

[0713] Step 2:

[0714] The server processes the collected business data and related information using information analysis tools. This process utilizes generative AI models and natural language processing techniques to analyze the data and identify relevant regulatory information and risk factors. The analysis results are output as data and passed on to the next processing step.

[0715] Step 3:

[0716] The server uses regulatory information matching means to compare the analysis results with legal information obtained from an external legal database. In this process, the analysis data is matched with the latest regulatory information, and the relevant laws are identified. The matching regulatory information is output and ready to be provided to the user.

[0717] Step 4:

[0718] The server analyzes transaction data in detail, particularly in electronic payment services, through transaction regulatory analysis tools. Based on this analysis, legal information and risk factors related to the user's transactions are identified, and the data is processed so that users can receive this information in real time.

[0719] Step 5:

[0720] The server uses information display means to present analysis results and legal information to the user. In this step, advice regarding legal risks and regulations is displayed on the user's terminal, and the user can receive this information in an easy-to-understand format.

[0721] Step 6:

[0722] The server utilizes automated generation and distribution methods to generate necessary documents and emails for business operations and send them to relevant parties. In this process, appropriate documents are automatically created based on collected and analyzed information and securely delivered to the specified recipients.

[0723] Step 7:

[0724] The user reviews the presented information and generated documents, and sends their feedback to the server. The server uses learning mechanisms to perform data calculations based on the received feedback to improve the system's accuracy, and uses this information for future analyses.

[0725] 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.

[0726] This invention is implemented in the form of an AI agent system that aims to streamline corporate legal operations, combined with an emotion engine that recognizes user emotions. This system not only analyzes business data and provides legal information, but also analyzes user emotions and presents information based on those emotions, thereby providing more personalized support.

[0727] First, the user inputs or selects business data through a terminal. The server then uses this data to collect relevant business information from internal systems and databases.

[0728] Once the data is collected, the server analyzes it using natural language processing technology. In this process, relevant legal information and risk factors are identified, and the latest legal information is retrieved from an external legal database and compared with the collected data.

[0729] Furthermore, the server analyzes user input and interaction data obtained from the terminal to recognize the user's emotions. The emotion engine identifies emotions from the user's text input, voice data, and behavioral data, and adjusts what information the system presents and how.

[0730] Through the information presentation mechanism, the terminal presents the user with analysis results and related legal information. In this process, the information provided to the user is adjusted by an emotion engine. For example, for users experiencing stress, the system optimizes the display to show particularly important information concisely.

[0731] Furthermore, using automated generation and transmission methods, the server automatically generates necessary documents and emails. These generated documents are provided to the user as a preview on their terminal, allowing the user to review the content and make any necessary corrections or approvals.

[0732] User feedback is collected through the device and sent to the server along with sentiment data. The server uses this feedback to reinforce the learning process and improve the system's accuracy and effectiveness.

[0733] As a concrete example, consider a scenario where a legal professional uses this system to conduct a risk assessment before the market launch of a new product. When the professional inputs business data, the server immediately identifies relevant laws and regulations, and the emotion engine senses the user's stress level. Accordingly, information that concisely summarizes explanations of complex laws and regulations is provided to the terminal. Necessary approval documents are automatically generated and sent to the relevant parties after user confirmation. Through this process, the present invention supports the rapid execution of legal work and improves the user experience.

[0734] The following describes the processing flow.

[0735] Step 1:

[0736] The user inputs or selects business data using a terminal. This action sends the business information to the system, initiating the analysis process.

[0737] Step 2:

[0738] The server accesses internal systems and databases to collect data related to the specified task. This data includes past work history and related documents.

[0739] Step 3:

[0740] The server uses natural language processing technology to analyze the collected data. This analysis extracts keywords and contexts related to the business, and based on this, legal information and risk factors are identified.

[0741] Step 4:

[0742] The server accesses an external legal database to retrieve the latest legal information. The retrieved legal information is then compared with the analysis results, and the laws relevant to the business are extracted.

