system

Abstract

システムを提供する。【解決手段】法令情報をデータ源から自動的に取得し、法令変更を検出する手段と、法令情報をデータ源から自動的に取得し、法令変更を検出する手段と、自然言語処理を用いて、法令変更内容を解析し、主要な変更点を抽出する手段と、解析された変更内容が事業活動に及ぼす影響を評価し、関連する担当者を特定する手段と、対応策を自動生成し、担当者に通知するための手段と、法令変更履歴を記憶装置に保存し、探索可能な状態で管理する手段と、履歴に基づく参考事例を提供する手段と、生成された対応策を情報端末に送信し、推奨行動計画を表示する手段と、を含むシステム。

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] While enterprises are required to respond promptly and efficiently to frequent changes and abolitions of laws and regulations, in the conventional method, since changes in laws and regulations are manually detected and analyzed, it takes a lot of time and labor and is prone to human errors. In addition, since the impact assessment of law changes and the formulation of countermeasures depend on manual work, information sharing with relevant departments is delayed, and the effectiveness of the enterprise's compliance with laws and regulations may decrease.

Means for Solving the Problems

[0005] This invention provides means for automatically acquiring legal information and detecting changes in laws and regulations, and further comprises means for analyzing the changes using natural language processing, conducting impact assessments, and identifying relevant personnel. Therefore, it enables rapid response by automatically generating appropriate countermeasures based on the legal changes and notifying the relevant personnel of these countermeasures. Furthermore, it stores the history of legal changes in a database and manages it in a searchable format, making it easy to refer to past changes. In addition, by registering the generated countermeasures in a project management system for continuous management and using evaluation models, it achieves the streamlining and improvement of a company's legal compliance system.

[0006] "Legal information" refers to information consisting of documents such as laws, orders, notices, and guidelines enacted by the national or local government.

[0007] "Data source" refers to an external system or database used to obtain legal information.

[0008] "Automatic acquisition" refers to a process in which a program directly collects data without requiring any human intervention.

[0009] "Amendment to laws and regulations" refers to a phenomenon in which existing laws or regulations have been modified, added to, or deleted.

[0010] "Means of detection" refers to a method or device for a system to recognize a change and confirm a specific event.

[0011] "Natural language processing" refers to the techniques or set of processes that enable computers to understand and process human language.

[0012] "Analysis" is the process of breaking down complex information and data, understanding them, and extracting their meaning.

[0013] "Impact assessment" is the act of determining the degree and scope of the impact that a particular change or event will have on an organization or process.

[0014] The "means for identifying a person in charge" refers to a method or device for identifying a person with specific business or responsibilities and providing necessary information.

[0015] The "countermeasure" refers to a course of action or plan for dealing with specific issues or problems.

[0016] "Automatically generated" means that a program or system creates necessary information or output without human intervention.

[0017] "To notify" is a process of delivering specific information or a message as a response to the necessary person or team.

[0018] A "database" is a storage system that systematically organizes information and enables easy search and management.

[0019] "Searchable" means a state where specific information can be easily retrieved.

[0020] A "project management device" is a combination of hardware and software for organizing a project and monitoring its progress.

[0021] An "evaluation model" refers to a set of rules or mathematical formulas for evaluating the quality or characteristics of an object using specific criteria or algorithms.

Brief Description of the Drawings

[0022] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0023] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0024] First, the terms used in the following description will be explained.

[0025] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0026] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0027] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0028] 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).

[0029] 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."

[0030] [First Embodiment]

[0031] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0032] 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.

[0033] 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).

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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".

[0043] This invention relates to a system that enables companies to respond quickly to changes in laws and regulations. The system automates the entire process from collecting and analyzing legal information to impact assessment, proposing countermeasures, and providing notifications, thereby streamlining the compliance process.

[0044] First, the server accesses data sources that provide legal information and periodically checks for new laws and notifications. Because this information is acquired automatically, it is more accurate and requires less effort compared to manual data collection.

[0045] Users can view changes in data collected by the server via their terminals. The server uses an analysis module to analyze these legal changes using natural language processing techniques and extract key changes. At this stage, a pre-configured evaluation model is used to assess which business processes within the company the legal changes will affect.

[0046] For example, if new environmental regulations are announced, the server analyzes them and identifies any potential impacts on the manufacturing department. Based on this information, the server generates necessary countermeasures and promptly notifies the relevant personnel. The notification includes specific action plans and priorities, allowing users to begin taking action immediately.

[0047] Furthermore, the server stores a history of legal changes in a database. This allows users to easily search and refer to past change histories, enabling them to use past countermeasures as a reference for similar situations.

[0048] Furthermore, the system integrates with the project management device and automatically registers the generated corresponding tasks. Users can access the project management device's interface via their terminals to visualize progress and modify or adjust tasks as needed. In this way, the entire process is automated, creating an environment where legal compliance is efficiently implemented as part of the company's strategic activities.

[0049] The following describes the processing flow.

[0050] Step 1:

[0051] The server periodically accesses multiple data sources that provide legal information to check for any new laws or changes that have been announced. This process is automated by scheduled jobs and runs regularly without human intervention.

[0052] Step 2:

[0053] The server analyzes newly detected legal information using a natural language processing engine. This extracts the main changes and key points of the legal documents. The analysis includes keyword extraction and grammatical structure analysis.

[0054] Step 3:

[0055] Based on the analysis results, the server evaluates which departments and business processes within a company will be affected by the legal changes. This impact assessment is performed using pre-configured evaluation models and rule-based systems.

[0056] Step 4:

[0057] The server automatically generates necessary countermeasures based on the impact assessment. These countermeasures may include specific action steps and implementation periods, enabling relevant departments to quickly begin taking action.

[0058] Step 5:

[0059] The server notifies the responsible party of the generated countermeasures and a summary of the changes. The notification is distributed via email or the company's internal messenger system.

[0060] Step 6:

[0061] The server stores all legal changes and corresponding countermeasures in a database. This allows users to search and refer to past legal change history using their terminals.

[0062] Step 7:

[0063] The terminal automatically registers the corresponding tasks generated in the project management device. This allows the user to monitor the progress of tasks through the project management tool interface and make adjustments as needed.

[0064] (Example 1)

[0065] 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."

[0066] For modern businesses, responding quickly and efficiently to changes in laws and regulations is a crucial challenge. However, collecting and analyzing legal information, assessing impacts, and developing countermeasures requires considerable time and effort, which reduces operational efficiency. Furthermore, the manual processes involved in creating, implementing, and managing countermeasures make it difficult to ensure accuracy and prompt response.

[0067] 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.

[0068] In this invention, the server includes means for automatically acquiring legal information from an information provision device and detecting changes in the law, means for analyzing the content of the legal changes using natural language processing technology and extracting important changes, and means for automatically generating countermeasures using generative AI technology and notifying the person in charge of operations. This enables a quick and accurate response to changes in the law and facilitates the smooth execution of operations.

[0069] "Legal information" refers to all information relating to laws and regulations, including the latest changes in laws and regulations that companies need to comply with them.

[0070] An "information provision device" refers to a database or online resource for obtaining legal information from external sources, and it is possible to obtain the latest legal information through this device.

[0071] "Natural language processing technology" refers to the technology that enables computers to understand, analyze, and manipulate human language, and is a technology that can analyze text and speech to extract their meaning.

[0072] "Significant changes" refer to points in legal revisions that are deemed to have a direct impact on a company's business processes.

[0073] "Generative AI technology" refers to technology that uses AI technology to generate new information and structures, and in this invention, it is used for generating countermeasures, etc.

[0074] An "information storage device" refers to a recording medium or database system used to store data and information, and is used to facilitate the storage and retrieval of information.

[0075] A "planning and management system" refers to a management system that manages the execution of projects and tasks, and makes their progress visible and adjustable.

[0076] "Evaluation criteria" refer to pre-established guidelines or standards used to assess the impact of changes in laws and regulations on a company's operations, and are used to determine appropriate responses.

[0077] This invention provides a system necessary for companies to respond quickly to changes in laws and regulations. In its specific implementation, the system is designed so that servers, terminals, and users each play their respective roles, and the compliance process is carried out efficiently.

[0078] The server first automatically retrieves legal information from the information provider. This is done using a program that periodically retrieves the latest legal information via a database or API. This information is collected by the server and then proceeds to the next analysis process; currently, programming languages ​​such as Python and request libraries are used for this process.

[0079] Next, the server analyzes the legal information obtained using natural language processing technology. For this analysis, for example, it uses spaCy, a Python natural language processing library, to analyze important keywords and sentence structures from legal documents and extract changes that are important to the company.

[0080] The analyzed information is then used with generative AI technology to match user-related assignment information and generate specific countermeasures. This countermeasure generation utilizes a generative AI model to create action plans tailored to the company's business processes.

[0081] Users receive notifications sent from the server via their devices. These notifications include specific actions and priorities that have been generated, allowing users to quickly take action based on them. For example, a prompt such as, "Analyze the changes in new environmental regulations from a manufacturing department's perspective and propose necessary actions," will prompt the generating AI to present the optimal solution to the department.

[0082] Furthermore, since the server stores a history of legal changes in an information storage device, users can easily search past data and refer to examples. This enables a swift and effective response when similar legal changes occur.

[0083] These processes are integrated with the project management system, and the generated corresponding tasks are automatically registered. Users can visualize task progress on the project management interface via their terminals and make adjustments as needed to improve overall work efficiency.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The server accesses databases and external APIs that provide legal information to retrieve the latest laws and regulations. It uses the URL or API key of the information source as input and generates text data of the retrieved laws as output. This step uses HTTP communication libraries, such as request libraries, to perform the specific actions of retrieving data from the web.

[0087] Step 2:

[0088] The server analyzes the acquired text data of laws and regulations using a natural language processing library. It receives the text of the laws and regulations as input, extracts important changes through data processing, and generates a list of changes as output. Specifically, it uses NLP libraries such as spaCy to analyze the meaning and structure of the text.

[0089] Step 3:

[0090] The server evaluates the impact of the analyzed changes on business processes. Using a list of changes and data on the company's business processes as input, it generates a list of affected business processes as output. This step includes specific actions to perform simulations of business processes using a pre-configured evaluation model.

[0091] Step 4:

[0092] The server generates specific countermeasures using generative AI technology based on the evaluation results. It takes a list of affected business processes as input and generates detailed countermeasures and action plans as output. This process includes leveraging a generative AI model and using prompt statements to suggest the optimal countermeasure.

[0093] Step 5:

[0094] The server sends the generated countermeasures to the relevant personnel via email or a notification system. It receives information about the countermeasures as input and sends notifications to the appropriate personnel as output. Specifically, it uses email sending functionality and notification APIs to transmit information in real time.

[0095] Step 6:

[0096] The server stores a history of all legal changes in a database and manages it in a searchable format for the future. It receives legal changes and related data as input and records them in the database as output. In this step, a database management system such as SQL is used to implement storage and search functions.

[0097] Step 7:

[0098] The server registers the generated countermeasures in the project management system and visualizes the progress. It uses information on the countermeasures and related tasks as input and generates tasks registered in the project management system as output. Specifically, this includes the operation of automatically registering tasks through an interface to the project management tool API.

[0099] (Application Example 1)

[0100] 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."

[0101] Changes in laws and regulations can have a significant impact on a company's business activities, and there is a particular need to respond quickly to changes in security-related laws and regulations. However, manually acquiring relevant information from vast amounts of legal data, analyzing it, and evaluating its impact on business activities is a highly specialized and inefficient process. There is a need for solutions to these challenges and to streamline the legal compliance process.

[0102] 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.

[0103] In this invention, the server includes means for automatically acquiring legal information from a data source and detecting changes in the law; means for analyzing the changes in the law using natural language processing and extracting key changes; means for evaluating the impact of the analyzed changes on business activities and identifying the relevant personnel; means for automatically generating countermeasures and notifying the personnel; means for storing the history of changes in the law in a storage device and managing it in a searchable state; means for providing reference cases based on the history; and means for transmitting the generated countermeasures to an information terminal and displaying a recommended action plan. This makes it possible to automatically evaluate the impact of changes in the law on a company's security-related activities and to respond quickly and efficiently.

[0104] "Legal information" refers to information about laws and regulations enacted by countries and regions, and is data that companies and organizations need for legal compliance.

[0105] "Data source" refers to the source from which legal information and related data are collected, and includes online databases and official websites.

[0106] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language, and is used in applications such as analyzing text data.

[0107] "Analysis" is the process of breaking down large amounts of data, organizing its contents, and extracting useful information from it.

[0108] "Changes" refer to the alterations in content that occur when laws are amended, and are recognized as differences between the old and new laws.

[0109] "Business activities" refer to the process of producing and providing goods and services that a company undertakes for the purpose of economic gain.

[0110] "Impact assessment" is the process of analyzing the results and impacts that changes in laws and regulations have on various activities and departments within a company.

[0111] The term "person in charge" refers to a person responsible for carrying out specific tasks or duties, and is a person who assumes a specific role within a company.

[0112] A "recommended action plan" is a plan that outlines a series of specific actions that are desirable to implement in response to changes in laws and regulations.

[0113] The system that realizes this invention operates primarily using a cloud server, user terminals, a database, and a natural language processing engine. The server periodically accesses data sources that provide legal information to detect whether new laws have been issued or amended. The data sources used include online databases of official organizations and public government websites.

[0114] The server analyzes legal information obtained using natural language processing technologies such as Google Cloud Natural Language API. This analysis extracts key changes from a comparison of old and new versions of the law. Subsequently, machine learning models such as TENSORFLOW® are used to evaluate the impact of the extracted changes on the company's business activities. Evaluation models set up for each department of the company are used for the impact assessment.

[0115] Once the relevant personnel are identified, the server automatically generates a response plan and sends it to the user's device via push notification. This notification includes a recommended action plan and priorities. The data is managed in Firebase Realtime Database and other systems, accumulating a change history and allowing users to search through past versions at any time.

[0116] For example, when a new data protection law is announced, the server immediately performs an analysis to identify the impact of the legal change on the company's security management. The generated countermeasures are notified to security personnel, who are instructed to implement protocols that comply with the new data protection regulations. In this way, a system is in place to respond quickly and efficiently to changes in legislation.

