system

Abstract

We provide the system. [Solution] Means of obtaining information to monitor changes in laws and regulations, Information analysis means for analyzing acquired information and summarizing changes in laws and regulations, An impact assessment method for evaluating the impact on an organization based on the analysis results, A means of notifying countermeasures to propose and notify countermeasures based on the evaluation results, A history management system that databases the history of legal changes and manages that data in a searchable format, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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] Conventionally, business processes associated with changes in laws and regulations required manual information collection and analysis, which consumed a lot of time and labor. As a result, the workload increased and the risk of human error became higher. In addition, the inability to quickly respond to changes in laws and regulations can pose a significant risk to enterprises. Therefore, it has been required to efficiently and accurately grasp the content of changes in laws and regulations and respond quickly.

Means for Solving the Problems

[0005] This invention provides means for acquiring information to monitor changes in laws and regulations, means for analyzing the acquired information to summarize the changes in laws and regulations, means for evaluating the impact on the organization based on the analysis results, means for proposing and notifying countermeasures according to the evaluation results, and means for creating a database of the history of changes in laws and regulations and managing the data in a searchable format. By providing these means, it streamlines the collection and analysis of information related to changes in laws and regulations, enabling rapid information sharing and proposal of countermeasures to relevant departments and personnel within the organization. This makes it possible to ensure thorough compliance with laws and regulations and improve operational efficiency.

[0006] "Information acquisition methods" refer to means for automatically obtaining information on changes in laws and regulations from external online data sources.

[0007] "Information analysis means" refers to methods used to analyze acquired information on changes in laws and regulations and to summarize the changes, and natural language processing technology can be utilized.

[0008] An "impact assessment tool" is a means of evaluating the impact that a legal change will have on an organization or its operations, based on the analyzed content of the legal change.

[0009] A "means for notifying about countermeasures" refers to a means of proposing appropriate countermeasures based on the results of an impact assessment and notifying relevant parties of them.

[0010] A "history management system" is a means of recording information on changes in laws and regulations and analysis results in a database and managing it in a searchable format. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] The legal monitoring system of the present invention consists of multiple components in order to efficiently monitor changes in laws and regulations and to facilitate appropriate responses within corporate organizations.

[0033] The server plays a central role in monitoring legal changes, automatically obtaining the latest legal updates by regularly checking online data sources and government APIs. When the server detects new information, it registers it as a change event and records it in the database.

[0034] The acquired information is analyzed by information analysis tools on the server. The server uses natural language processing (NLP) technology to analyze legal documents and automatically generates key changes and summaries. This analysis process extracts the essential points of the changes and efficiently evaluates their impact on the organization.

[0035] The impact assessment is performed using algorithms on the server to identify which departments and business processes within the organization the change affects. The server analyzes the degree of impact and assesses the impact on the relevant departments to determine appropriate countermeasures.

[0036] Next, the server uses a response notification system to propose specific countermeasures based on the analysis and evaluation results and notify the relevant parties. This is done via email or an in-system dashboard, enabling users to take necessary actions quickly.

[0037] Furthermore, all legal changes and their analysis results are stored in a database through a history management system. This database is accessible via terminals, allowing users to easily search and refer to past legal change history.

[0038] For example, if new data protection legislation is enacted, the server will detect it and notify the data management department to implement the new protective measures. In this way, cumbersome legal research is eliminated, and users can immediately understand and act on what they need to do to comply with the law.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The server periodically accesses online data sources and government APIs that provide legal information to obtain new legal changes and notifications. The server collects data using scraping techniques and API calls, and records any updates it detects.

[0042] Step 2:

[0043] The server analyzes the acquired information using natural language processing (NLP) techniques. The server analyzes the text of legal documents and automatically generates key changes and summaries. At this stage, the information is structured and prepared for use in subsequent processes.

[0044] Step 3:

[0045] The server uses the analysis results to evaluate the impact on the organization and operations. The server matches the analysis results with business process information and departmental information stored in the internal database to identify which departments and processes may be affected.

[0046] Step 4:

[0047] The server develops appropriate countermeasures based on the assessment results. The server lists the necessary response procedures for each affected department and creates notification messages to inform relevant parties within the company.

[0048] Step 5:

[0049] The server notifies users of the necessary actions. The server sends notifications to users in the relevant department via email or the system's dashboard, enabling them to take the required action quickly.

[0050] Step 6:

[0051] The server stores information on legal changes, analysis results, and impact assessment results in a database. Users can easily access past legal change history and response history by searching this database using their terminals.

[0052] (Example 1)

[0053] 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."

[0054] Changes in legislation are often complex, and it is crucial for organizations to quickly carry out processes of proper information gathering, analysis, impact assessment, and proposal of countermeasures. However, doing this manually is extremely time-consuming and resource-intensive. Furthermore, continuously tracking and making available the history of legal changes is not easy.