[0743] Step 5:

[0744] The device sends user input data to the emotion engine, which analyzes the user's emotions. The emotion engine identifies the user's emotional state and adjusts the information presented based on that state.

[0745] Step 6:

[0746] The information presentation mechanism allows the terminal to present analysis results and related legal information to the user. This information is displayed in a format adjusted by the emotion engine; for example, important information is presented concisely to users experiencing high stress levels.

[0747] Step 7:

[0748] The server utilizes automated generation and sending mechanisms to generate necessary documents and emails based on templates. The generated documents and emails are then provided to the user as a preview via their terminal.

[0749] Step 8:

[0750] The user reviews the generated document on their device and makes corrections or approvals as needed. Approved documents and emails are then sent to the relevant parties by the server.

[0751] Step 9:

[0752] The device collects user feedback and sends it to the server along with sentiment data. The server uses this feedback as a learning tool to improve the overall accuracy of the system.

[0753] (Example 2)

[0754] 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".

[0755] Traditional business support systems have faced challenges in legal work, where large amounts of information must be processed. These systems are inefficient in organizing and presenting information, and they fail to consider the user's feelings when providing information. As a result, users often experience stress when understanding information or making decisions.

[0756] 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.

[0757] In this invention, the server includes data collection means for acquiring information related to business operations, data analysis means for analyzing the collected information and identifying relevant regulatory information, and emotion recognition means for recognizing the user's emotions and adjusting the analysis results. This enables efficient information presentation that takes the user's emotions into consideration.

[0758] "Information related to business operations" refers to all data and information related to the operations carried out by a company or organization, and this includes information for compliance with laws and regulations.

[0759] "Data collection means" refers to the means by which a system acquires information entered or selected by a user and aggregates the necessary business data.

[0760] "Data analysis methods" refer to means of analyzing collected information using natural language processing technology and other methods to identify relevant regulatory information and risk factors.

[0761] A "regulatory information matching means" is a means for matching regulatory information obtained from an external regulatory database with information identified internally.

[0762] An "information presentation means" is a means of displaying information to the user based on analysis results and providing necessary documents and communications in an easy-to-understand format.

[0763] An "automatic generation and transmission method" is a means of automatically creating documents and emails necessary for business operations and sending them to relevant parties.

[0764] "Learning methods" are means of improving the functionality and accuracy of a system based on feedback obtained from users.

[0765] "Emotion recognition means" are tools for analyzing a user's emotional state based on their text input and interaction data, and for adjusting the information presented accordingly.

[0766] This invention aims to streamline information management in business support systems and reduce the burden on users. The system operates primarily through the interaction of servers, terminals, and users.

[0767] First, the user uses their device to input or select work-related information. In this process, the work-related information is imported into the system and proceeds to the next analysis step. The devices used are expected to be general-purpose computers or smartphones.

[0768] The server receives collected business information and performs analysis using natural language processing techniques with data analysis tools. Specifically, it uses libraries such as Python's NLTK and spaCy to perform semantic analysis and keyword extraction of the information, and then identifies relevant regulatory information based on the identified information. The server also accesses external regulatory databases (e.g., government legal databases) to obtain the latest information and compare it with internal data.

[0769] The emotion recognition system also functions within the server, analyzing user input and interaction data. The user's text and voice data are analyzed using Google Cloud's Natural Language API to identify stress levels and emotional states. Based on these results, the server provides the user with tailored information through an information presentation system. This information is presented on the device in a clear and organized manner, incorporating features designed to reduce stress.

[0770] Furthermore, the server uses an automated generation and transmission mechanism to automatically create and finalize the necessary documents and emails for business operations. Users preview the generated documents on their terminals, review the details, make any necessary changes, and finally send them.

[0771] In this invention, user feedback is crucial. This feedback is collected via a terminal, transmitted to a server, and used to improve the system through a learning mechanism.

[0772] As a concrete example, consider the case of verifying legal compliance when launching a new product into the market. The user enters instructions into the system as prompts, such as "Identify the laws and regulations related to the market launch risk assessment of the new product," or "Present important legal information concisely, taking into consideration the user's feelings," and starts the process. This form of system achieves both the rapid execution of legal tasks and an improved user experience.