[0117] Example of a prompt:

[0118] "Please promptly analyze and report on the impact of the newly announced data protection legislation on our company's data security protocols. Please prioritize and indicate the necessary changes and countermeasures."

[0119] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0120] Step 1:

[0121] The server accesses data sources that provide legal information and periodically checks for new laws or changes. Inputs include URLs or API endpoints of the data sources, and output is the retrieved legal information data. These data sources include online databases and official websites, and information is collected using HTTP requests.

[0122] Step 2:

[0123] The server passes the acquired legal information to a natural language processing engine. The input is raw legal text data, and the output is parsed language data. The Google Cloud Natural Language API is used to extract major changes and structure their content.

[0124] Step 3:

[0125] The server inputs the analyzed changes into a machine learning model to evaluate the impact of the changes on business activities. The input is analyzed linguistic data, and the output is the impact assessment results. The impacts are classified and prioritized according to the company's existing assessment models, using tools such as TensorFlow.

[0126] Step 4:

[0127] The server automatically generates countermeasures based on the impact assessment results. The input is the impact assessment results data, and the output is a list of generated countermeasures. This includes specific action plans for each affected business process.

[0128] Step 5:

[0129] The server delivers the generated countermeasures as push notifications to the terminals of the relevant personnel. The input is a list of generated countermeasures, and the output is a recommended action plan for each notified personnel. Notifications are sent using methods such as Firebase Cloud Messaging, allowing personnel to take immediate action.

[0130] Step 6:

[0131] The server stores and manages a history of legal changes in a database in a retrievalable format. Input consists of all data after analysis and impact assessment, while output is historical data that users can search at any time. Firebase Realtime Database is used for data storage and management.

[0132] Step 7:

[0133] Users access historical data via their devices to derive efficient countermeasures for similar legal changes based on past cases. The input is the user's search query, and the output is a list of relevant past change histories and countermeasures resulting from this process. This allows users to efficiently leverage past experience.

[0134] 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.

[0135] This invention incorporates an emotion engine into an automated system for collecting legal information, assessing its impact based on changes, and developing countermeasures, thereby considering user emotions. This enables flexible communication that reflects the user's emotional state when providing notifications and information.

[0136] First, the server accesses data sources that provide legal information to automatically retrieve new legal information and detect changes. The amended laws are analyzed by a natural language processing engine to extract key changes. The server then evaluates the impact of these changes on business operations and identifies the relevant personnel. A pre-configured evaluation model is used for this impact assessment.

[0137] The emotion engine plays a crucial role in this notification process. When a user receives a notification of a change in legislation, the server uses the emotion engine to analyze the user's emotional state in real time and adjust the notification content and wording accordingly. For example, if a user is feeling stressed, the notification can be phrased in a more relaxing manner, demonstrating an emotionally sensitive approach.

[0138] As a concrete example, consider the case where a new amendment to the Labor Standards Act is made. This information is analyzed on a server, and its impact on the HR department is evaluated. When the person in charge receives this notification during a busy period, the emotion engine analyzes their emotional state and explains the amendment in calm language that does not cause them to feel burdened.

[0139] Furthermore, the emotion engine can receive user feedback and automatically improve its responses. This feedback loop allows the system to continuously adapt to user emotions and needs, aiming to provide more personalized services.

[0140] Furthermore, the server stores a history of legal changes in a database, and users can access past history via their terminals. In addition, the generated countermeasures are registered in the project management system, allowing users to manage tasks via their terminals. In this way, by incorporating an emotion engine, a more humane and flexible legal compliance system is built.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] The server connects to data sources that provide legal information and automatically retrieves new laws and changes periodically. This process may involve web scraping or APIs. When a change in legal information is detected, it is recorded.

[0144] Step 2:

[0145] The server uses a natural language processing engine to analyze the key points of legal changes on the acquired legal information. The analyzed data is then analyzed using text mining and text classification techniques to extract important changes.

[0146] Step 3:

[0147] Based on the analyzed changes, the server evaluates which business processes will be affected by the legal changes. This is done using an evaluation model that takes into account the relationship between pre-configured business processes and legal requirements.

[0148] Step 4:

[0149] The server automatically generates countermeasures based on the evaluation results. These countermeasures include specific implementation procedures and necessary modifications and improvements to affected business processes.

[0150] Step 5:

[0151] The server uses an emotion engine to analyze the user's emotional state. This is done to determine the user's current emotional state based on the user's past responses and real-time input data.

[0152] Step 6:

[0153] The server adjusts notification content to take the user's emotional state into consideration. For example, if the user is feeling stressed, the notification will be more concise and use gentler language. It will also provide supplementary information to alleviate anxiety.

[0154] Step 7:

[0155] The server sends the coordinated notifications to the designated user, utilizing various channels such as email notifications and internal messengers.

[0156] Step 8:

[0157] The server stores all legal changes and corresponding measures in a database, making them searchable and accessible to users via their terminals.

[0158] Step 9:

[0159] The terminal automatically registers the generated countermeasures with the project management system, making it easier for users to manage progress. Users can monitor tasks through the terminal and make necessary corrections.

[0160] (Example 2)

[0161] 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 will be referred to as the "terminal."

[0162] Changes in laws and regulations have a significant impact on business activities, making it crucial to collect this information in a timely manner and assess its impact on operations. However, there is no system in place that can efficiently collect and analyze information, assess its impact, and notify stakeholders. Furthermore, it is difficult to consider the emotional state of stakeholders when making notifications. Therefore, there is a need for a system that can respond flexibly and efficiently to changes in laws and regulations.

[0163] 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.

[0164] In this invention, the server includes means for automatically acquiring data from data sources that provide information and detecting changes; means for analyzing the changes in the data using natural language processing and extracting key points; and means for analyzing the emotional state of stakeholders using sentiment analysis technology and adjusting the content of notifications. This enables efficient collection and analysis of information on changes in laws and regulations, appropriate evaluation of the impact on business operations, and provision of information in a manner that takes into account the emotions of the recipients.

[0165] "Data sources that provide information" refers to data storage or network resources that provide, access to, and retrieve information related to laws and regulations.

[0166] "Means of detecting changes" refers to processes and systems for determining whether new content or updates have occurred in continuously monitored information.

[0167] "Natural language processing" refers to the technology that enables computers to understand, process, and analyze human language, and is particularly used for analyzing text data and extracting important information.

[0168] "Methods for extracting key points" refers to the process of identifying and extracting particularly important parts or parts where changes have occurred from a large amount of information.

[0169] "Emotional analysis technology" refers to techniques for analyzing and evaluating human emotions and psychological states from written and behavioral data.

[0170] "Means of adjusting notification content" refers to the process of changing the expression and method of information to suit the recipient, based on specific criteria or analysis results.

[0171] A "data storage device" refers to a hardware or software system that stores acquired data and its update history, and enables retrieval and use as needed.

[0172] A "work management system" refers to a software platform for effectively tracking and managing generated countermeasures and the progress of tasks.

[0173] An "evaluation model" refers to an analytical method or algorithm used to evaluate the impact of data and events on business operations based on specific rules or historical data.

[0174] This invention is a system that efficiently collects and analyzes information on changes in laws and regulations, conducts impact assessments related to business operations, and notifies stakeholders of the information while considering their emotional state. A specific example of this system is shown below.

[0175] The server continuously accesses data sources that provide legal information and automatically retrieves new information from specific websites and APIs. This process uses scraping tools and software for API access. Specifically, it uses Python's Beautiful Soup to retrieve data from web pages and, when necessary, downloads data directly using APIs. The retrieved data is stored in a database.

[0176] Next, the server uses a natural language processing engine to analyze the text of the acquired legal information. For example, it uses Python's NLTK or SpaCy to tokenize the legal text and extract key changes. This allows for the analysis of significant changes in the legal text.

[0177] Based on the analysis results, the server uses a pre-configured evaluation model to assess the impact on operations. This evaluation model is implemented as a rule-based algorithm or machine learning model, which identifies stakeholders. As a result of this process, a list of stakeholders who need to take responsibility is generated.

[0178] When sending notifications, the server uses sentiment analysis technology to analyze the emotional state of those involved. Related tools include Google's Perspective API and proprietary models. This analysis adjusts the notification content to match the recipient's emotional state. As a result, notifications received by users are expressed in a gentler and less burdensome manner.

[0179] For example, a user can input a prompt such as, "Please explain the changes in the new Labor Standards Act in a relaxed tone," into an AI model to generate a tailored notification.

[0180] In this way, the server can efficiently manage a series of information related to changes in laws and regulations, and appropriately notify users via their terminals. As a result, users can receive information in a way that is easy to understand and less stressful for them.

[0181] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0182] Step 1:

[0183] The server accesses data sources that provide information and retrieves new information. The input data is the URL or API endpoint of the data source, and the output is the raw data of the retrieved legal information. Specifically, the server uses a Python script to scrape web pages using the Beautiful Soup and Requests libraries to collect the necessary information.

[0184] Step 2:

[0185] The server detects changes from the acquired legal information. The input is the raw data acquired in step 1, and the output is the text of the changed parts. The server compares the new data with the information stored in the past database and runs a comparison algorithm to find the differences.

[0186] Step 3:

[0187] The server analyzes the information where changes have been detected using a natural language processing engine and extracts the main points of change. The input is the modified text detected in step 2, and the output is the extracted main points of change. In this step, the server tokenizes the text using Python's NLTK or SpaCy library and extracts important keywords and phrases.

[0188] Step 4:

[0189] The server evaluates the impact on business operations based on the extracted change points. The input is the major change points extracted in step 3, and the output is a list of affected business departments and stakeholders. The server uses a pre-configured rule-based evaluation model to determine the degree of impact of the given information on business operations and lists the relevant departments and individuals.

[0190] Step 5:

[0191] The server analyzes the user's emotional state before delivering a tailored notification. Inputs are past user behavior data and feedback, while output is an assessment of the user's current emotional state. The server utilizes Google's Perspective API and its own models to analyze the user's emotional tone and adjust the tone of the notification accordingly.

[0192] Step 6:

[0193] The server generates a coordinated notification and sends it to the user via the terminal. The input is the notification content coordinated in step 5, and the output is the final notification message the user receives. In this step, the server automatically delivers the notification using a mail server or messaging protocol.

[0194] Step 7:

[0195] Users receive notifications through their devices and refer to the legal change history as needed. Inputs are all notification and change history data, while outputs are the historical information the user refers to. Users can search the data and review past history using the application on their device or the web interface.

[0196] Step 8:

[0197] Users provide feedback, and the server continuously improves the system using a generative AI model. Input is user feedback data, and output is notifications and system behavior that reflect the improvements. The server incorporates the new feedback into its training data, retrains the AI ​​model, and enhances the user experience.

[0198] (Application Example 2)

[0199] 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".

[0200] Changes in laws and regulations often have a significant impact on business operations, but the process of acquiring this information, assessing its impact, and notifying relevant personnel may fail to consider the emotional state of users, potentially leading to stress and confusion. Therefore, there is a need for a system that takes users' feelings into consideration and provides notifications through appropriate communication.

[0201] 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.

[0202] In this invention, the server includes means for automatically acquiring legal information from information sources and detecting changes in laws and regulations; means for analyzing the content of legal changes using natural language processing and extracting key changes; means for evaluating the impact of the analyzed changes on business operations and identifying relevant individuals; notification means combining a sentiment analysis engine that analyzes the user's emotional state and flexibly adjusts notification content; and means for storing the history of legal changes in an information storage device and managing it in a searchable state. This makes it possible to appropriately provide information on legal changes while taking into consideration the user's emotions.

[0203] "Legal information" refers to official information relating to laws, rules, or regulations that may affect business or personal activities.

[0204] "Information source" refers to the original data provider or database used to obtain legal information.

[0205] "Natural language processing" is a technology that enables computers to understand and analyze human language, and makes it possible to automatically analyze text data.

[0206] "Assessing the impact" is the process of quantitatively or qualitatively determining how changes in laws and regulations will affect business operations.

[0207] "Relevant persons" refers to individuals whose duties or responsibilities may be directly affected by changes in laws and regulations.

[0208] An "emotion analysis engine" is a technology that analyzes a user's emotional state in real time and adjusts communication based on that information.

[0209] "Notification means" refers to a method or medium for communicating changed legal information to users.

[0210] An "information storage device" is a data storage facility that stores acquired information and makes it accessible or retrievable as needed.

[0211] The system of this invention provides users with information that is sensitive to their emotions through the acquisition, analysis, evaluation, notification, and history management of legal information.

[0212] The server automatically retrieves legal information from its sources and detects changes. The retrieved information is analyzed using a natural language processing engine to extract key changes. This analysis utilizes tools such as Python and TensorFlow, processing the information as text data. For impact assessment, a pre-configured evaluation model is used to determine how new legal changes will affect operations. The evaluation results are used to identify relevant individuals.

[0213] The device features an emotion analysis engine that analyzes the user's emotional state in real time. This engine uses generative AI models, such as OpenAI's GPT model, to analyze the user's current emotional state. Notification content is adjusted based on this emotion analysis, enabling flexible communication. For example, if a notification about a change in legislation is likely to cause stress, the content of the notification will be changed to more relaxing language.

[0214] Furthermore, the server comprehensively stores a history of legal changes in its information storage device and manages it so that users can search past history as needed. This function allows users to easily access past change history related to their work.

[0215] For example, if a user receives a notification about a new tax law revision, the sentiment analysis engine will detect the user's anxiety and send a notification using phrasing such as, "The new tax law revision will change your tax rate, but please rest assured that we will support you with the detailed procedures." An example of a prompt message would be, "We have detected that the user is anxious about the tax law change. Please suggest ways to soften the notification. The change is the application of a new tax rate."

[0216] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0217] Step 1:

[0218] The server automatically retrieves legal information from its sources. At this stage, it uses databases and APIs to collect the latest legal information. The input is legal information data, and the output is the retrieved raw legal data. The server accesses the data periodically or triggered by events, sending requests to receive data.

[0219] Step 2:

[0220] The server analyzes legal information obtained using a natural language processing engine and extracts key changes. The input is the raw legal data obtained, and the output is a list of the analyzed key changes. For text analysis of the data, Python libraries such as NLTK and SpaCy are used to identify and summarize the changes.