[0055] 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.

[0056] In this invention, the server includes an information acquisition means for automatically collecting information from a data source, an information analysis means that utilizes natural language processing technology to analyze the collected data and summarize the changes in laws and regulations, and an impact assessment means that identifies and evaluates the impact on each department and operation within the organization based on the analysis results. This enables rapid and efficient monitoring and response to changes in laws and regulations.

[0057] "Information acquisition means" refers to processes or devices that automatically collect relevant information from data sources.

[0058] "Information analysis means" refers to the process or apparatus of analyzing collected data and summarizing the changes in laws and regulations using natural language processing technology.

[0059] "Impact assessment tools" refer to processes and devices used to identify and evaluate the impact on various departments and operations within an organization based on analysis results.

[0060] A "means of notifying about countermeasures" refers to a system or device that proposes necessary actions based on an evaluation and notifies relevant parties.

[0061] "History management means" refers to a process or device for accumulating changes in laws and regulations and their analysis results, and enabling searching and referencing them via terminals.

[0062] "Natural language processing technology" refers to the technology used to enable computers to understand and process human language.

[0063] This invention aims to provide a legal monitoring system that enables organizations to quickly and efficiently monitor changes in laws and regulations and to facilitate appropriate responses. This system primarily consists of server-centric functions and operates as follows:

[0064] The server obtains information on changes in laws and regulations via web services and online databases. This is achieved through various means of information retrieval, such as periodically scanning data sources using APIs to obtain the latest legal information.

[0065] Furthermore, the server processes the acquired legal information using information analysis tools. This analysis utilizes natural language processing technologies such as SpaCy and NLTK libraries. This makes it possible to tokenize changes in the laws and extract and summarize the main points.

[0066] Furthermore, the server uses impact assessment tools to evaluate the analyzed data and determine which departments and business processes within the organization are affected. This involves using analytical algorithms to compare the changes with the organization's business profile.

[0067] Based on analysis and impact assessment, the server notifies stakeholders of the necessary countermeasures through its countermeasure notification system. This notification is provided to users via email or the system's dashboard, allowing them to take necessary actions quickly.

[0068] The terminal is used to allow users to access past legal change information through history management mechanisms and search for necessary data. This makes it easier for users to manage their access to past information.

[0069] For example, if new data protection legislation is enacted, the server will detect it. The server will then notify the data management department of the necessary countermeasures, including changes to new processes and procedures. This allows the organization to comply with the new legislation quickly.

[0070] The following prompt could be used as input for the generative AI model: "Please explain the main points of the newly enacted data protection law and the corresponding actions organizations should take."

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

[0072] Step 1:

[0073] The server accesses data sources using information acquisition methods based on a pre-configured schedule, automatically retrieving the latest legal change information from online databases and APIs. The input is connection information such as URLs and API keys, and the output is the latest legal change information. Specifically, the server periodically executes API calls and retrieves response data.

[0074] Step 2:

[0075] The server analyzes legal data acquired using information analysis tools. The input is the legal change information acquired in step 1. The server uses natural language processing technology (e.g., SpaCy or NLTK) to tokenize and analyze the legal document and extract important changes. The output is a summary of the main changes to the law. Specifically, the data is tokenized, noun phrases and verb phrases are extracted, and the important parts of the law are summarized.

[0076] Step 3:

[0077] The server uses impact assessment tools to evaluate the impact within the organization based on the analysis results. The input is the changes in legislation summarized in step 2. Based on the organization's business profile, the server identifies which departments and business processes will be affected. The output is the affected areas and the degree of impact. Specifically, an evaluation algorithm is used to identify the relevant departments within the organization.

[0078] Step 4:

[0079] The server uses a response notification mechanism to notify relevant parties of the necessary countermeasures. The input is the impact assessment results identified in step 3. The output is notification information sent to relevant departments and personnel within the organization. Specifically, the server sets up notification messages to be automatically generated by the server and immediately distributes them to relevant parties via email or dashboard.

[0080] Step 5:

[0081] Using a terminal, users access the history of legal changes through a history management system. Input consists of keywords and dates / times for the user's search. Output is detailed data of past legal change history. Specifically, the user logs into the system from the terminal, searches the database using a GUI, and retrieves the necessary information.

[0082] (Application Example 1)

[0083] 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."

[0084] Modern businesses are required to quickly and accurately grasp frequently changing laws and regulations and take appropriate countermeasures based on them. However, because legal information changes are so diverse, manual management is difficult, leading to risks of delayed compliance and incorrect responses. Furthermore, there are insufficient means to quickly assess the impact of legal changes on specific departments within an organization and to effectively notify those departments. As a result, there is a need to minimize the impact on legal compliance and security measures across the entire organization.

[0085] 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.