[0773] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0774] Step 1:

[0775] Users input business-related data using a terminal. In this input process, users provide specific information to the terminal according to their business objectives, such as information regarding the market launch of a new product. The output is business data necessary for the server to use in subsequent processing.

[0776] Step 2:

[0777] The server receives the input data and, via data collection mechanisms, gathers relevant internal information and similar past case data from the internal database. Specifically, it executes database queries to obtain the necessary data and outputs it as an initial list of data to be analyzed.

[0778] Step 3:

[0779] The server analyzes business data using natural language processing techniques. In this step, Python's NLTK and spaCy libraries are used to analyze the input data and extract important keywords. The input is business data provided by the user, and the output is the analyzed information and related regulatory information.

[0780] Step 4:

[0781] The server accesses an external regulatory database to retrieve the latest legal information and compares it with the previously identified information. Specifically, it uses an API to query information from the external database and retrieve regulatory information. After the comparison, the output is the legal information that has been adjusted.

[0782] Step 5:

[0783] To recognize a user's emotions, the server performs sentiment analysis using user text and interaction data obtained from the device. It uses Google Cloud's Natural Language API to determine the user's stress level and emotions. The input is user text and interaction data, and the output is the user's emotional state.

[0784] Step 6:

[0785] The device analyzes and recognizes emotions from the data, then presents the user with adjusted information. Here, information presentation methods are used to display the information in a way that is easy for the user to understand. The input consists of analyzed data and emotion data, while the output is concise and personalized information presented to the user.

[0786] Step 7:

[0787] The server uses an automated generation and sending mechanism to generate the necessary documents and emails, and provides a preview on the user's terminal. The user reviews this on the terminal, makes any necessary corrections, and then issues a final sending instruction. The input is the generated document data, and the output is the reviewed and approved document.

[0788] Step 8:

[0789] Feedback is collected from users via their devices and sent to a server. The server uses this feedback to improve the accuracy and effectiveness of the system through learning mechanisms. The input is user feedback and sentiment data, and the output is an augmented learning algorithm.

[0790] (Application Example 2)

[0791] 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".

[0792] In current in-store customer service, it is difficult to properly understand customer emotions and provide services accordingly. This can lead to decreased customer satisfaction and lost sales opportunities. The goal is to solve this problem and achieve more personalized customer service.

[0793] 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.

[0794] In this invention, the server includes data acquisition means for obtaining business information, data analysis means for analyzing the acquired information and identifying relevant rule information, matching means for obtaining rule information from an external rule database and aligning it with the identified rule information, and emotion analysis means for analyzing the user's emotions and optimizing information provision. This makes it possible to grasp the customer's emotional state in real time and provide information and customer service accordingly.

[0795] "Data acquisition means" refers to the mechanisms and techniques used to acquire business information.

[0796] "Data analysis means" refers to mechanisms and techniques for analyzing acquired business information and identifying relevant rule information.

[0797] A "matching method" refers to a mechanism or technique for aligning regulatory information obtained from an external regulatory database with specified rule information.

[0798] "Information display means" refers to mechanisms or techniques that display relevant information to users based on matching results.

[0799] "Generation and transmission means" refers to mechanisms and techniques for automatically generating and transmitting documents and communications necessary for business operations.

[0800] A "learning method" refers to a mechanism or technique aimed at improving system accuracy by updating records based on user feedback.

[0801] "Emotional analysis tools" refer to mechanisms and techniques for analyzing users' emotions and optimizing information provision.

[0802] A system for carrying out this invention includes a smart device and a server to support customer service operations in a physical store. As an example of a smart device, smart glasses are used. These smart glasses are equipped with a camera and a microphone to capture the customer's facial expressions and voice.

[0803] The server receives data transmitted from smart glasses and collects business information using data acquisition means. Subsequently, data analysis means analyze this data to identify relevant regulatory information. Natural language processing technology is used for the analysis. After obtaining the necessary regulatory information from an external regulatory database, matching means integrate this information.