[0221] Step 3:

[0222] The server evaluates the impact of the analyzed changes on business operations and identifies the individuals involved. The input is a list of key changes, and the output is the impact analysis results and a list of the individuals involved. A pre-configured data model is used for the evaluation to quantitatively analyze how the changes affect business processes.

[0223] Step 4:

[0224] The device uses an emotion analysis engine to analyze the user's current emotional state. The input is data related to the user's emotions (e.g., past response history and current task content), and the output is the current emotional state. The engine uses a generative AI model to analyze the user's emotions and create prompts.

[0225] Step 5:

[0226] The server flexibly adjusts the notification text based on the sentiment analysis results and sends the notification to the user from the terminal. The inputs are the impact analysis results, a list of relevant people, and the user's emotional state, while the output is the adjusted notification content. The notification is customized based on the generated prompt text.

[0227] Step 6:

[0228] The server stores a history of legal changes in an information storage device and manages it so that users can search the history as needed. Inputs include changes, impact analysis results, and notification content, while output is a well-organized database of legal change history. Users can access past historical information through their terminals and extract data when necessary.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] [Second Embodiment]

[0233] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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).

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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".

[0245] This invention relates to a system that enables companies to respond quickly to changes in laws and regulations. The system automates the entire process from collecting and analyzing legal information to impact assessment, proposing countermeasures, and providing notifications, thereby streamlining the compliance process.

[0246] First, the server accesses data sources that provide legal information and periodically checks for new laws and notifications. Because this information is acquired automatically, it is more accurate and requires less effort compared to manual data collection.

[0247] Users can view changes in data collected by the server via their terminals. The server uses an analysis module to analyze these legal changes using natural language processing techniques and extract key changes. At this stage, a pre-configured evaluation model is used to assess which business processes within the company the legal changes will affect.

[0248] For example, if new environmental regulations are announced, the server analyzes them and identifies any potential impacts on the manufacturing department. Based on this information, the server generates necessary countermeasures and promptly notifies the relevant personnel. The notification includes specific action plans and priorities, allowing users to begin taking action immediately.

[0249] Furthermore, the server stores a history of legal changes in a database. This allows users to easily search and refer to past change histories, enabling them to use past countermeasures as a reference for similar situations.

[0250] Furthermore, the system integrates with the project management device and automatically registers the generated corresponding tasks. Users can access the project management device's interface via their terminals to visualize progress and modify or adjust tasks as needed. In this way, the entire process is automated, creating an environment where legal compliance is efficiently implemented as part of the company's strategic activities.

[0251] The following describes the processing flow.

[0252] Step 1:

[0253] The server periodically accesses multiple data sources that provide legal information to check for any new laws or changes that have been announced. This process is automated by scheduled jobs and runs regularly without human intervention.

[0254] Step 2:

[0255] The server analyzes newly detected legal information using a natural language processing engine. This extracts the main changes and key points of the legal documents. The analysis includes keyword extraction and grammatical structure analysis.

[0256] Step 3:

[0257] Based on the analysis results, the server evaluates which departments and business processes within a company will be affected by the legal changes. This impact assessment is performed using pre-configured evaluation models and rule-based systems.

[0258] Step 4:

[0259] The server automatically generates necessary countermeasures based on the impact assessment. These countermeasures may include specific action steps and implementation periods, enabling relevant departments to quickly begin taking action.

[0260] Step 5:

[0261] The server notifies the responsible party of the generated countermeasures and a summary of the changes. The notification is distributed via email or the company's internal messenger system.

[0262] Step 6:

[0263] The server stores all legal changes and corresponding countermeasures in a database. This allows users to search and refer to past legal change history using their terminals.

[0264] Step 7:

[0265] The terminal automatically registers the corresponding tasks generated in the project management device. This allows the user to monitor the progress of tasks through the project management tool interface and make adjustments as needed.

[0266] (Example 1)

[0267] 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."

[0268] For modern businesses, responding quickly and efficiently to changes in laws and regulations is a crucial challenge. However, collecting and analyzing legal information, assessing impacts, and developing countermeasures requires considerable time and effort, which reduces operational efficiency. Furthermore, the manual processes involved in creating, implementing, and managing countermeasures make it difficult to ensure accuracy and prompt response.

[0269] 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.

[0270] In this invention, the server includes means for automatically acquiring legal information from an information provision device and detecting changes in the law, means for analyzing the content of the legal changes using natural language processing technology and extracting important changes, and means for automatically generating countermeasures using generative AI technology and notifying the person in charge of operations. This enables a quick and accurate response to changes in the law and facilitates the smooth execution of operations.

[0271] "Legal information" refers to all information relating to laws and regulations, including the latest changes in laws and regulations that companies need to comply with them.

[0272] An "information provision device" refers to a database or online resource for obtaining legal information from external sources, and it is possible to obtain the latest legal information through this device.

[0273] "Natural language processing technology" refers to the technology that enables computers to understand, analyze, and manipulate human language, and is a technology that can analyze text and speech to extract their meaning.

[0274] "Significant changes" refer to points in legal revisions that are deemed to have a direct impact on a company's business processes.

[0275] "Generative AI technology" refers to technology that uses AI technology to generate new information and structures, and in this invention, it is used for generating countermeasures, etc.

[0276] An "information storage device" refers to a recording medium or database system used to store data and information, and is used to facilitate the storage and retrieval of information.

[0277] A "planning and management system" refers to a management system that manages the execution of projects and tasks, and makes their progress visible and adjustable.

[0278] "Evaluation criteria" refer to pre-established guidelines or standards used to assess the impact of changes in laws and regulations on a company's operations, and are used to determine appropriate responses.

[0279] This invention provides a system necessary for companies to respond quickly to changes in laws and regulations. In its specific implementation, the system is designed so that servers, terminals, and users each play their respective roles, and the compliance process is carried out efficiently.

[0280] The server first automatically retrieves legal information from the information provider. This is done using a program that periodically retrieves the latest legal information via a database or API. This information is collected by the server and then proceeds to the next analysis process; currently, programming languages ​​such as Python and request libraries are used for this process.

[0281] Next, the server analyzes the legal information obtained using natural language processing technology. For this analysis, for example, it uses spaCy, a Python natural language processing library, to analyze important keywords and sentence structures from legal documents and extract changes that are important to the company.

[0282] The analyzed information uses generative AI technology to generate specific countermeasures while verifying information related to the assignment associated with the user. A generative AI model is used to generate these countermeasures, and an action plan suitable for the enterprise's business process is created.

[0283] The user receives notifications sent from the server via the terminal. These notifications include the generated specific countermeasures and priorities, based on which the user can quickly initiate responses. For example, for a prompt sentence like "Analyze the changes in the new environmental regulations from the perspective of the manufacturing department and propose necessary countermeasures", the generative AI presents the optimal solution to the department.

[0284] Furthermore, the server stores the legal change history in the information storage device, so that the user can easily search for past data and refer to cases. This enables a quick and effective response when similar legal changes are made.

[0285] These series of processes are also coordinated with the planning management device, and the generated response tasks are automatically registered. The user can visualize the progress of the tasks on the project management interface through the terminal and improve the overall business efficiency by making adjustments as needed.

[0286] The flow of the specific process in Example 1 will be described using FIG. 11.

[0287] Step 1:

[0288] The server accesses a database or external API that provides legal information to obtain the latest laws and notices. Using the URL or API key of the information source as input, it generates the text data of the obtained laws as output. In this step, specific operations are performed to obtain data from the web using an HTTP communication library such as a request library.

[0289] Step 2:

[0290] The server analyzes the acquired text data of laws and regulations using a natural language processing library. It receives the text of the laws and regulations as input, extracts important changes through data processing, and generates a list of changes as output. Specifically, it uses NLP libraries such as spaCy to analyze the meaning and structure of the text.

[0291] Step 3:

[0292] The server evaluates the impact of the analyzed changes on business processes. Using a list of changes and data on the company's business processes as input, it generates a list of affected business processes as output. This step includes specific actions to perform simulations of business processes using a pre-configured evaluation model.

[0293] Step 4:

[0294] The server generates specific countermeasures using generative AI technology based on the evaluation results. It takes a list of affected business processes as input and generates detailed countermeasures and action plans as output. This process includes leveraging a generative AI model and using prompt statements to suggest the optimal countermeasure.

[0295] Step 5:

[0296] The server sends the generated countermeasures to the relevant personnel via email or a notification system. It receives information about the countermeasures as input and sends notifications to the appropriate personnel as output. Specifically, it uses email sending functionality and notification APIs to transmit information in real time.

[0297] Step 6:

[0298] The server stores a history of all legal changes in a database and manages it in a searchable format for the future. It receives legal changes and related data as input and records them in the database as output. In this step, a database management system such as SQL is used to implement storage and search functions.

[0299] Step 7:

[0300] The server registers the generated countermeasures in the project management system and visualizes the progress. It uses information on the countermeasures and related tasks as input and generates tasks registered in the project management system as output. Specifically, this includes the operation of automatically registering tasks through an interface to the project management tool API.

[0301] (Application Example 1)

[0302] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0303] Changes in laws and regulations can have a significant impact on a company's business activities, and there is a particular need to respond quickly to changes in security-related laws and regulations. However, manually acquiring relevant information from vast amounts of legal data, analyzing it, and evaluating its impact on business activities is a highly specialized and inefficient process. There is a need for solutions to these challenges and to streamline the legal compliance process.

[0304] 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.

[0305] In this invention, the server includes means for automatically obtaining legal information from a data source and detecting legal changes; means for analyzing the content of legal changes and extracting major change points using natural language processing; means for evaluating the impact of the analyzed change content on business activities and identifying relevant responsible persons; means for automatically generating countermeasures and notifying the responsible persons; means for storing the legal change history in a storage device and managing it in a searchable state; means for providing reference examples based on the history; and means for transmitting the generated countermeasures to an information terminal and displaying a recommended action plan. Thereby, it becomes possible to automatically evaluate the impact of legal changes on a company's security-related activities and respond quickly and efficiently.

[0306] "Legal information" refers to information regarding laws and regulations formulated by a country or region, and is data necessary for companies and organizations to comply with the law.

[0307] "Data source" refers to the source from which legal information and related data are collected, and includes online databases and official websites.

[0308] "Natural language processing" is a technology for a computer to understand, analyze, and generate human language, and is a technology used for analyzing text data, etc.

[0309] "Analysis" is a process of decomposing a large amount of data, organizing the content, and extracting useful information from it.

[0310] "Change point" refers to the change in content that occurs when a law is amended, and is recognized as the difference between the old and new laws.

[0311] "Business activities" refer to the process of producing and providing goods and services that a company conducts for the purpose of economic benefits.

[0312] "Impact evaluation" is a process of analyzing the results and impacts of legal changes on various activities and departments of a company.

[0313] The term "person in charge" refers to a person responsible for carrying out specific tasks or duties, and is a person who assumes a specific role within a company.

[0314] A "recommended action plan" is a plan that outlines a series of specific actions that are desirable to implement in response to changes in laws and regulations.

[0315] The system that realizes this invention operates primarily using a cloud server, user terminals, a database, and a natural language processing engine. The server periodically accesses data sources that provide legal information to detect whether new laws have been issued or amended. The data sources used include online databases of official organizations and public government websites.

[0316] The server analyzes legal information obtained using natural language processing technologies such as the Google Cloud Natural Language API. This analysis extracts key changes from a comparison of old and new versions of the law. Subsequently, machine learning models such as TensorFlow are used to evaluate the impact of the extracted changes on the company's business activities. Evaluation models set up for each department of the company are used for the impact assessment.

[0317] Once the relevant personnel are identified, the server automatically generates a response plan and sends it to the user's device via push notification. This notification includes a recommended action plan and priorities. The data is managed in Firebase Realtime Database and other systems, accumulating a change history and allowing users to search through past versions at any time.

[0318] For example, when a new data protection law is announced, the server immediately performs an analysis to identify the impact of the legal change on the company's security management. The generated countermeasures are notified to security personnel, who are instructed to implement protocols that comply with the new data protection regulations. In this way, a system is in place to respond quickly and efficiently to changes in legislation.

[0319] Example of a prompt:

[0320] "Please promptly analyze and report on the impact of the newly announced data protection legislation on our company's data security protocols. Please prioritize and indicate the necessary changes and countermeasures."

[0321] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0322] Step 1:

[0323] The server accesses data sources that provide legal information and periodically checks for new laws or changes. Inputs include URLs or API endpoints of the data sources, and output is the retrieved legal information data. These data sources include online databases and official websites, and information is collected using HTTP requests.

[0324] Step 2:

[0325] The server passes the acquired legal information to a natural language processing engine. The input is raw legal text data, and the output is parsed language data. The Google Cloud Natural Language API is used to extract major changes and structure their content.

[0326] Step 3:

[0327] The server inputs the analyzed changes into a machine learning model to evaluate the impact of the changes on business activities. The input is analyzed linguistic data, and the output is the impact assessment results. The impacts are classified and prioritized according to the company's existing assessment models, using tools such as TensorFlow.

[0328] Step 4:

[0329] The server automatically generates countermeasures based on the impact assessment results. The input is the impact assessment results data, and the output is a list of generated countermeasures. This includes specific action plans for each affected business process.

[0330] Step 5:

[0331] The server delivers the generated countermeasures as push notifications to the terminals of the relevant personnel. The input is a list of generated countermeasures, and the output is a recommended action plan for each notified personnel. Notifications are sent using methods such as Firebase Cloud Messaging, allowing personnel to take immediate action.

[0332] Step 6:

[0333] The server stores and manages a history of legal changes in a database in a retrievalable format. Input consists of all data after analysis and impact assessment, while output is historical data that users can search at any time. Firebase Realtime Database is used for data storage and management.

[0334] Step 7:

[0335] Users access historical data via their devices to derive efficient countermeasures for similar legal changes based on past cases. The input is the user's search query, and the output is a list of relevant past change histories and countermeasures resulting from this process. This allows users to efficiently leverage past experience.

[0336] 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.

[0337] This invention incorporates an emotion engine into an automated system for collecting legal information, assessing its impact based on changes, and developing countermeasures, thereby considering user emotions. This enables flexible communication that reflects the user's emotional state when providing notifications and information.