[0086] In this invention, the server includes information acquisition means for monitoring information on changes in laws and regulations, information analysis means for analyzing the acquired information and generating a summary, and impact assessment means for evaluating the impact based on the analysis results. This makes it possible to grasp changes in laws and regulations in real time and automatically provide accurate notifications to the relevant departments.

[0087] "Changes to laws and regulations" refer to changes in the content of laws and regulations enacted or amended by the government or administrative agencies.

[0088] "Information acquisition means" refers to a function that automatically acquires the latest information on legal changes from online data sources or APIs.

[0089] "Information analysis means" refers to technology that analyzes acquired legal information and generates key changes and summaries of it.

[0090] "Impact assessment tools" refer to the function that evaluates which departments or business processes within an organization will be affected by the analyzed legal changes.

[0091] "Means of notifying countermeasures" refers to the means of communicating necessary countermeasures to relevant departments based on changes in laws and regulations.

[0092] "History management means" refers to a function that stores the history of legal changes and analysis results in a database and manages them in a way that allows them to be referenced later.

[0093] An "application that notifies machines and devices in real time" refers to software that immediately notifies users of changes in laws and regulations on their devices, making relevant information accessible.

[0094] "Machine learning" refers to the technology that allows computers to learn from past data and patterns to predict or classify future data and events.

[0095] A "security policy" refers to a set of guidelines and procedures designed to help an organization protect its information and comply with laws and regulations.

[0096] To implement this invention, a system is constructed in which a server plays a central role. The server is equipped with means for automatically obtaining information on changes in laws and regulations from online data sources and government APIs. In this process, cloud services such as AWS® and Google® Cloud are utilized for data acquisition and storage. The acquired legal information is analyzed using NLP (Natural Language Processing) technology to generate a summary of the changes. Open-source libraries such as SpaCy and NLTK are used for this analysis.

[0097] The server also includes an impact assessment tool that uses machine learning techniques to evaluate the impact of legal changes on the organization's security policies. The results of this assessment are sent as push notifications to user terminals in relevant departments via notification services such as Firebase Cloud Messaging. Users who receive the notification can then check the content on their smartphones or desktop devices and take necessary countermeasures quickly.

[0098] For example, if new data protection regulations are announced, the server will immediately detect them, perform an impact analysis, and notify users of the necessary actions. This process allows users to accurately understand the situation and ensure legal compliance while improving organizational security.

[0099] To gain specific insights into the impact of new legislation on an organization using a generative AI model, the following prompt statements can be used:

[0100] "New data protection legislation has been announced. What changes will this have on how companies manage their data?"

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] The server periodically accesses online data sources and government APIs to retrieve information on changes in legislation. The input is API requests, and the output is raw data of the legislation. The server stores this data in a database. This process is performed as an automatically scheduled task.

[0104] Step 2:

[0105] The server analyzes the acquired legal data using natural language processing (NLP) techniques. The input is the raw legal data obtained in step 1, and the output is a summary of legal changes. The server extracts characteristic keywords and phrases and summarizes the important changes. The SpaCy library is often used for this process.

[0106] Step 3:

[0107] The server assesses the impact on each department of an organization based on a summary of the legal changes. The input is the summary information generated in step 2, and the output is a list of affected departments and the degree of impact. A machine learning model is used for the impact assessment, making decisions based on past data and impact patterns.

[0108] Step 4:

[0109] The server develops necessary countermeasures based on the impact assessment results and notifies the user. The input is the impact assessment results from step 3, and the output is a push notification message. Firebase Cloud Messaging is used to send the notification directly to the user's device.

[0110] Step 5:

[0111] Users check notifications on their devices and refer to recommended actions to take the necessary steps. The input is the notification message, and the output is the actual status of the implemented actions. This ensures rapid compliance with regulations and appropriate measures within the organization.

[0112] Step 6:

[0113] The server stores all legal change information, its analysis results, and a history of countermeasures in a database. Input is the data obtained in each step so far, and output is searchable historical data. Users can refer to past history as needed.

[0114] The above steps enable a swift and appropriate response to changes in legislation. Downstream users can use prompts for the generated AI model, such as, "New data protection legislation has been announced. What changes will affect our company's data management?", to conduct a detailed impact analysis.

[0115] 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.

[0116] This invention combines a system for monitoring and analyzing changes in laws and regulations with an emotion engine that recognizes user emotions, thereby enabling the notification of more appropriate and effective countermeasures. Specific embodiments for carrying out this invention are shown below.

[0117] The server, like traditional legal monitoring systems, periodically retrieves information on changes in laws and regulations using online data sources and government APIs. This ensures that the server always maintains up-to-date legal information and detects changes in real time.

[0118] Once legal information is retrieved, the server uses natural language processing technology to analyze the information and identify the key points of the changes and their impact. This allows for an assessment of the impact of legal changes on the organization and the development of necessary countermeasures.