[0804] To analyze user emotions, the server uses emotion analysis tools to identify customer emotions from acquired data. For example, it may use facial recognition and emotion analysis libraries (e.g., OpenCV, Affectiva API) to identify emotions from facial expressions and voice.

[0805] The information display system shows relevant information on smart glasses based on matching and sentiment analysis results. For example, it displays product information and campaign information that the customer might be interested in.

[0806] Furthermore, the generation and transmission mechanism allows for the automatic generation of necessary documents and communications for business operations and their transmission to relevant parties. User feedback is recorded through a learning mechanism, and the system's accuracy is improved based on this feedback.

[0807] As a concrete example, consider a scenario where a customer shows interest in a new electronic product. In this case, the smart glasses display detailed specifications and reviews of the product. If the system analyzes that the customer is excited or interested, it will also provide information that highlights the product's special features and limited-time offers.

[0808] An example of a prompt for the AI ​​model is: "Consider the customer's current emotions and decide what information should be displayed on the smart glasses. The customer's area of ​​interest is new electronic products. Emotion analysis result: Excitement, curiosity." Using this prompt, more effective customer service can be provided.

[0809] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0810] Step 1:

[0811] The server receives customer facial expression and voice data transmitted from smart glasses. Based on this input data, it uses data acquisition methods to extract business information such as basic customer information and past purchase history from the company's internal database. The output consists of various business-related information necessary for analysis.

[0812] Step 2:

[0813] The server uses data analysis tools to perform natural language processing and sentiment analysis on the received facial expression and voice data. The input data is analyzed using sentiment recognition libraries (e.g., OpenCV, Affectiva API) to identify the customer's current emotional state. The output is the customer's emotion, such as excitement or curiosity.

[0814] Step 3:

[0815] The server uses data analysis tools to extract relevant regulatory information from the acquired business data. Next, it retrieves the latest regulatory information from an external regulatory database and matches it using matching tools. The input data consists of business information and regulatory information, and the output is the result of identifying the relevant information.

[0816] Step 4:

[0817] The server uses an information display mechanism to transmit information based on the identified results and sentiment analysis results to the smart glasses. Using the prompt text generated at this stage, the generating AI model presents information optimized for the customer. The input is sentiment and rule information, and the output is a customized information display for the customer.

[0818] Step 5:

[0819] The user (store clerk) interacts with customers based on information displayed on smart glasses. Customer reactions and feedback become new inputs, which are sent to the server and recorded by a generation and transmission system. The output is feedback data that helps improve the service.

[0820] Step 6:

[0821] The server learns from feedback to improve the accuracy of sentiment analysis and information presentation. The learning process is used to enhance the overall system performance for future customer interactions. Input consists of feedback data and feedback content, while output is the updated analysis model.

[0822] 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.

[0823] 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.

[0824] 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 robot 414.

[0825] 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.

[0826] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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."

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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 as being incorporated by reference.

[0843] The following is further disclosed regarding the embodiments described above.

[0844] (Claim 1)

[0845] Data collection methods for obtaining business data,

[0846] A data analysis means for analyzing the collected data and identifying relevant legal information,

[0847] A legal information matching means that obtains legal information from an external legal database and matches it with the identified legal information,

[0848] Information presentation means for presenting information to the user based on the matching result,

[0849] An automated generation and sending method for automatically generating and sending documents and emails necessary for business operations,

[0850] A learning method that updates records based on user feedback and improves system accuracy,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, comprising means for analyzing the meaning of documents using natural language processing technology when collecting business data.

[0854] (Claim 3)

[0855] The system according to claim 1, further comprising means for providing the user with a preview of the generated document or email and for sending it after obtaining the user's confirmation.

[0856] "Example 1"

[0857] (Claim 1)

[0858] Data collection methods for obtaining business information,

[0859] A data analysis means that analyzes the collected information using natural language processing technology and identifies relevant normative information,

[0860] A normative information matching means that obtains normative information from an external normative database and matches it with the identified normative information,

[0861] Information presentation means for presenting information to the user based on the matching result,

[0862] An automated generation and transmission method that automatically generates and sends documents and communications necessary for business operations based on templates,

[0863] A learning method that updates records based on user feedback and improves system accuracy,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, comprising means for visually presenting the analysis results and related normative information, and providing the user with the knowledge necessary to carry out their work.