[0338] First, the server accesses data sources that provide legal information to automatically retrieve new legal information and detect changes. The amended laws are analyzed by a natural language processing engine to extract key changes. The server then evaluates the impact of these changes on business operations and identifies the relevant personnel. A pre-configured evaluation model is used for this impact assessment.

[0339] The emotion engine plays a crucial role in this notification process. When a user receives a notification of a change in legislation, the server uses the emotion engine to analyze the user's emotional state in real time and adjust the notification content and wording accordingly. For example, if a user is feeling stressed, the notification can be phrased in a more relaxing manner, demonstrating an emotionally sensitive approach.

[0340] As a concrete example, consider the case where a new amendment to the Labor Standards Act is made. This information is analyzed on a server, and its impact on the HR department is evaluated. When the person in charge receives this notification during a busy period, the emotion engine analyzes their emotional state and explains the amendment in calm language that does not cause them to feel burdened.

[0341] Furthermore, the emotion engine can receive user feedback and automatically improve its responses. This feedback loop allows the system to continuously adapt to user emotions and needs, aiming to provide more personalized services.

[0342] Furthermore, the server stores a history of legal changes in a database, and users can access past history via their terminals. In addition, the generated countermeasures are registered in the project management system, allowing users to manage tasks via their terminals. In this way, by incorporating an emotion engine, a more humane and flexible legal compliance system is built.

[0343] The following describes the processing flow.

[0344] Step 1:

[0345] The server connects to data sources that provide legal information and automatically retrieves new laws and changes periodically. This process may involve web scraping or APIs. When a change in legal information is detected, it is recorded.

[0346] Step 2:

[0347] The server uses a natural language processing engine to analyze the key points of legal changes on the acquired legal information. The analyzed data is then analyzed using text mining and text classification techniques to extract important changes.

[0348] Step 3:

[0349] Based on the analyzed changes, the server evaluates which business processes will be affected by the legal changes. This is done using an evaluation model that takes into account the relationship between pre-configured business processes and legal requirements.

[0350] Step 4:

[0351] The server automatically generates countermeasures based on the evaluation results. These countermeasures include specific implementation procedures and necessary modifications and improvements to affected business processes.

[0352] Step 5:

[0353] The server uses an emotion engine to analyze the user's emotional state. This is done to determine the user's current emotional state based on the user's past responses and real-time input data.

[0354] Step 6:

[0355] The server adjusts notification content to take the user's emotional state into consideration. For example, if the user is feeling stressed, the notification will be more concise and use gentler language. It will also provide supplementary information to alleviate anxiety.

[0356] Step 7:

[0357] The server sends the coordinated notifications to the designated user, utilizing various channels such as email notifications and internal messengers.

[0358] Step 8:

[0359] The server stores all legal changes and corresponding measures in a database, making them searchable and accessible to users via their terminals.

[0360] Step 9:

[0361] The terminal automatically registers the generated countermeasures with the project management system, making it easier for users to manage progress. Users can monitor tasks through the terminal and make necessary corrections.

[0362] (Example 2)

[0363] 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".

[0364] Changes in laws and regulations have a significant impact on business activities, making it crucial to collect this information in a timely manner and assess its impact on operations. However, there is no system in place that can efficiently collect and analyze information, assess its impact, and notify stakeholders. Furthermore, it is difficult to consider the emotional state of stakeholders when making notifications. Therefore, there is a need for a system that can respond flexibly and efficiently to changes in laws and regulations.

[0365] 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.

[0366] In this invention, the server includes means for automatically acquiring data from data sources that provide information and detecting changes; means for analyzing the changes in the data using natural language processing and extracting key points; and means for analyzing the emotional state of stakeholders using sentiment analysis technology and adjusting the content of notifications. This enables efficient collection and analysis of information on changes in laws and regulations, appropriate evaluation of the impact on business operations, and provision of information in a manner that takes into account the emotions of the recipients.

[0367] "Data sources that provide information" refers to data storage or network resources that provide, access to, and retrieve information related to laws and regulations.

[0368] "Means of detecting changes" refers to processes and systems for determining whether new content or updates have occurred in continuously monitored information.

[0369] "Natural language processing" refers to the technology that enables computers to understand, process, and analyze human language, and is particularly used for analyzing text data and extracting important information.

[0370] "Methods for extracting key points" refers to the process of identifying and extracting particularly important parts or parts where changes have occurred from a large amount of information.

[0371] "Emotional analysis technology" refers to techniques for analyzing and evaluating human emotions and psychological states from written and behavioral data.

[0372] "Means of adjusting notification content" refers to the process of changing the expression and method of information to suit the recipient, based on specific criteria or analysis results.

[0373] A "data storage device" refers to a hardware or software system that stores acquired data and its update history, and enables retrieval and use as needed.

[0374] A "work management system" refers to a software platform for effectively tracking and managing generated countermeasures and the progress of tasks.

[0375] An "evaluation model" refers to an analytical method or algorithm used to evaluate the impact of data and events on business operations based on specific rules or historical data.

[0376] This invention is a system that efficiently collects and analyzes information on changes in laws and regulations, conducts impact assessments related to business operations, and notifies stakeholders of the information while considering their emotional state. A specific example of this system is shown below.

[0377] The server continuously accesses data sources that provide legal information and automatically retrieves new information from specific websites and APIs. This process uses scraping tools and software for API access. Specifically, it uses Python's Beautiful Soup to retrieve data from web pages and, when necessary, downloads data directly using APIs. The retrieved data is stored in a database.

[0378] Next, the server uses a natural language processing engine to analyze the text of the acquired legal information. For example, it uses Python's NLTK or SpaCy to tokenize the legal text and extract key changes. This allows for the analysis of significant changes in the legal text.

[0379] Based on the analysis results, the server uses a pre-configured evaluation model to assess the impact on operations. This evaluation model is implemented as a rule-based algorithm or machine learning model, which identifies stakeholders. As a result of this process, a list of stakeholders who need to take responsibility is generated.

[0380] When sending notifications, the server uses sentiment analysis technology to analyze the emotional state of those involved. Related tools include Google's Perspective API and proprietary models. This analysis adjusts the notification content to match the recipient's emotional state. As a result, notifications received by users are expressed in a gentler and less burdensome manner.

[0381] For example, a user can input a prompt such as, "Please explain the changes in the new Labor Standards Act in a relaxed tone," into an AI model to generate a tailored notification.

[0382] In this way, the server can efficiently manage a series of information related to changes in laws and regulations, and appropriately notify users via their terminals. As a result, users can receive information in a way that is easy to understand and less stressful for them.

[0383] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0384] Step 1:

[0385] The server accesses data sources that provide information and retrieves new information. The input data is the URL or API endpoint of the data source, and the output is the raw data of the retrieved legal information. Specifically, the server uses a Python script to scrape web pages using the Beautiful Soup and Requests libraries to collect the necessary information.

[0386] Step 2:

[0387] The server detects changes from the acquired legal information. The input is the raw data acquired in step 1, and the output is the text of the changed parts. The server compares the new data with the information stored in the past database and runs a comparison algorithm to find the differences.

[0388] Step 3:

[0389] The server analyzes the information where changes have been detected using a natural language processing engine and extracts the main points of change. The input is the modified text detected in step 2, and the output is the extracted main points of change. In this step, the server tokenizes the text using Python's NLTK or SpaCy library and extracts important keywords and phrases.

[0390] Step 4:

[0391] The server evaluates the impact on business operations based on the extracted change points. The input is the major change points extracted in step 3, and the output is a list of affected business departments and stakeholders. The server uses a pre-configured rule-based evaluation model to determine the degree of impact of the given information on business operations and lists the relevant departments and individuals.

[0392] Step 5:

[0393] The server analyzes the user's emotional state before delivering a tailored notification. Inputs are past user behavior data and feedback, while output is an assessment of the user's current emotional state. The server utilizes Google's Perspective API and its own models to analyze the user's emotional tone and adjust the tone of the notification accordingly.

[0394] Step 6:

[0395] The server generates a coordinated notification and sends it to the user via the terminal. The input is the notification content coordinated in step 5, and the output is the final notification message the user receives. In this step, the server automatically delivers the notification using a mail server or messaging protocol.

[0396] Step 7:

[0397] Users receive notifications through their devices and refer to the legal change history as needed. Inputs are all notification and change history data, while outputs are the historical information the user refers to. Users can search the data and review past history using the application on their device or the web interface.

[0398] Step 8:

[0399] Users provide feedback, and the server continuously improves the system using a generative AI model. Input is user feedback data, and output is notifications and system behavior that reflect the improvements. The server incorporates the new feedback into its training data, retrains the AI ​​model, and enhances the user experience.

[0400] (Application Example 2)

[0401] 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".

[0402] Changes in laws and regulations often have a significant impact on business operations, but the process of acquiring this information, assessing its impact, and notifying relevant personnel may fail to consider the emotional state of users, potentially leading to stress and confusion. Therefore, there is a need for a system that takes users' feelings into consideration and provides notifications through appropriate communication.

[0403] 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.

[0404] In this invention, the server includes means for automatically acquiring legal information from information sources and detecting changes in laws and regulations; means for analyzing the content of legal changes using natural language processing and extracting key changes; means for evaluating the impact of the analyzed changes on business operations and identifying relevant individuals; notification means combining a sentiment analysis engine that analyzes the user's emotional state and flexibly adjusts notification content; and means for storing the history of legal changes in an information storage device and managing it in a searchable state. This makes it possible to appropriately provide information on legal changes while taking into consideration the user's emotions.

[0405] "Legal information" refers to official information relating to laws, rules, or regulations that may affect business or personal activities.

[0406] "Information source" refers to the original data provider or database used to obtain legal information.

[0407] "Natural language processing" is a technology that enables computers to understand and analyze human language, and makes it possible to automatically analyze text data.

[0408] "Assessing the impact" is the process of quantitatively or qualitatively determining how changes in laws and regulations will affect business operations.

[0409] "Relevant persons" refers to individuals whose duties or responsibilities may be directly affected by changes in laws and regulations.

[0410] An "emotion analysis engine" is a technology that analyzes a user's emotional state in real time and adjusts communication based on that information.

[0411] "Notification means" refers to a method or medium for communicating changed legal information to users.

[0412] An "information storage device" is a data storage facility that stores acquired information and makes it accessible or retrievable as needed.

[0413] The system of this invention provides users with information that is sensitive to their emotions through the acquisition, analysis, evaluation, notification, and history management of legal information.

[0414] The server automatically retrieves legal information from its sources and detects changes. The retrieved information is analyzed using a natural language processing engine to extract key changes. This analysis utilizes tools such as Python and TensorFlow, processing the information as text data. For impact assessment, a pre-configured evaluation model is used to determine how new legal changes will affect operations. The evaluation results are used to identify relevant individuals.

[0415] The device features an emotion analysis engine that analyzes the user's emotional state in real time. This engine uses generative AI models, such as OpenAI's GPT model, to analyze the user's current emotional state. Notification content is adjusted based on this emotion analysis, enabling flexible communication. For example, if a notification about a change in legislation is likely to cause stress, the content of the notification will be changed to more relaxing language.

[0416] Furthermore, the server comprehensively stores a history of legal changes in its information storage device and manages it so that users can search past history as needed. This function allows users to easily access past change history related to their work.

[0417] For example, if a user receives a notification about a new tax law revision, the sentiment analysis engine will detect the user's anxiety and send a notification using phrasing such as, "The new tax law revision will change your tax rate, but please rest assured that we will support you with the detailed procedures." An example of a prompt message would be, "We have detected that the user is anxious about the tax law change. Please suggest ways to soften the notification. The change is the application of a new tax rate."

[0418] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0419] Step 1:

[0420] The server automatically retrieves legal information from its sources. At this stage, it uses databases and APIs to collect the latest legal information. The input is legal information data, and the output is the retrieved raw legal data. The server accesses the data periodically or triggered by events, sending requests to receive data.

[0421] Step 2:

[0422] The server analyzes legal information obtained using a natural language processing engine and extracts key changes. The input is the raw legal data obtained, and the output is a list of the analyzed key changes. For text analysis of the data, Python libraries such as NLTK and SpaCy are used to identify and summarize the changes.

[0423] Step 3:

[0424] The server evaluates the impact of the analyzed changes on business operations and identifies the individuals involved. The input is a list of key changes, and the output is the impact analysis results and a list of the individuals involved. A pre-configured data model is used for the evaluation to quantitatively analyze how the changes affect business processes.

[0425] Step 4:

[0426] The device uses an emotion analysis engine to analyze the user's current emotional state. The input is data related to the user's emotions (e.g., past response history and current task content), and the output is the current emotional state. The engine uses a generative AI model to analyze the user's emotions and create prompts.

[0427] Step 5:

[0428] The server flexibly adjusts the notification text based on the sentiment analysis results and sends the notification to the user from the terminal. The inputs are the impact analysis results, a list of relevant people, and the user's emotional state, while the output is the adjusted notification content. The notification is customized based on the generated prompt text.

[0429] Step 6:

[0430] The server stores a history of legal changes in an information storage device and manages it so that users can search the history as needed. Inputs include changes, impact analysis results, and notification content, while output is a well-organized database of legal change history. Users can access past historical information through their terminals and extract data when necessary.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] [Third Embodiment]

[0435] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0436] 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.

[0437] 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).

[0438] 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.

[0439] 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.

[0440] 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).

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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".

[0447] This invention relates to a system that enables companies to respond quickly to changes in laws and regulations. The system automates the entire process from collecting and analyzing legal information to impact assessment, proposing countermeasures, and providing notifications, thereby streamlining the compliance process.

[0448] First, the server accesses data sources that provide legal information and periodically checks for new laws and notifications. Because this information is acquired automatically, it is more accurate and requires less effort compared to manual data collection.

[0449] Users can view changes in data collected by the server via their terminals. The server uses an analysis module to analyze these legal changes using natural language processing techniques and extract key changes. At this stage, a pre-configured evaluation model is used to assess which business processes within the company the legal changes will affect.

[0450] For example, if new environmental regulations are announced, the server analyzes them and identifies any potential impacts on the manufacturing department. Based on this information, the server generates necessary countermeasures and promptly notifies the relevant personnel. The notification includes specific action plans and priorities, allowing users to begin taking action immediately.