[0119] The emotion engine, a key feature of this invention, has the ability to recognize the user's emotions in real time. This engine analyzes the user's facial expressions and tone of voice through a camera and microphone, and identifies emotions from them. Specifically, the terminal captures the user's facial expressions, and the server analyzes that data to determine the user's emotional state.

[0120] Information obtained from the emotion engine is reflected in notifications of legal changes and suggestions for countermeasures. For example, if the server detects that a user is experiencing stress, it adjusts the content of the notification to be softer and condenses the information provided to the bare minimum, thereby reducing the user's burden.

[0121] For example, if a user is concerned about a change after receiving notification of new data protection regulations, the system will soften the tone of the notification and present solutions in reassuring language.

[0122] This configuration allows for communication that not only informs users of legal changes but also takes their feelings into consideration, thereby supporting more effective legal compliance.

[0123] The following describes the processing flow.

[0124] Step 1:

[0125] The server periodically accesses online data sources and government APIs that provide legal information to obtain new legal changes and notifications. When the server detects new information, it records it in the database and registers it as a legal document to be analyzed.

[0126] Step 2:

[0127] The server analyzes acquired legal information using natural language processing (NLP) technology. The server analyzes the text of legal documents, extracts key changes, and automatically generates summaries. This analysis clarifies the specific impact on organizations and operations.

[0128] Step 3:

[0129] Based on the impact assessment results, the server will formulate necessary countermeasures regarding the legal changes. The server will list the countermeasures for each affected department and prepare them as notifications.

[0130] Step 4:

[0131] The user's device utilizes its camera and microphone to record the user's current emotional state. The device captures the user's facial expressions and collects digital data for analyzing their voice tone.

[0132] Step 5:

[0133] The server uses an emotion engine to analyze data sent from the terminal and identify the user's emotions. For example, it can determine whether the user is feeling at ease or stressed based on changes in voice tone and facial expressions.

[0134] Step 6:

[0135] The server adjusts the tone and content of notifications based on the user's emotional state. If the server determines that the user is stressed, it will make the notifications gentler and more concise to avoid burdening them.

[0136] Step 7:

[0137] The server sends a tailored notification to the user. The user receives the notification on their device and reviews the suggested course of action, which is presented in an emotionally sensitive manner.

[0138] Step 8:

[0139] The server stores information on legal changes, analysis results, and sentiment analysis results in a database. By using a terminal, users can easily access past legal change history, response history, and sentiment-based notification adjustment history by searching this database.

[0140] (Example 2)

[0141] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0142] Changes in laws and regulations are crucial information for organizations, and it is essential to grasp them in a timely manner and take appropriate action. However, conventional legal monitoring systems unilaterally notify users of information without considering their feelings, which can cause unnecessary stress to recipients. Furthermore, there is a challenge in providing flexible responses that take into account the emotional state of the recipients.

[0143] 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.

[0144] In this invention, the server includes information acquisition means, information analysis means, impact assessment means, emotion recognition means, and notification adjustment means. This makes it possible to adjust the content of legal change notifications to take into account the recipient's emotional state, thereby supporting more effective and user-friendly legal compliance.

[0145] "Information acquisition means" refers to an element that has the function of automatically acquiring information on changes in laws and regulations from a data set.

[0146] An "information analysis tool" is an element that has the function of analyzing acquired legal information using natural language processing technology and summarizing the changes.

[0147] An "impact assessment tool" is an element that has the function of evaluating the impact on an organization based on the analysis results.

[0148] A "means for notifying countermeasures" refers to an element that has the function of proposing countermeasures based on evaluation results and notifying the user of those countermeasures.

[0149] A "history management system" is an element that has the function of creating a database of the history of legal changes and managing them in a searchable format.

[0150] An "emotion recognition tool" is an element that has the function of recognizing and analyzing the user's emotions in real time.

[0151] A "notification adjustment mechanism" is an element that has the function of adjusting notification content based on information obtained from emotion recognition mechanisms and presenting it in a form optimized for the user.

[0152] To implement this invention, it is necessary to construct a system that monitors and notifies of changes in laws and regulations, and recognizes the emotions of users.

[0153] The server is responsible for automatically collecting information on changes in laws and regulations by utilizing online data sources and government APIs. This involves using scripts written in programming languages ​​such as Python and Java to periodically retrieve data via APIs. The retrieved legal information is then analyzed on the server using natural language processing (NLP) technology. This analysis utilizes generative AI models such as OpenAI's GPT and Google's BERT to extract summaries of the changes and their impact.

[0154] The device plays a crucial role in recognizing the user's emotions in real time. Specifically, it uses a camera and microphone to capture the user's facial expressions and tone of voice, and sends this data to a server. The server analyzes this data using emotion recognition tools to determine the user's emotional state. Face recognition libraries such as OpenCV and dlib are useful in this process.