[0867] (Claim 3)

[0868] The system according to claim 1, comprising means for providing a user with a preview of the generated document or communication and for transmitting it after obtaining the user's confirmation.

[0869] "Application Example 1"

[0870] (Claim 1)

[0871] Information gathering methods for acquiring business data,

[0872] Information analysis means for analyzing the collected information and identifying relevant regulatory information,

[0873] A regulatory information matching means that obtains legal information from an external legal database and matches it with the identified regulatory information,

[0874] Information display means for presenting information to the user based on the matching result,

[0875] An automated generation and distribution method that automatically generates and distributes documents and communications necessary for business operations,

[0876] A learning method that updates records based on user feedback and improves the machine's accuracy,

[0877] A trading regulatory analysis tool for collecting trading data, analyzing trading regulatory risks, and providing users with legal information in advance,

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, which includes means for analyzing the meaning of documents using natural language processing technology when collecting business data, and presents regulatory risks to the user in real time.

[0881] (Claim 3)

[0882] The system according to claim 1, comprising means for providing a user with a preview of generated documents and communications, and for sending them after obtaining user confirmation, and providing legal compliance status based on transaction data.

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

[0884] (Claim 1)

[0885] Data collection methods for obtaining information related to business operations,

[0886] A data analysis means for analyzing the collected information and identifying relevant regulatory information,

[0887] A regulatory information matching means that obtains regulatory information from an external regulatory database and compares it with the identified regulatory information,

[0888] Information presentation means for presenting information to the user based on the matching result,

[0889] An automated generation and transmission method for automatically generating and sending documents and communications necessary for business operations,

[0890] A learning method that updates records based on user feedback and improves system accuracy,

[0891] An emotion recognition means that recognizes the user's emotions and adjusts the analysis results,

[0892] A system that includes this.

[0893] (Claim 2)

[0894] The system according to claim 1, comprising means for analyzing the meaning of documents using spatial language processing technology when collecting business information.

[0895] (Claim 3)

[0896] The system according to claim 1, comprising means for providing a user with a preview of the generated document or communication and for transmitting it after obtaining the user's confirmation.

[0897] "Application example 2 when combining with an emotional engine"

[0898] (Claim 1)

[0899] Data acquisition methods for obtaining business information,

[0900] A data analysis means that analyzes acquired information to identify related rule information,

[0901] A matching means for obtaining regulatory information from an external regulatory database and aligning it with identified rule information,

[0902] Information display means that displays information to the user based on the matching results,

[0903] A generation and transmission method that automatically generates and sends documents and communications necessary for business operations,

[0904] A learning method that updates records based on user feedback and improves the accuracy of the system,

[0905] A sentiment analysis tool that analyzes user emotions and optimizes information provision,

[0906] A system that includes this.

[0907] (Claim 2)

[0908] The system according to claim 1, comprising means for analyzing the meaning of text using natural language processing technology when acquiring business information, and for detecting emotional states.

[0909] (Claim 3)

[0910] The system according to claim 1, comprising means for providing a user with a preview of the generated document or communication, and for sending it after obtaining the user's confirmation, and further for adjusting the presentation of information according to emotions. [Explanation of Symbols]

[0911] 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. Data collection methods for obtaining business data, A data analysis means for analyzing the collected data and identifying relevant legal information, A legal information matching means that obtains legal information from an external legal database and matches it with the identified legal information, Information presentation means for presenting information to the user based on the matching result, An automated generation and sending method for automatically generating and sending documents and emails necessary for business operations, A learning method that updates records based on user feedback and improves system accuracy, A system that includes this.

2. The system according to claim 1, comprising means for analyzing the meaning of documents using natural language processing technology when collecting business data.

3. The system according to claim 1, further comprising means for providing the user with a preview of the generated document or email and for sending it after obtaining the user's confirmation.

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