[0451] Furthermore, the server stores a history of legal changes in a database. This allows users to easily search and refer to past change histories, enabling them to use past countermeasures as a reference for similar situations.

[0452] Furthermore, the system integrates with the project management device and automatically registers the generated corresponding tasks. Users can access the project management device's interface via their terminals to visualize progress and modify or adjust tasks as needed. In this way, the entire process is automated, creating an environment where legal compliance is efficiently implemented as part of the company's strategic activities.

[0453] The following describes the processing flow.

[0454] Step 1:

[0455] The server periodically accesses multiple data sources that provide legal information to check for any new laws or changes that have been announced. This process is automated by scheduled jobs and runs regularly without human intervention.

[0456] Step 2:

[0457] The server analyzes newly detected legal information using a natural language processing engine. This extracts the main changes and key points of the legal documents. The analysis includes keyword extraction and grammatical structure analysis.

[0458] Step 3:

[0459] Based on the analysis results, the server evaluates which departments and business processes within a company will be affected by the legal changes. This impact assessment is performed using pre-configured evaluation models and rule-based systems.

[0460] Step 4:

[0461] The server automatically generates necessary countermeasures based on the impact assessment. These countermeasures may include specific action steps and implementation periods, enabling relevant departments to quickly begin taking action.

[0462] Step 5:

[0463] The server notifies the responsible party of the generated countermeasures and a summary of the changes. The notification is distributed via email or the company's internal messenger system.

[0464] Step 6:

[0465] The server stores all legal changes and corresponding countermeasures in a database. This allows users to search and refer to past legal change history using their terminals.

[0466] Step 7:

[0467] The terminal automatically registers the corresponding tasks generated in the project management device. This allows the user to monitor the progress of tasks through the project management tool interface and make adjustments as needed.

[0468] (Example 1)

[0469] 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."

[0470] For modern businesses, responding quickly and efficiently to changes in laws and regulations is a crucial challenge. However, collecting and analyzing legal information, assessing impacts, and developing countermeasures requires considerable time and effort, which reduces operational efficiency. Furthermore, the manual processes involved in creating, implementing, and managing countermeasures make it difficult to ensure accuracy and prompt response.

[0471] 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.

[0472] In this invention, the server includes means for automatically acquiring legal information from an information provision device and detecting changes in the law, means for analyzing the content of the legal changes using natural language processing technology and extracting important changes, and means for automatically generating countermeasures using generative AI technology and notifying the person in charge of operations. This enables a quick and accurate response to changes in the law and facilitates the smooth execution of operations.

[0473] "Legal information" refers to all information relating to laws and regulations, including the latest changes in laws and regulations that companies need to comply with them.

[0474] An "information provision device" refers to a database or online resource for obtaining legal information from external sources, and it is possible to obtain the latest legal information through this device.

[0475] "Natural language processing technology" refers to the technology that enables computers to understand, analyze, and manipulate human language, and is a technology that can analyze text and speech to extract their meaning.

[0476] "Significant changes" refer to points in legal revisions that are deemed to have a direct impact on a company's business processes.

[0477] "Generative AI technology" refers to technology that uses AI technology to generate new information and structures, and in this invention, it is used for generating countermeasures, etc.

[0478] An "information storage device" refers to a recording medium or database system used to store data and information, and is used to facilitate the storage and retrieval of information.

[0479] A "planning and management system" refers to a management system that manages the execution of projects and tasks, and makes their progress visible and adjustable.

[0480] "Evaluation criteria" refer to pre-established guidelines or standards used to assess the impact of changes in laws and regulations on a company's operations, and are used to determine appropriate responses.

[0481] This invention provides a system necessary for companies to respond quickly to changes in laws and regulations. In its specific implementation, the system is designed so that servers, terminals, and users each play their respective roles, and the compliance process is carried out efficiently.

[0482] The server first automatically retrieves legal information from the information provider. This is done using a program that periodically retrieves the latest legal information via a database or API. This information is collected by the server and then proceeds to the next analysis process; currently, programming languages ​​such as Python and request libraries are used for this process.

[0483] Next, the server analyzes the legal information obtained using natural language processing technology. For this analysis, for example, it uses spaCy, a Python natural language processing library, to analyze important keywords and sentence structures from legal documents and extract changes that are important to the company.

[0484] The analyzed information is then used with generative AI technology to match user-related assignment information and generate specific countermeasures. This countermeasure generation utilizes a generative AI model to create action plans tailored to the company's business processes.

[0485] Users receive notifications sent from the server via their devices. These notifications include specific actions and priorities that have been generated, allowing users to quickly take action based on them. For example, a prompt such as, "Analyze the changes in new environmental regulations from a manufacturing department's perspective and propose necessary actions," will prompt the generating AI to present the optimal solution to the department.

[0486] Furthermore, since the server stores a history of legal changes in an information storage device, users can easily search past data and refer to examples. This enables a swift and effective response when similar legal changes occur.

[0487] These processes are integrated with the project management system, and the generated corresponding tasks are automatically registered. Users can visualize task progress on the project management interface via their terminals and make adjustments as needed to improve overall work efficiency.

[0488] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0489] Step 1:

[0490] The server accesses databases and external APIs that provide legal information to retrieve the latest laws and regulations. It uses the URL or API key of the information source as input and generates text data of the retrieved laws as output. This step uses HTTP communication libraries, such as request libraries, to perform the specific actions of retrieving data from the web.

[0491] Step 2:

[0492] The server analyzes the acquired text data of laws and regulations using a natural language processing library. It receives the text of the laws and regulations as input, extracts important changes through data processing, and generates a list of changes as output. Specifically, it uses NLP libraries such as spaCy to analyze the meaning and structure of the text.

[0493] Step 3:

[0494] The server evaluates the impact of the analyzed changes on business processes. Using a list of changes and data on the company's business processes as input, it generates a list of affected business processes as output. This step includes specific actions to perform simulations of business processes using a pre-configured evaluation model.

[0495] Step 4:

[0496] The server generates specific countermeasures using generative AI technology based on the evaluation results. It takes a list of affected business processes as input and generates detailed countermeasures and action plans as output. This process includes leveraging a generative AI model and using prompt statements to suggest the optimal countermeasure.

[0497] Step 5:

[0498] The server sends the generated countermeasures to the relevant personnel via email or a notification system. It receives information about the countermeasures as input and sends notifications to the appropriate personnel as output. Specifically, it uses email sending functionality and notification APIs to transmit information in real time.

[0499] Step 6:

[0500] The server stores a history of all legal changes in a database and manages it in a searchable format for the future. It receives legal changes and related data as input and records them in the database as output. In this step, a database management system such as SQL is used to implement storage and search functions.

[0501] Step 7:

[0502] The server registers the generated countermeasures in the project management system and visualizes the progress. It uses information on the countermeasures and related tasks as input and generates tasks registered in the project management system as output. Specifically, this includes the operation of automatically registering tasks through an interface to the project management tool API.

[0503] (Application Example 1)

[0504] 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."

[0505] Changes in laws and regulations can have a significant impact on a company's business activities, and there is a particular need to respond quickly to changes in security-related laws and regulations. However, manually acquiring relevant information from vast amounts of legal data, analyzing it, and evaluating its impact on business activities is a highly specialized and inefficient process. There is a need for solutions to these challenges and to streamline the legal compliance process.

[0506] 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.

[0507] In this invention, the server includes means for automatically acquiring legal information from a data source and detecting changes in the law; means for analyzing the changes in the law using natural language processing and extracting key changes; means for evaluating the impact of the analyzed changes on business activities and identifying the relevant personnel; means for automatically generating countermeasures and notifying the personnel; means for storing the history of changes in the law in a storage device and managing it in a searchable state; means for providing reference cases based on the history; and means for transmitting the generated countermeasures to an information terminal and displaying a recommended action plan. This makes it possible to automatically evaluate the impact of changes in the law on a company's security-related activities and to respond quickly and efficiently.

[0508] "Legal information" refers to information about laws and regulations enacted by countries and regions, and is data that companies and organizations need for legal compliance.

[0509] "Data source" refers to the source from which legal information and related data are collected, and includes online databases and official websites.

[0510] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language, and is used in applications such as analyzing text data.

[0511] "Analysis" is the process of breaking down large amounts of data, organizing its contents, and extracting useful information from it.

[0512] "Changes" refer to the alterations in content that occur when laws are amended, and are recognized as differences between the old and new laws.

[0513] "Business activities" refer to the process of producing and providing goods and services that a company undertakes for the purpose of economic gain.

[0514] "Impact assessment" is the process of analyzing the results and impacts that changes in laws and regulations have on various activities and departments within a company.

[0515] The term "person in charge" refers to a person responsible for carrying out specific tasks or duties, and is a person who assumes a specific role within a company.

[0516] A "recommended action plan" is a plan that outlines a series of specific actions that are desirable to implement in response to changes in laws and regulations.

[0517] The system that realizes this invention operates primarily using a cloud server, user terminals, a database, and a natural language processing engine. The server periodically accesses data sources that provide legal information to detect whether new laws have been issued or amended. The data sources used include online databases of official organizations and public government websites.

[0518] The server analyzes legal information obtained using natural language processing technologies such as the Google Cloud Natural Language API. This analysis extracts key changes from a comparison of old and new versions of the law. Subsequently, machine learning models such as TensorFlow are used to evaluate the impact of the extracted changes on the company's business activities. Evaluation models set up for each department of the company are used for the impact assessment.

[0519] Once the relevant personnel are identified, the server automatically generates a response plan and sends it to the user's device via push notification. This notification includes a recommended action plan and priorities. The data is managed in Firebase Realtime Database and other systems, accumulating a change history and allowing users to search through past versions at any time.

[0520] For example, when a new data protection law is announced, the server immediately performs an analysis to identify the impact of the legal change on the company's security management. The generated countermeasures are notified to security personnel, who are instructed to implement protocols that comply with the new data protection regulations. In this way, a system is in place to respond quickly and efficiently to changes in legislation.

[0521] Example of a prompt:

[0522] "Please promptly analyze and report on the impact of the newly announced data protection legislation on our company's data security protocols. Please prioritize and indicate the necessary changes and countermeasures."

[0523] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0524] Step 1:

[0525] The server accesses data sources that provide legal information and periodically checks for new laws or changes. Inputs include URLs or API endpoints of the data sources, and output is the retrieved legal information data. These data sources include online databases and official websites, and information is collected using HTTP requests.

[0526] Step 2:

[0527] The server passes the acquired legal information to a natural language processing engine. The input is raw legal text data, and the output is parsed language data. The Google Cloud Natural Language API is used to extract major changes and structure their content.

[0528] Step 3:

[0529] The server inputs the analyzed changes into a machine learning model to evaluate the impact of the changes on business activities. The input is analyzed linguistic data, and the output is the impact assessment results. The impacts are classified and prioritized according to the company's existing assessment models, using tools such as TensorFlow.

[0530] Step 4:

[0531] The server automatically generates countermeasures based on the impact assessment results. The input is the impact assessment results data, and the output is a list of generated countermeasures. This includes specific action plans for each affected business process.

[0532] Step 5:

[0533] The server delivers the generated countermeasures as push notifications to the terminals of the relevant personnel. The input is a list of generated countermeasures, and the output is a recommended action plan for each notified personnel. Notifications are sent using methods such as Firebase Cloud Messaging, allowing personnel to take immediate action.

[0534] Step 6:

[0535] The server stores and manages a history of legal changes in a database in a retrievalable format. Input consists of all data after analysis and impact assessment, while output is historical data that users can search at any time. Firebase Realtime Database is used for data storage and management.

[0536] Step 7:

[0537] Users access historical data via their devices to derive efficient countermeasures for similar legal changes based on past cases. The input is the user's search query, and the output is a list of relevant past change histories and countermeasures resulting from this process. This allows users to efficiently leverage past experience.

[0538] 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.

[0539] This invention incorporates an emotion engine into an automated system for collecting legal information, assessing its impact based on changes, and developing countermeasures, thereby considering user emotions. This enables flexible communication that reflects the user's emotional state when providing notifications and information.

[0540] First, the server accesses data sources that provide legal information to automatically retrieve new legal information and detect changes. The amended laws are analyzed by a natural language processing engine to extract key changes. The server then evaluates the impact of these changes on business operations and identifies the relevant personnel. A pre-configured evaluation model is used for this impact assessment.

[0541] The emotion engine plays a crucial role in this notification process. When a user receives a notification of a change in legislation, the server uses the emotion engine to analyze the user's emotional state in real time and adjust the notification content and wording accordingly. For example, if a user is feeling stressed, the notification can be phrased in a more relaxing manner, demonstrating an emotionally sensitive approach.

[0542] As a concrete example, consider the case where a new amendment to the Labor Standards Act is made. This information is analyzed on a server, and its impact on the HR department is evaluated. When the person in charge receives this notification during a busy period, the emotion engine analyzes their emotional state and explains the amendment in calm language that does not cause them to feel burdened.

[0543] Furthermore, the emotion engine can receive user feedback and automatically improve its responses. This feedback loop allows the system to continuously adapt to user emotions and needs, aiming to provide more personalized services.

[0544] Furthermore, the server stores a history of legal changes in a database, and users can access past history via their terminals. In addition, the generated countermeasures are registered in the project management system, allowing users to manage tasks via their terminals. In this way, by incorporating an emotion engine, a more humane and flexible legal compliance system is built.

[0545] The following describes the processing flow.

[0546] Step 1:

[0547] The server connects to data sources that provide legal information and automatically retrieves new laws and changes periodically. This process may involve web scraping or APIs. When a change in legal information is detected, it is recorded.

[0548] Step 2:

[0549] The server uses a natural language processing engine to analyze the key points of legal changes on the acquired legal information. The analyzed data is then analyzed using text mining and text classification techniques to extract important changes.

[0550] Step 3:

[0551] Based on the analyzed changes, the server evaluates which business processes will be affected by the legal changes. This is done using an evaluation model that takes into account the relationship between pre-configured business processes and legal requirements.

[0552] Step 4:

[0553] The server automatically generates countermeasures based on the evaluation results. These countermeasures include specific implementation procedures and necessary modifications and improvements to affected business processes.

[0554] Step 5:

[0555] The server uses an emotion engine to analyze the user's emotional state. This is done to determine the user's current emotional state based on the user's past responses and real-time input data.