[0155] Based on the results of emotion recognition, the notification content is adjusted. The server utilizes a generative AI model to automatically generate notification content with appropriate language according to the user's emotional state. For example, in a notification regarding new data protection regulations, if the user is feeling anxious, the notification tone is softened and reassuring language is used to create the message. This adjusted notification content is delivered to the user via email or internal channels.

[0156] As a concrete example, a prompt message for the generating AI model could be, "New data protection regulations have been implemented. Please create an explanatory message that will reassure users." In this way, the system reduces user stress regarding notifications of legal changes and enables more effective compliance.

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

[0158] Step 1:

[0159] The server automatically retrieves information on legal changes from online data sources and government APIs. It receives new data retrieval requests as input and accesses the APIs using Python or Java. As output, it retrieves raw data on legal changes and stores it in a database. This ensures the server always maintains the most up-to-date legal information.

[0160] Step 2:

[0161] The server applies natural language processing to the acquired legal information to analyze the changes. The input is the legal data acquired in step 1. A generative AI model (e.g., GPT or BERT) is used to analyze the text data and summarize the changes. The output extracts the summarized changes and their impact on related organizations. This makes it easier to understand the key points of the changes.

[0162] Step 3:

[0163] The device uses a camera and microphone to capture facial expressions and tone of voice in order to recognize the user's emotions. Input consists of the user's facial expression data and voice. A facial recognition library (such as OpenCV or dlib) is used to analyze the data and identify the user's emotional state as output. This data is sent to a server and used for notification adjustments.

[0164] Step 4:

[0165] The server adjusts the content of the legal change notification based on emotion recognition data. It uses emotion data received from the terminal as input. A generative AI model is used to automatically generate a notification message with a tone and expression appropriate to the user's emotions. The output is the adjusted notification message, which is then sent to the user.

[0166] Step 5:

[0167] The server provides users with coordinated notifications and countermeasures. The notification message generated in step 4 is used as input. It is delivered to users as output via a communication tool (email or messaging app). This allows users to understand the countermeasures with reduced emotional burden in response to the legal changes.

[0168] (Application Example 2)

[0169] 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".

[0170] In proposing countermeasures in response to legal changes, conventional systems often notify users without considering their feelings, which can cause stress and anxiety. This can lead to a decrease in motivation for effective legal compliance. In particular, when legal changes have a significant impact on an organization, notifications that disregard user feelings risk increasing their burden.

[0171] 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.

[0172] In this invention, the server includes means for acquiring information to monitor changes in laws and regulations, means for analyzing the acquired information and summarizing the changes in laws and regulations, means for notifying countermeasures to propose and notify countermeasures according to the evaluation results, and means for recognizing the user's emotions and adjusting the notification content according to their state. This enables appropriate notifications and suggestions that take the user's emotions into consideration, making it possible to respond effectively to changes in laws and regulations and reduce user stress.

[0173] "Information acquisition means" refers to a device or process that monitors changes in laws and regulations and automatically collects the latest legal information from online data sources or external APIs.

[0174] "Information analysis methods" refer to techniques that analyze acquired legal information using natural language processing technology and summarize the changes to those laws.

[0175] An "impact assessment tool" is a process that evaluates the impact of legal changes on an organization based on analyzed legal information.

[0176] A "response measure notification system" is a system that formulates necessary countermeasures for organizations and individuals based on evaluation results and notifies users of these measures.

[0177] A "history management system" is a device or process that databases the history of changes in laws and regulations and manages that information in a way that allows for quick and efficient retrieval.

[0178] An "emotion recognition system" is a system that detects a user's emotions in real time and adjusts notification content and suggestions according to that state.

[0179] This invention is constructed as a system for appropriately and effectively notifying users of changes in laws and regulations, by combining a server, a terminal, and an emotion recognition engine.

[0180] The server first uses online data sources and external APIs to collect information on changes in laws and regulations. After collection, an information analysis engine using natural language processing technology analyzes the acquired legal information and summarizes the changes and their impact.

[0181] Based on the analyzed information, the server evaluates the impact of legal changes on the organization via an impact assessment engine. Depending on the evaluation results, a response notification engine generates suggestions and notifies the user. During this notification, an emotion recognition engine detects the user's emotions through the camera and microphone on the device and generates notification content that takes those emotions into consideration. If the user is feeling stressed, the content and tone of the notification are adjusted, and softened as needed to reduce the user's anxiety.

[0182] Furthermore, changes in laws and regulations, along with their history, are stored in a database and managed in a way that allows for quick searching, making it possible to refer to past information as needed.

[0183] As a concrete example, suppose a small business using this system receives notification of a change in regulations, and the emotion recognition engine detects the business's anxiety. In this case, the server presents simple steps in calm language, such as "This is all you need to do to comply with the latest data protection regulations," thereby reducing the user's psychological burden.