[0556] Step 6:

[0557] The server adjusts notification content to take the user's emotional state into consideration. For example, if the user is feeling stressed, the notification will be more concise and use gentler language. It will also provide supplementary information to alleviate anxiety.

[0558] Step 7:

[0559] The server sends the coordinated notifications to the designated user, utilizing various channels such as email notifications and internal messengers.

[0560] Step 8:

[0561] The server stores all legal changes and corresponding measures in a database, making them searchable and accessible to users via their terminals.

[0562] Step 9:

[0563] The terminal automatically registers the generated countermeasures with the project management system, making it easier for users to manage progress. Users can monitor tasks through the terminal and make necessary corrections.

[0564] (Example 2)

[0565] 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."

[0566] Changes in laws and regulations have a significant impact on business activities, making it crucial to collect this information in a timely manner and assess its impact on operations. However, there is no system in place that can efficiently collect and analyze information, assess its impact, and notify stakeholders. Furthermore, it is difficult to consider the emotional state of stakeholders when making notifications. Therefore, there is a need for a system that can respond flexibly and efficiently to changes in laws and regulations.

[0567] 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.

[0568] In this invention, the server includes means for automatically acquiring data from data sources that provide information and detecting changes; means for analyzing the changes in the data using natural language processing and extracting key points; and means for analyzing the emotional state of stakeholders using sentiment analysis technology and adjusting the content of notifications. This enables efficient collection and analysis of information on changes in laws and regulations, appropriate evaluation of the impact on business operations, and provision of information in a manner that takes into account the emotions of the recipients.

[0569] "Data sources that provide information" refers to data storage or network resources that provide, access to, and retrieve information related to laws and regulations.

[0570] "Means of detecting changes" refers to processes and systems for determining whether new content or updates have occurred in continuously monitored information.

[0571] "Natural language processing" refers to the technology that enables computers to understand, process, and analyze human language, and is particularly used for analyzing text data and extracting important information.

[0572] "Methods for extracting key points" refers to the process of identifying and extracting particularly important parts or parts where changes have occurred from a large amount of information.

[0573] "Emotional analysis technology" refers to techniques for analyzing and evaluating human emotions and psychological states from written and behavioral data.

[0574] "Means of adjusting notification content" refers to the process of changing the expression and method of information to suit the recipient, based on specific criteria or analysis results.

[0575] A "data storage device" refers to a hardware or software system that stores acquired data and its update history, and enables retrieval and use as needed.

[0576] A "work management system" refers to a software platform for effectively tracking and managing generated countermeasures and the progress of tasks.

[0577] An "evaluation model" refers to an analytical method or algorithm used to evaluate the impact of data and events on business operations based on specific rules or historical data.

[0578] This invention is a system that efficiently collects and analyzes information on changes in laws and regulations, conducts impact assessments related to business operations, and notifies stakeholders of the information while considering their emotional state. A specific example of this system is shown below.

[0579] The server continuously accesses data sources that provide legal information and automatically retrieves new information from specific websites and APIs. This process uses scraping tools and software for API access. Specifically, it uses Python's Beautiful Soup to retrieve data from web pages and, when necessary, downloads data directly using APIs. The retrieved data is stored in a database.

[0580] Next, the server uses a natural language processing engine to analyze the text of the acquired legal information. For example, it uses Python's NLTK or SpaCy to tokenize the legal text and extract key changes. This allows for the analysis of significant changes in the legal text.

[0581] Based on the analysis results, the server uses a pre-configured evaluation model to assess the impact on operations. This evaluation model is implemented as a rule-based algorithm or machine learning model, which identifies stakeholders. As a result of this process, a list of stakeholders who need to take responsibility is generated.

[0582] When sending notifications, the server uses sentiment analysis technology to analyze the emotional state of those involved. Related tools include Google's Perspective API and proprietary models. This analysis adjusts the notification content to match the recipient's emotional state. As a result, notifications received by users are expressed in a gentler and less burdensome manner.

[0583] For example, a user can input a prompt such as, "Please explain the changes in the new Labor Standards Act in a relaxed tone," into an AI model to generate a tailored notification.

[0584] In this way, the server can efficiently manage a series of information related to changes in laws and regulations, and appropriately notify users via their terminals. As a result, users can receive information in a way that is easy to understand and less stressful for them.

[0585] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0586] Step 1:

[0587] The server accesses data sources that provide information and retrieves new information. The input data is the URL or API endpoint of the data source, and the output is the raw data of the retrieved legal information. Specifically, the server uses a Python script to scrape web pages using the Beautiful Soup and Requests libraries to collect the necessary information.

[0588] Step 2:

[0589] The server detects changes from the acquired legal information. The input is the raw data acquired in step 1, and the output is the text of the changed parts. The server compares the new data with the information stored in the past database and runs a comparison algorithm to find the differences.

[0590] Step 3:

[0591] The server analyzes the information where changes have been detected using a natural language processing engine and extracts the main points of change. The input is the modified text detected in step 2, and the output is the extracted main points of change. In this step, the server tokenizes the text using Python's NLTK or SpaCy library and extracts important keywords and phrases.

[0592] Step 4:

[0593] The server evaluates the impact on business operations based on the extracted change points. The input is the major change points extracted in step 3, and the output is a list of affected business departments and stakeholders. The server uses a pre-configured rule-based evaluation model to determine the degree of impact of the given information on business operations and lists the relevant departments and individuals.

[0594] Step 5:

[0595] The server analyzes the user's emotional state before delivering a tailored notification. Inputs are past user behavior data and feedback, while output is an assessment of the user's current emotional state. The server utilizes Google's Perspective API and its own models to analyze the user's emotional tone and adjust the tone of the notification accordingly.

[0596] Step 6:

[0597] The server generates a coordinated notification and sends it to the user via the terminal. The input is the notification content coordinated in step 5, and the output is the final notification message the user receives. In this step, the server automatically delivers the notification using a mail server or messaging protocol.

[0598] Step 7:

[0599] Users receive notifications through their devices and refer to the legal change history as needed. Inputs are all notification and change history data, while outputs are the historical information the user refers to. Users can search the data and review past history using the application on their device or the web interface.

[0600] Step 8:

[0601] Users provide feedback, and the server continuously improves the system using a generative AI model. Input is user feedback data, and output is notifications and system behavior that reflect the improvements. The server incorporates the new feedback into its training data, retrains the AI ​​model, and enhances the user experience.

[0602] (Application Example 2)

[0603] 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."

[0604] Changes in laws and regulations often have a significant impact on business operations, but the process of acquiring this information, assessing its impact, and notifying relevant personnel may fail to consider the emotional state of users, potentially leading to stress and confusion. Therefore, there is a need for a system that takes users' feelings into consideration and provides notifications through appropriate communication.

[0605] 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.

[0606] In this invention, the server includes means for automatically acquiring legal information from information sources and detecting changes in laws and regulations; means for analyzing the content of legal changes using natural language processing and extracting key changes; means for evaluating the impact of the analyzed changes on business operations and identifying relevant individuals; notification means combining a sentiment analysis engine that analyzes the user's emotional state and flexibly adjusts notification content; and means for storing the history of legal changes in an information storage device and managing it in a searchable state. This makes it possible to appropriately provide information on legal changes while taking into consideration the user's emotions.

[0607] "Legal information" refers to official information relating to laws, rules, or regulations that may affect business or personal activities.

[0608] "Information source" refers to the original data provider or database used to obtain legal information.

[0609] "Natural language processing" is a technology that enables computers to understand and analyze human language, and makes it possible to automatically analyze text data.

[0610] "Assessing the impact" is the process of quantitatively or qualitatively determining how changes in laws and regulations will affect business operations.

[0611] "Relevant persons" refers to individuals whose duties or responsibilities may be directly affected by changes in laws and regulations.

[0612] An "emotion analysis engine" is a technology that analyzes a user's emotional state in real time and adjusts communication based on that information.

[0613] "Notification means" refers to a method or medium for communicating changed legal information to users.

[0614] An "information storage device" is a data storage facility that stores acquired information and makes it accessible or retrievable as needed.

[0615] The system of this invention provides users with information that is sensitive to their emotions through the acquisition, analysis, evaluation, notification, and history management of legal information.

[0616] The server automatically retrieves legal information from its sources and detects changes. The retrieved information is analyzed using a natural language processing engine to extract key changes. This analysis utilizes tools such as Python and TensorFlow, processing the information as text data. For impact assessment, a pre-configured evaluation model is used to determine how new legal changes will affect operations. The evaluation results are used to identify relevant individuals.

[0617] The device features an emotion analysis engine that analyzes the user's emotional state in real time. This engine uses generative AI models, such as OpenAI's GPT model, to analyze the user's current emotional state. Notification content is adjusted based on this emotion analysis, enabling flexible communication. For example, if a notification about a change in legislation is likely to cause stress, the content of the notification will be changed to more relaxing language.

[0618] Furthermore, the server comprehensively stores a history of legal changes in its information storage device and manages it so that users can search past history as needed. This function allows users to easily access past change history related to their work.

[0619] For example, if a user receives a notification about a new tax law revision, the sentiment analysis engine will detect the user's anxiety and send a notification using phrasing such as, "The new tax law revision will change your tax rate, but please rest assured that we will support you with the detailed procedures." An example of a prompt message would be, "We have detected that the user is anxious about the tax law change. Please suggest ways to soften the notification. The change is the application of a new tax rate."

[0620] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0621] Step 1:

[0622] The server automatically retrieves legal information from its sources. At this stage, it uses databases and APIs to collect the latest legal information. The input is legal information data, and the output is the retrieved raw legal data. The server accesses the data periodically or triggered by events, sending requests to receive data.

[0623] Step 2:

[0624] The server analyzes legal information obtained using a natural language processing engine and extracts key changes. The input is the raw legal data obtained, and the output is a list of the analyzed key changes. For text analysis of the data, Python libraries such as NLTK and SpaCy are used to identify and summarize the changes.

[0625] Step 3:

[0626] The server evaluates the impact of the analyzed changes on business operations and identifies the individuals involved. The input is a list of key changes, and the output is the impact analysis results and a list of the individuals involved. A pre-configured data model is used for the evaluation to quantitatively analyze how the changes affect business processes.

[0627] Step 4:

[0628] The device uses an emotion analysis engine to analyze the user's current emotional state. The input is data related to the user's emotions (e.g., past response history and current task content), and the output is the current emotional state. The engine uses a generative AI model to analyze the user's emotions and create prompts.

[0629] Step 5:

[0630] The server flexibly adjusts the notification text based on the sentiment analysis results and sends the notification to the user from the terminal. The inputs are the impact analysis results, a list of relevant people, and the user's emotional state, while the output is the adjusted notification content. The notification is customized based on the generated prompt text.

[0631] Step 6:

[0632] The server stores a history of legal changes in an information storage device and manages it so that users can search the history as needed. Inputs include changes, impact analysis results, and notification content, while output is a well-organized database of legal change history. Users can access past historical information through their terminals and extract data when necessary.

[0633] 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.

[0634] 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.

[0635] 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.

[0636] [Fourth Embodiment]

[0637] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0638] 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.

[0639] 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).

[0640] 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.

[0641] 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.

[0642] 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).

[0643] 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.

[0644] 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.

[0645] 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.

[0646] 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.

[0647] 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.

[0648] 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.

[0649] 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".

[0650] This invention relates to a system that enables companies to respond quickly to changes in laws and regulations. The system automates the entire process from collecting and analyzing legal information to impact assessment, proposing countermeasures, and providing notifications, thereby streamlining the compliance process.

[0651] First, the server accesses data sources that provide legal information and periodically checks for new laws and notifications. Because this information is acquired automatically, it is more accurate and requires less effort compared to manual data collection.

[0652] Users can view changes in data collected by the server via their terminals. The server uses an analysis module to analyze these legal changes using natural language processing techniques and extract key changes. At this stage, a pre-configured evaluation model is used to assess which business processes within the company the legal changes will affect.

[0653] For example, if new environmental regulations are announced, the server analyzes them and identifies any potential impacts on the manufacturing department. Based on this information, the server generates necessary countermeasures and promptly notifies the relevant personnel. The notification includes specific action plans and priorities, allowing users to begin taking action immediately.

[0654] Furthermore, the server stores a history of legal changes in a database. This allows users to easily search and refer to past change histories, enabling them to use past countermeasures as a reference for similar situations.

[0655] Furthermore, the system integrates with the project management device and automatically registers the generated corresponding tasks. Users can access the project management device's interface via their terminals to visualize progress and modify or adjust tasks as needed. In this way, the entire process is automated, creating an environment where legal compliance is efficiently implemented as part of the company's strategic activities.

[0656] The following describes the processing flow.

[0657] Step 1:

[0658] The server periodically accesses multiple data sources that provide legal information to check for any new laws or changes that have been announced. This process is automated by scheduled jobs and runs regularly without human intervention.

[0659] Step 2:

[0660] The server analyzes newly detected legal information using a natural language processing engine. This extracts the main changes and key points of the legal documents. The analysis includes keyword extraction and grammatical structure analysis.

[0661] Step 3:

[0662] Based on the analysis results, the server evaluates which departments and business processes within a company will be affected by the legal changes. This impact assessment is performed using pre-configured evaluation models and rule-based systems.

[0663] Step 4:

[0664] The server automatically generates necessary countermeasures based on the impact assessment. These countermeasures may include specific action steps and implementation periods, enabling relevant departments to quickly begin taking action.

[0665] Step 5:

[0666] The server notifies the responsible party of the generated countermeasures and a summary of the changes. The notification is distributed via email or the company's internal messenger system.

[0667] Step 6:

[0668] The server stores all legal changes and corresponding countermeasures in a database. This allows users to search and refer to past legal change history using their terminals.

[0669] Step 7:

[0670] The terminal automatically registers the corresponding tasks generated in the project management device. This allows the user to monitor the progress of tasks through the project management tool interface and make adjustments as needed.

[0671] (Example 1)

[0672] 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".

[0673] For modern businesses, responding quickly and efficiently to changes in laws and regulations is a crucial challenge. However, collecting and analyzing legal information, assessing impacts, and developing countermeasures requires considerable time and effort, which reduces operational efficiency. Furthermore, the manual processes involved in creating, implementing, and managing countermeasures make it difficult to ensure accuracy and prompt response.