[0184] An example of a prompt is: "Provide emotionally sensitive advice on how small business owners affected by legal changes can learn new security procedures while maintaining a sense of security."

[0185] Servers and terminals can utilize natural language processing libraries using Python, API servers using Node.js, and databases using MongoDB.

[0186] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0187] Step 1:

[0188] The server automatically retrieves information on legal changes from online data sources and external APIs. It takes raw data from each data source as input and generates detailed information on legal changes as output. This data is converted into a structured format and used in subsequent analysis steps. The server repeats this process periodically.

[0189] Step 2:

[0190] The server analyzes the acquired legal change information using natural language processing technology. The input is the detailed information of the legal change acquired in step 1, and the output is data summarizing the content and key points of the legal change. The server performs processes such as text tokenization, part-of-speech tagging, and semantic analysis to generate a summary of the legal change.

[0191] Step 3:

[0192] The server evaluates the impact on the organization based on the analysis results. The input is the output data from step 2, and the output is the impact analysis results. The server applies evaluation criteria and rules to assess the impact of legal changes on the organization's operations and business processes, and identifies specific impacts.

[0193] Step 4:

[0194] The server proposes countermeasures and prepares notifications based on the impact assessment results. The input is the impact analysis results from step 3, and the output is the proposed countermeasures and notification message. The server creates optimal countermeasure proposals using template generation and generation AI models.

[0195] Step 5:

[0196] The device recognizes the user's emotions in real time. The input is the user's facial expressions and voice data acquired through the camera and microphone, and the output is the user's emotional state. The device's emotion recognition engine evaluates the user's current emotions using image analysis and voice analysis.

[0197] Step 6:

[0198] The server adjusts and sends notification content according to the user's emotional state. The input is the emotional state obtained in step 5 and the notification message prepared in step 4, and the output is the adjusted notification. If the user is feeling anxious, the server softens the wording and tone of the notification to reduce the user's psychological burden.

[0199] Step 7:

[0200] Users receive notifications from their devices and act accordingly. Through coordinated notifications sent from the server, users can understand the appropriate countermeasures for new legal changes and take action based on them.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Second Embodiment]

[0205] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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".

[0217] The legal monitoring system of the present invention consists of multiple components in order to efficiently monitor changes in laws and regulations and to facilitate appropriate responses within corporate organizations.

[0218] The server plays a central role in monitoring legal changes, automatically obtaining the latest legal updates by regularly checking online data sources and government APIs. When the server detects new information, it registers it as a change event and records it in the database.

[0219] The acquired information is analyzed by information analysis tools on the server. The server uses natural language processing (NLP) technology to analyze legal documents and automatically generates key changes and summaries. This analysis process extracts the essential points of the changes and efficiently evaluates their impact on the organization.

[0220] The impact assessment is performed using algorithms on the server to identify which departments and business processes within the organization the change affects. The server analyzes the degree of impact and assesses the impact on the relevant departments to determine appropriate countermeasures.

[0221] Next, the server uses a response notification system to propose specific countermeasures based on the analysis and evaluation results and notify the relevant parties. This is done via email or an in-system dashboard, enabling users to take necessary actions quickly.

[0222] Furthermore, all legal changes and their analysis results are stored in a database through a history management system. This database is accessible via terminals, allowing users to easily search and refer to past legal change history.

[0223] For example, if new data protection legislation is enacted, the server will detect it and notify the data management department to implement the new protective measures. In this way, cumbersome legal research is eliminated, and users can immediately understand and act on what they need to do to comply with the law.

[0224] The following describes the processing flow.

[0225] Step 1:

[0226] The server periodically accesses online data sources and government APIs that provide legal information to obtain new legal changes and notifications. The server collects data using scraping techniques and API calls, and records any updates it detects.

[0227] Step 2:

[0228] The server analyzes the acquired information using natural language processing (NLP) techniques. The server analyzes the text of legal documents and automatically generates key changes and summaries. At this stage, the information is structured and prepared for use in subsequent processes.

[0229] Step 3:

[0230] The server uses the analysis results to evaluate the impact on the organization and operations. The server matches the analysis results with business process information and departmental information stored in the internal database to identify which departments and processes may be affected.

[0231] Step 4:

[0232] The server develops appropriate countermeasures based on the assessment results. The server lists the necessary response procedures for each affected department and creates notification messages to inform relevant parties within the company.

[0233] Step 5:

[0234] The server notifies users of the necessary actions. The server sends notifications to users in the relevant department via email or the system's dashboard, enabling them to take the required action quickly.

[0235] Step 6:

[0236] The server stores information on legal changes, analysis results, and impact assessment results in a database. Users can easily access past legal change history and response history by searching this database using their terminals.