[0674] 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.

[0675] In this invention, the server includes means for automatically acquiring legal information from an information provision device and detecting changes in the law, means for analyzing the content of the legal changes using natural language processing technology and extracting important changes, and means for automatically generating countermeasures using generative AI technology and notifying the person in charge of operations. This enables a quick and accurate response to changes in the law and facilitates the smooth execution of operations.

[0676] "Legal information" refers to all information relating to laws and regulations, including the latest changes in laws and regulations that companies need to comply with them.

[0677] An "information provision device" refers to a database or online resource for obtaining legal information from external sources, and it is possible to obtain the latest legal information through this device.

[0678] "Natural language processing technology" refers to the technology that enables computers to understand, analyze, and manipulate human language, and is a technology that can analyze text and speech to extract their meaning.

[0679] "Significant changes" refer to points in legal revisions that are deemed to have a direct impact on a company's business processes.

[0680] "Generative AI technology" refers to technology that uses AI technology to generate new information and structures, and in this invention, it is used for generating countermeasures, etc.

[0681] An "information storage device" refers to a recording medium or database system used to store data and information, and is used to facilitate the storage and retrieval of information.

[0682] A "planning and management system" refers to a management system that manages the execution of projects and tasks, and makes their progress visible and adjustable.

[0683] "Evaluation criteria" refer to pre-established guidelines or standards used to assess the impact of changes in laws and regulations on a company's operations, and are used to determine appropriate responses.

[0684] This invention provides a system necessary for companies to respond quickly to changes in laws and regulations. In its specific implementation, the system is designed so that servers, terminals, and users each play their respective roles, and the compliance process is carried out efficiently.

[0685] The server first automatically retrieves legal information from the information provider. This is done using a program that periodically retrieves the latest legal information via a database or API. This information is collected by the server and then proceeds to the next analysis process; currently, programming languages ​​such as Python and request libraries are used for this process.

[0686] Next, the server analyzes the legal information obtained using natural language processing technology. For this analysis, for example, it uses spaCy, a Python natural language processing library, to analyze important keywords and sentence structures from legal documents and extract changes that are important to the company.

[0687] The analyzed information is then used with generative AI technology to match user-related assignment information and generate specific countermeasures. This countermeasure generation utilizes a generative AI model to create action plans tailored to the company's business processes.

[0688] Users receive notifications sent from the server via their devices. These notifications include specific actions and priorities that have been generated, allowing users to quickly take action based on them. For example, a prompt such as, "Analyze the changes in new environmental regulations from a manufacturing department's perspective and propose necessary actions," will prompt the generating AI to present the optimal solution to the department.

[0689] Furthermore, since the server stores a history of legal changes in an information storage device, users can easily search past data and refer to examples. This enables a swift and effective response when similar legal changes occur.

[0690] These processes are integrated with the project management system, and the generated corresponding tasks are automatically registered. Users can visualize task progress on the project management interface via their terminals and make adjustments as needed to improve overall work efficiency.

[0691] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0692] Step 1:

[0693] The server accesses databases and external APIs that provide legal information to retrieve the latest laws and regulations. It uses the URL or API key of the information source as input and generates text data of the retrieved laws as output. This step uses HTTP communication libraries, such as request libraries, to perform the specific actions of retrieving data from the web.

[0694] Step 2:

[0695] The server analyzes the acquired text data of laws and regulations using a natural language processing library. It receives the text of the laws and regulations as input, extracts important changes through data processing, and generates a list of changes as output. Specifically, it uses NLP libraries such as spaCy to analyze the meaning and structure of the text.

[0696] Step 3:

[0697] The server evaluates the impact of the analyzed changes on business processes. Using a list of changes and data on the company's business processes as input, it generates a list of affected business processes as output. This step includes specific actions to perform simulations of business processes using a pre-configured evaluation model.

[0698] Step 4:

[0699] The server generates specific countermeasures using generative AI technology based on the evaluation results. It takes a list of affected business processes as input and generates detailed countermeasures and action plans as output. This process includes leveraging a generative AI model and using prompt statements to suggest the optimal countermeasure.

[0700] Step 5:

[0701] The server sends the generated countermeasures to the relevant personnel via email or a notification system. It receives information about the countermeasures as input and sends notifications to the appropriate personnel as output. Specifically, it uses email sending functionality and notification APIs to transmit information in real time.

[0702] Step 6:

[0703] The server stores a history of all legal changes in a database and manages it in a searchable format for the future. It receives legal changes and related data as input and records them in the database as output. In this step, a database management system such as SQL is used to implement storage and search functions.

[0704] Step 7:

[0705] The server registers the generated countermeasures in the project management system and visualizes the progress. It uses information on the countermeasures and related tasks as input and generates tasks registered in the project management system as output. Specifically, this includes the operation of automatically registering tasks through an interface to the project management tool API.

[0706] (Application Example 1)

[0707] 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".

[0708] Changes in laws and regulations can have a significant impact on a company's business activities, and there is a particular need to respond quickly to changes in security-related laws and regulations. However, manually acquiring relevant information from vast amounts of legal data, analyzing it, and evaluating its impact on business activities is a highly specialized and inefficient process. There is a need for solutions to these challenges and to streamline the legal compliance process.

[0709] 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.

[0710] In this invention, the server includes means for automatically acquiring legal information from a data source and detecting changes in the law; means for analyzing the changes in the law using natural language processing and extracting key changes; means for evaluating the impact of the analyzed changes on business activities and identifying the relevant personnel; means for automatically generating countermeasures and notifying the personnel; means for storing the history of changes in the law in a storage device and managing it in a searchable state; means for providing reference cases based on the history; and means for transmitting the generated countermeasures to an information terminal and displaying a recommended action plan. This makes it possible to automatically evaluate the impact of changes in the law on a company's security-related activities and to respond quickly and efficiently.

[0711] "Legal information" refers to information about laws and regulations enacted by countries and regions, and is data that companies and organizations need for legal compliance.

[0712] "Data source" refers to the source from which legal information and related data are collected, and includes online databases and official websites.

[0713] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language, and is used in applications such as analyzing text data.

[0714] "Analysis" is the process of breaking down large amounts of data, organizing its contents, and extracting useful information from it.

[0715] "Changes" refer to the alterations in content that occur when laws are amended, and are recognized as differences between the old and new laws.

[0716] "Business activities" refer to the process of producing and providing goods and services that a company undertakes for the purpose of economic gain.

[0717] "Impact assessment" is the process of analyzing the results and impacts that changes in laws and regulations have on various activities and departments within a company.

[0718] The term "person in charge" refers to a person responsible for carrying out specific tasks or duties, and is a person who assumes a specific role within a company.

[0719] A "recommended action plan" is a plan that outlines a series of specific actions that are desirable to implement in response to changes in laws and regulations.

[0720] The system that realizes this invention operates primarily using a cloud server, user terminals, a database, and a natural language processing engine. The server periodically accesses data sources that provide legal information to detect whether new laws have been issued or amended. The data sources used include online databases of official organizations and public government websites.

[0721] The server analyzes legal information obtained using natural language processing technologies such as the Google Cloud Natural Language API. This analysis extracts key changes from a comparison of old and new versions of the law. Subsequently, machine learning models such as TensorFlow are used to evaluate the impact of the extracted changes on the company's business activities. Evaluation models set up for each department of the company are used for the impact assessment.

[0722] Once the relevant personnel are identified, the server automatically generates a response plan and sends it to the user's device via push notification. This notification includes a recommended action plan and priorities. The data is managed in Firebase Realtime Database and other systems, accumulating a change history and allowing users to search through past versions at any time.

[0723] For example, when a new data protection law is announced, the server immediately performs an analysis to identify the impact of the legal change on the company's security management. The generated countermeasures are notified to security personnel, who are instructed to implement protocols that comply with the new data protection regulations. In this way, a system is in place to respond quickly and efficiently to changes in legislation.

[0724] Example of a prompt:

[0725] "Please promptly analyze and report on the impact of the newly announced data protection legislation on our company's data security protocols. Please prioritize and indicate the necessary changes and countermeasures."

[0726] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0727] Step 1:

[0728] The server accesses data sources that provide legal information and periodically checks for new laws or changes. Inputs include URLs or API endpoints of the data sources, and output is the retrieved legal information data. These data sources include online databases and official websites, and information is collected using HTTP requests.

[0729] Step 2:

[0730] The server passes the acquired legal information to a natural language processing engine. The input is raw legal text data, and the output is parsed language data. The Google Cloud Natural Language API is used to extract major changes and structure their content.

[0731] Step 3:

[0732] The server inputs the analyzed changes into a machine learning model to evaluate the impact of the changes on business activities. The input is analyzed linguistic data, and the output is the impact assessment results. The impacts are classified and prioritized according to the company's existing assessment models, using tools such as TensorFlow.

[0733] Step 4:

[0734] The server automatically generates countermeasures based on the impact assessment results. The input is the impact assessment results data, and the output is a list of generated countermeasures. This includes specific action plans for each affected business process.

[0735] Step 5:

[0736] The server delivers the generated countermeasures as push notifications to the terminals of the relevant personnel. The input is a list of generated countermeasures, and the output is a recommended action plan for each notified personnel. Notifications are sent using methods such as Firebase Cloud Messaging, allowing personnel to take immediate action.

[0737] Step 6:

[0738] The server stores and manages a history of legal changes in a database in a retrievalable format. Input consists of all data after analysis and impact assessment, while output is historical data that users can search at any time. Firebase Realtime Database is used for data storage and management.

[0739] Step 7:

[0740] Users access historical data via their devices to derive efficient countermeasures for similar legal changes based on past cases. The input is the user's search query, and the output is a list of relevant past change histories and countermeasures resulting from this process. This allows users to efficiently leverage past experience.

[0741] 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.

[0742] This invention incorporates an emotion engine into an automated system for collecting legal information, assessing its impact based on changes, and developing countermeasures, thereby considering user emotions. This enables flexible communication that reflects the user's emotional state when providing notifications and information.

[0743] First, the server accesses data sources that provide legal information to automatically retrieve new legal information and detect changes. The amended laws are analyzed by a natural language processing engine to extract key changes. The server then evaluates the impact of these changes on business operations and identifies the relevant personnel. A pre-configured evaluation model is used for this impact assessment.

[0744] The emotion engine plays a crucial role in this notification process. When a user receives a notification of a change in legislation, the server uses the emotion engine to analyze the user's emotional state in real time and adjust the notification content and wording accordingly. For example, if a user is feeling stressed, the notification can be phrased in a more relaxing manner, demonstrating an emotionally sensitive approach.

[0745] As a concrete example, consider the case where a new amendment to the Labor Standards Act is made. This information is analyzed on a server, and its impact on the HR department is evaluated. When the person in charge receives this notification during a busy period, the emotion engine analyzes their emotional state and explains the amendment in calm language that does not cause them to feel burdened.

[0746] Furthermore, the emotion engine can receive user feedback and automatically improve its responses. This feedback loop allows the system to continuously adapt to user emotions and needs, aiming to provide more personalized services.

[0747] Furthermore, the server stores a history of legal changes in a database, and users can access past history via their terminals. In addition, the generated countermeasures are registered in the project management system, allowing users to manage tasks via their terminals. In this way, by incorporating an emotion engine, a more humane and flexible legal compliance system is built.

[0748] The following describes the processing flow.

[0749] Step 1:

[0750] The server connects to data sources that provide legal information and automatically retrieves new laws and changes periodically. This process may involve web scraping or APIs. When a change in legal information is detected, it is recorded.

[0751] Step 2:

[0752] The server uses a natural language processing engine to analyze the key points of legal changes on the acquired legal information. The analyzed data is then analyzed using text mining and text classification techniques to extract important changes.

[0753] Step 3:

[0754] Based on the analyzed changes, the server evaluates which business processes will be affected by the legal changes. This is done using an evaluation model that takes into account the relationship between pre-configured business processes and legal requirements.

[0755] Step 4:

[0756] The server automatically generates countermeasures based on the evaluation results. These countermeasures include specific implementation procedures and necessary modifications and improvements to affected business processes.

[0757] Step 5:

[0758] The server uses an emotion engine to analyze the user's emotional state. This is done to determine the user's current emotional state based on the user's past responses and real-time input data.

[0759] Step 6:

[0760] The server adjusts notification content to take the user's emotional state into consideration. For example, if the user is feeling stressed, the notification will be more concise and use gentler language. It will also provide supplementary information to alleviate anxiety.

[0761] Step 7:

[0762] The server sends the coordinated notifications to the designated user, utilizing various channels such as email notifications and internal messengers.

[0763] Step 8:

[0764] The server stores all legal changes and corresponding measures in a database, making them searchable and accessible to users via their terminals.

[0765] Step 9:

[0766] The terminal automatically registers the generated countermeasures with the project management system, making it easier for users to manage progress. Users can monitor tasks through the terminal and make necessary corrections.

[0767] (Example 2)

[0768] 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".

[0769] Changes in laws and regulations have a significant impact on business activities, making it crucial to collect this information in a timely manner and assess its impact on operations. However, there is no system in place that can efficiently collect and analyze information, assess its impact, and notify stakeholders. Furthermore, it is difficult to consider the emotional state of stakeholders when making notifications. Therefore, there is a need for a system that can respond flexibly and efficiently to changes in laws and regulations.

[0770] 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.

[0771] In this invention, the server includes means for automatically acquiring data from data sources that provide information and detecting changes; means for analyzing the changes in the data using natural language processing and extracting key points; and means for analyzing the emotional state of stakeholders using sentiment analysis technology and adjusting the content of notifications. This enables efficient collection and analysis of information on changes in laws and regulations, appropriate evaluation of the impact on business operations, and provision of information in a manner that takes into account the emotions of the recipients.

[0772] "Data sources that provide information" refers to data storage or network resources that provide, access to, and retrieve information related to laws and regulations.

[0773] "Means of detecting changes" refers to processes and systems for determining whether new content or updates have occurred in continuously monitored information.

[0774] "Natural language processing" refers to the technology that enables computers to understand, process, and analyze human language, and is particularly used for analyzing text data and extracting important information.

[0775] "Methods for extracting key points" refers to the process of identifying and extracting particularly important parts or parts where changes have occurred from a large amount of information.

[0776] "Emotional analysis technology" refers to techniques for analyzing and evaluating human emotions and psychological states from written and behavioral data.

[0777] "Means of adjusting notification content" refers to the process of changing the expression and method of information to suit the recipient, based on specific criteria or analysis results.

[0778] A "data storage device" refers to a hardware or software system that stores acquired data and its update history, and enables retrieval and use as needed.

[0779] A "work management system" refers to a software platform for effectively tracking and managing generated countermeasures and the progress of tasks.

[0780] An "evaluation model" refers to an analytical method or algorithm used to evaluate the impact of data and events on business operations based on specific rules or historical data.

[0781] This invention is a system that efficiently collects and analyzes information on changes in laws and regulations, conducts impact assessments related to business operations, and notifies stakeholders of the information while considering their emotional state. A specific example of this system is shown below.

[0782] The server continuously accesses data sources that provide legal information and automatically retrieves new information from specific websites and APIs. This process uses scraping tools and software for API access. Specifically, it uses Python's Beautiful Soup to retrieve data from web pages and, when necessary, downloads data directly using APIs. The retrieved data is stored in a database.

[0783] Next, the server uses a natural language processing engine to analyze the text of the acquired legal information. For example, it uses Python's NLTK or SpaCy to tokenize the legal text and extract key changes. This allows for the analysis of significant changes in the legal text.

[0784] Based on the analysis results, the server uses a pre-configured evaluation model to assess the impact on operations. This evaluation model is implemented as a rule-based algorithm or machine learning model, which identifies stakeholders. As a result of this process, a list of stakeholders who need to take responsibility is generated.

[0785] When sending notifications, the server uses sentiment analysis technology to analyze the emotional state of those involved. Related tools include Google's Perspective API and proprietary models. This analysis adjusts the notification content to match the recipient's emotional state. As a result, notifications received by users are expressed in a gentler and less burdensome manner.

[0786] For example, a user can input a prompt such as, "Please explain the changes in the new Labor Standards Act in a relaxed tone," into an AI model to generate a tailored notification.

[0787] In this way, the server can efficiently manage a series of information related to changes in laws and regulations, and appropriately notify users via their terminals. As a result, users can receive information in a way that is easy to understand and less stressful for them.

[0788] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0789] Step 1:

[0790] The server accesses data sources that provide information and retrieves new information. The input data is the URL or API endpoint of the data source, and the output is the raw data of the retrieved legal information. Specifically, the server uses a Python script to scrape web pages using the Beautiful Soup and Requests libraries to collect the necessary information.

[0791] Step 2:

[0792] The server detects changes from the acquired legal information. The input is the raw data acquired in step 1, and the output is the text of the changed parts. The server compares the new data with the information stored in the past database and runs a comparison algorithm to find the differences.

[0793] Step 3:

[0794] The server analyzes the information where changes have been detected using a natural language processing engine and extracts the main points of change. The input is the modified text detected in step 2, and the output is the extracted main points of change. In this step, the server tokenizes the text using Python's NLTK or SpaCy library and extracts important keywords and phrases.

[0795] Step 4:

[0796] The server evaluates the impact on business operations based on the extracted change points. The input is the major change points extracted in step 3, and the output is a list of affected business departments and stakeholders. The server uses a pre-configured rule-based evaluation model to determine the degree of impact of the given information on business operations and lists the relevant departments and individuals.

[0797] Step 5:

[0798] The server analyzes the user's emotional state before delivering a tailored notification. Inputs are past user behavior data and feedback, while output is an assessment of the user's current emotional state. The server utilizes Google's Perspective API and its own models to analyze the user's emotional tone and adjust the tone of the notification accordingly.

[0799] Step 6:

[0800] The server generates a coordinated notification and sends it to the user via the terminal. The input is the notification content coordinated in step 5, and the output is the final notification message the user receives. In this step, the server automatically delivers the notification using a mail server or messaging protocol.

[0801] Step 7:

[0802] Users receive notifications through their devices and refer to the legal change history as needed. Inputs are all notification and change history data, while outputs are the historical information the user refers to. Users can search the data and review past history using the application on their device or the web interface.

[0803] Step 8:

[0804] Users provide feedback, and the server continuously improves the system using a generative AI model. Input is user feedback data, and output is notifications and system behavior that reflect the improvements. The server incorporates the new feedback into its training data, retrains the AI ​​model, and enhances the user experience.

[0805] (Application Example 2)

[0806] 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".

[0807] Changes in laws and regulations often have a significant impact on business operations, but the process of acquiring this information, assessing its impact, and notifying relevant personnel may fail to consider the emotional state of users, potentially leading to stress and confusion. Therefore, there is a need for a system that takes users' feelings into consideration and provides notifications through appropriate communication.

[0808] 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.

[0809] In this invention, the server includes means for automatically acquiring legal information from information sources and detecting changes in laws and regulations; means for analyzing the content of legal changes using natural language processing and extracting key changes; means for evaluating the impact of the analyzed changes on business operations and identifying relevant individuals; notification means combining a sentiment analysis engine that analyzes the user's emotional state and flexibly adjusts notification content; and means for storing the history of legal changes in an information storage device and managing it in a searchable state. This makes it possible to appropriately provide information on legal changes while taking into consideration the user's emotions.

[0810] "Legal information" refers to official information relating to laws, rules, or regulations that may affect business or personal activities.

[0811] "Information source" refers to the original data provider or database used to obtain legal information.

[0812] "Natural language processing" is a technology that enables computers to understand and analyze human language, and makes it possible to automatically analyze text data.

[0813] "Assessing the impact" is the process of quantitatively or qualitatively determining how changes in laws and regulations will affect business operations.

[0814] "Relevant persons" refers to individuals whose duties or responsibilities may be directly affected by changes in laws and regulations.

[0815] An "emotion analysis engine" is a technology that analyzes a user's emotional state in real time and adjusts communication based on that information.

[0816] "Notification means" refers to a method or medium for communicating changed legal information to users.

[0817] An "information storage device" is a data storage facility that stores acquired information and makes it accessible or retrievable as needed.

[0818] The system of this invention provides users with information that is sensitive to their emotions through the acquisition, analysis, evaluation, notification, and history management of legal information.

[0819] The server automatically retrieves legal information from its sources and detects changes. The retrieved information is analyzed using a natural language processing engine to extract key changes. This analysis utilizes tools such as Python and TensorFlow, processing the information as text data. For impact assessment, a pre-configured evaluation model is used to determine how new legal changes will affect operations. The evaluation results are used to identify relevant individuals.

[0820] The device features an emotion analysis engine that analyzes the user's emotional state in real time. This engine uses generative AI models, such as OpenAI's GPT model, to analyze the user's current emotional state. Notification content is adjusted based on this emotion analysis, enabling flexible communication. For example, if a notification about a change in legislation is likely to cause stress, the content of the notification will be changed to more relaxing language.

[0821] Furthermore, the server comprehensively stores a history of legal changes in its information storage device and manages it so that users can search past history as needed. This function allows users to easily access past change history related to their work.

[0822] For example, if a user receives a notification about a new tax law revision, the sentiment analysis engine will detect the user's anxiety and send a notification using phrasing such as, "The new tax law revision will change your tax rate, but please rest assured that we will support you with the detailed procedures." An example of a prompt message would be, "We have detected that the user is anxious about the tax law change. Please suggest ways to soften the notification. The change is the application of a new tax rate."

[0823] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0824] Step 1:

[0825] The server automatically retrieves legal information from its sources. At this stage, it uses databases and APIs to collect the latest legal information. The input is legal information data, and the output is the retrieved raw legal data. The server accesses the data periodically or triggered by events, sending requests to receive data.

[0826] Step 2:

[0827] The server analyzes legal information obtained using a natural language processing engine and extracts key changes. The input is the raw legal data obtained, and the output is a list of the analyzed key changes. For text analysis of the data, Python libraries such as NLTK and SpaCy are used to identify and summarize the changes.

[0828] Step 3:

[0829] The server evaluates the impact of the analyzed changes on business operations and identifies the individuals involved. The input is a list of key changes, and the output is the impact analysis results and a list of the individuals involved. A pre-configured data model is used for the evaluation to quantitatively analyze how the changes affect business processes.

[0830] Step 4:

[0831] The device uses an emotion analysis engine to analyze the user's current emotional state. The input is data related to the user's emotions (e.g., past response history and current task content), and the output is the current emotional state. The engine uses a generative AI model to analyze the user's emotions and create prompts.

[0832] Step 5:

[0833] The server flexibly adjusts the notification text based on the sentiment analysis results and sends the notification to the user from the terminal. The inputs are the impact analysis results, a list of relevant people, and the user's emotional state, while the output is the adjusted notification content. The notification is customized based on the generated prompt text.

[0834] Step 6:

[0835] The server stores a history of legal changes in an information storage device and manages it so that users can search the history as needed. Inputs include changes, impact analysis results, and notification content, while output is a well-organized database of legal change history. Users can access past historical information through their terminals and extract data when necessary.

[0836] 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.

[0837] 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.

[0838] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0839] 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.

[0840] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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."

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0857] The following is further disclosed regarding the embodiments described above.

[0858] (Claim 1)

[0859] A means for automatically acquiring legal information from a data source and detecting changes in laws and regulations,

[0860] A method for analyzing changes in laws and regulations using natural language processing and extracting the main changes,

[0861] A means to evaluate the impact of the analyzed changes on business operations and identify the relevant personnel,

[0862] A means to automatically generate countermeasures and notify the person in charge,

[0863] A means of storing and managing a history of legal changes in a database in a searchable format,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, further comprising means for registering the generated countermeasures with a project management device and managing them continuously.

[0867] (Claim 3)

[0868] The system according to claim 1, comprising means for using a pre-configured evaluation model when conducting an impact assessment of changes in laws and regulations.

[0869] "Example 1"

[0870] (Claim 1)

[0871] A means for automatically acquiring legal information from an information provision device and detecting changes in laws and regulations,

[0872] A means of analyzing legal changes using natural language processing technology and extracting important changes,

[0873] A means to evaluate the impact of the analyzed changes on business processes and identify the relevant business personnel,

[0874] A means of automatically generating countermeasures using generational AI technology and notifying the person in charge of operations,

[0875] A means of storing the history of legal changes in an information storage device and managing it in a searchable state,

[0876] A means to register the generated countermeasures in the planning management system, visualize the progress of the work, and make it adjustable,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, comprising means for using pre-set evaluation criteria when conducting an impact assessment of changes in laws and regulations.

[0880] (Claim 3)

[0881] The system according to claim 1, comprising means for generating a concrete action plan using generative AI technology.

[0882] "Application Example 1"

[0883] (Claim 1)

[0884] A means for automatically acquiring legal information from a data source and detecting changes in laws and regulations,

[0885] A method for analyzing changes in laws and regulations using natural language processing and extracting the main changes,

[0886] A means to evaluate the impact of the analyzed changes on business activities and identify the relevant personnel,

[0887] A means to automatically generate countermeasures and notify the person in charge,

[0888] A means of storing a history of legal changes in a storage device and managing it in a searchable state,

[0889] Means for providing reference cases based on history,

[0890] A means for transmitting the generated countermeasures to an information terminal and displaying the recommended action plan,

[0891] A system that includes this.

[0892] (Claim 2)

[0893] The system according to claim 1, comprising means for registering the generated countermeasures with a project management device and continuously managing them, and for sending push notifications to an information terminal.

[0894] (Claim 3)

[0895] The system according to claim 1, comprising means for using a pre-configured evaluation model and historical data to identify the priority of impacts when evaluating the impact of changes in laws and regulations.

[0896] "Example 2 of combining an emotion engine"

[0897] (Claim 1)

[0898] A means for automatically acquiring data from data sources that provide information and detecting changes,

[0899] A method for analyzing data changes using natural language processing and extracting key points,

[0900] A means to evaluate the impact of the analyzed and modified content on business operations and to identify relevant stakeholders,

[0901] A means of analyzing the emotional state of stakeholders using emotion analysis technology and adjusting the content of notifications,

[0902] A means of automatically generating countermeasures and notifying relevant parties,

[0903] A means for storing change history in a data storage device and managing it in a searchable state,

[0904] A system that includes this.

[0905] (Claim 2)

[0906] The system according to claim 1, further comprising means for registering the generated countermeasures in a work management system and for continuously managing them.

[0907] (Claim 3)

[0908] The system according to claim 1, comprising means for using a pre-configured evaluation model when performing an impact assessment of a change.

[0909] "Application example 2 when combining with an emotional engine"

[0910] (Claim 1)

[0911] A means of automatically obtaining legal information from sources and detecting changes in laws and regulations,

[0912] A method for analyzing changes in laws and regulations using natural language processing and extracting the main changes,

[0913] A means to evaluate the impact of the analyzed changes on business operations and identify the individuals involved,

[0914] A notification method that combines an emotion analysis engine that analyzes the user's emotional state and flexibly adjusts the notification content,

[0915] A means of storing a history of legal changes in an information storage device and managing it in a searchable state,

[0916] A system that includes this.

[0917] (Claim 2)

[0918] The system according to claim 1, further comprising means for registering the generated countermeasures in a business management device and managing them continuously.

[0919] (Claim 3)

[0920] The system according to claim 1, comprising means for using a pre-configured evaluation model when conducting an impact assessment of changes in laws and regulations. [Explanation of Symbols]

[0921] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for automatically acquiring legal information from a data source and detecting changes in laws and regulations, A method for analyzing legal changes using natural language processing and extracting key changes, A means to evaluate the impact of the analyzed changes on business activities and identify the relevant personnel, A means to automatically generate countermeasures and notify the person in charge, A means of storing a history of legal changes in a storage device and managing it in a searchable state, Means for providing reference cases based on history, A means for transmitting the generated countermeasures to an information terminal and displaying the recommended action plan, A system that includes this.

2. The system according to claim 1, comprising means for registering the generated countermeasures with a project management device and continuously managing them, and for sending push notifications to an information terminal.

3. The system according to claim 1, comprising means for using a pre-configured evaluation model and historical data to identify the priority of impacts when evaluating the impact of changes in laws and regulations.

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