[0237] (Example 1)

[0238] 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."

[0239] Changes in legislation are often complex, and it is crucial for organizations to quickly carry out processes of proper information gathering, analysis, impact assessment, and proposal of countermeasures. However, doing this manually is extremely time-consuming and resource-intensive. Furthermore, continuously tracking and making available the history of legal changes is not easy.

[0240] 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.

[0241] In this invention, the server includes an information acquisition means for automatically collecting information from a data source, an information analysis means that utilizes natural language processing technology to analyze the collected data and summarize the changes in laws and regulations, and an impact assessment means that identifies and evaluates the impact on each department and operation within the organization based on the analysis results. This enables rapid and efficient monitoring and response to changes in laws and regulations.

[0242] "Information acquisition means" refers to processes or devices that automatically collect relevant information from data sources.

[0243] "Information analysis means" refers to the process or apparatus of analyzing collected data and summarizing the changes in laws and regulations using natural language processing technology.

[0244] "Impact assessment tools" refer to processes and devices used to identify and evaluate the impact on various departments and operations within an organization based on analysis results.

[0245] A "means of notifying about countermeasures" refers to a system or device that proposes necessary actions based on an evaluation and notifies relevant parties.

[0246] "History management means" refers to a process or device for accumulating changes in laws and regulations and their analysis results, and enabling searching and referencing them via terminals.

[0247] "Natural language processing technology" refers to the technology used to enable computers to understand and process human language.

[0248] This invention aims to provide a legal monitoring system that enables organizations to quickly and efficiently monitor changes in laws and regulations and to facilitate appropriate responses. This system primarily consists of server-centric functions and operates as follows:

[0249] The server obtains information on changes in laws and regulations via web services and online databases. This is achieved through various means of information retrieval, such as periodically scanning data sources using APIs to obtain the latest legal information.

[0250] Furthermore, the server processes the acquired legal information using information analysis tools. This analysis utilizes natural language processing technologies such as SpaCy and NLTK libraries. This makes it possible to tokenize changes in the laws and extract and summarize the main points.

[0251] Furthermore, the server uses impact assessment tools to evaluate the analyzed data and determine which departments and business processes within the organization are affected. This involves using analytical algorithms to compare the changes with the organization's business profile.

[0252] Based on analysis and impact assessment, the server notifies stakeholders of the necessary countermeasures through its countermeasure notification system. This notification is provided to users via email or the system's dashboard, allowing them to take necessary actions quickly.

[0253] The terminal is used to allow users to access past legal change information through history management mechanisms and search for necessary data. This makes it easier for users to manage their access to past information.

[0254] For example, if new data protection legislation is enacted, the server will detect it. The server will then notify the data management department of the necessary countermeasures, including changes to new processes and procedures. This allows the organization to comply with the new legislation quickly.

[0255] The following prompt could be used as input for the generative AI model: "Please explain the main points of the newly enacted data protection law and the corresponding actions organizations should take."

[0256] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0257] Step 1:

[0258] The server accesses data sources using information acquisition methods based on a pre-configured schedule, automatically retrieving the latest legal change information from online databases and APIs. The input is connection information such as URLs and API keys, and the output is the latest legal change information. Specifically, the server periodically executes API calls and retrieves response data.

[0259] Step 2:

[0260] The server analyzes legal data acquired using information analysis tools. The input is the legal change information acquired in step 1. The server uses natural language processing technology (e.g., SpaCy or NLTK) to tokenize and analyze the legal document and extract important changes. The output is a summary of the main changes to the law. Specifically, the data is tokenized, noun phrases and verb phrases are extracted, and the important parts of the law are summarized.

[0261] Step 3:

[0262] The server uses impact assessment tools to evaluate the impact within the organization based on the analysis results. The input is the changes in legislation summarized in step 2. Based on the organization's business profile, the server identifies which departments and business processes will be affected. The output is the affected areas and the degree of impact. Specifically, an evaluation algorithm is used to identify the relevant departments within the organization.

[0263] Step 4:

[0264] The server uses a response notification mechanism to notify relevant parties of the necessary countermeasures. The input is the impact assessment results identified in step 3. The output is notification information sent to relevant departments and personnel within the organization. Specifically, the server sets up notification messages to be automatically generated by the server and immediately distributes them to relevant parties via email or dashboard.

[0265] Step 5:

[0266] Using a terminal, users access the history of legal changes through a history management system. Input consists of keywords and dates / times for the user's search. Output is detailed data of past legal change history. Specifically, the user logs into the system from the terminal, searches the database using a GUI, and retrieves the necessary information.

[0267] (Application Example 1)

[0268] 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."

[0269] Modern businesses are required to quickly and accurately grasp frequently changing laws and regulations and take appropriate countermeasures based on them. However, because legal information changes are so diverse, manual management is difficult, leading to risks of delayed compliance and incorrect responses. Furthermore, there are insufficient means to quickly assess the impact of legal changes on specific departments within an organization and to effectively notify those departments. As a result, there is a need to minimize the impact on legal compliance and security measures across the entire organization.

[0270] 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.

[0271] In this invention, the server includes information acquisition means for monitoring information on changes in laws and regulations, information analysis means for analyzing the acquired information and generating a summary, and impact assessment means for evaluating the impact based on the analysis results. This makes it possible to grasp changes in laws and regulations in real time and automatically provide accurate notifications to the relevant departments.

[0272] "Changes to laws and regulations" refer to changes in the content of laws and regulations enacted or amended by the government or administrative agencies.

[0273] "Information acquisition means" refers to a function that automatically acquires the latest information on legal changes from online data sources or APIs.

[0274] "Information analysis means" refers to technology that analyzes acquired legal information and generates key changes and summaries of it.

[0275] "Impact assessment tools" refer to the function that evaluates which departments or business processes within an organization will be affected by the analyzed legal changes.

[0276] "Means of notifying countermeasures" refers to the means of communicating necessary countermeasures to relevant departments based on changes in laws and regulations.

[0277] "History management means" refers to a function that stores the history of legal changes and analysis results in a database and manages them in a way that allows them to be referenced later.

[0278] An "application that notifies machines and devices in real time" refers to software that immediately notifies users of changes in laws and regulations on their devices, making relevant information accessible.

[0279] "Machine learning" refers to the technology that allows computers to learn from past data and patterns to predict or classify future data and events.

[0280] A "security policy" refers to a set of guidelines and procedures designed to help an organization protect its information and comply with laws and regulations.

[0281] To implement this invention, a system is constructed in which a server plays a central role. The server is equipped with means for automatically obtaining information on changes in laws and regulations from online data sources and government APIs. In this process, cloud services such as AWS and Google Cloud are utilized for data acquisition and storage. The acquired legal information is analyzed using NLP (Natural Language Processing) technology to generate a summary of the changes. Open-source libraries such as SpaCy and NLTK are used for this analysis.

[0282] The server also includes an impact assessment tool that uses machine learning techniques to evaluate the impact of legal changes on the organization's security policies. The results of this assessment are sent as push notifications to user terminals in relevant departments via notification services such as Firebase Cloud Messaging. Users who receive the notification can then check the content on their smartphones or desktop devices and take necessary countermeasures quickly.

[0283] For example, when new data protection regulations are announced, the server immediately detects them, conducts an impact analysis, and notifies users of the necessary actions. Through this process, users can accurately understand the situation and ensure compliance with laws and regulations and improve the security of the organization.

[0284] To obtain specific insights into the impact of new laws on an organization using a generative AI model, the following prompt sentences can be used.

[0285] "New data protection laws have been announced. What changes will affect the company's data management?"

[0286] The flow of the specific process in Application Example 1 will be described using Figure 12.

[0287] Step 1:

[0288] The server regularly accesses online data sources and government APIs to obtain information on legal changes. The input is an API request, and the output is the raw data of the laws. The server saves this in a database. This process is carried out as an automatically scheduled task.

[0289] Step 2:

[0290] The server analyzes the obtained legal data using natural language processing (NLP) techniques. The input is the raw data of the laws obtained in Step 1, and the output is summary information on legal changes. The server extracts characteristic keywords and phrases and summarizes the important points of change. The SpaCy library is often used in this process.

[0291] Step 3:

[0292] The server assesses the impact on each department of an organization based on a summary of the legal changes. The input is the summary information generated in step 2, and the output is a list of affected departments and the degree of impact. A machine learning model is used for the impact assessment, making decisions based on past data and impact patterns.

[0293] Step 4:

[0294] The server develops necessary countermeasures based on the impact assessment results and notifies the user. The input is the impact assessment results from step 3, and the output is a push notification message. Firebase Cloud Messaging is used to send the notification directly to the user's device.

[0295] Step 5:

[0296] Users check notifications on their devices and refer to recommended actions to take the necessary steps. The input is the notification message, and the output is the actual status of the implemented actions. This ensures rapid compliance with regulations and appropriate measures within the organization.

[0297] Step 6:

[0298] The server stores all legal change information, its analysis results, and a history of countermeasures in a database. Input is the data obtained in each step so far, and output is searchable historical data. Users can refer to past history as needed.

[0299] The above steps enable a swift and appropriate response to changes in legislation. Downstream users can use prompts for the generated AI model, such as, "New data protection legislation has been announced. What changes will affect our company's data management?", to conduct a detailed impact analysis.

[0300] 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.

[0301] This invention combines a system for monitoring and analyzing changes in laws and regulations with an emotion engine that recognizes user emotions, thereby enabling the notification of more appropriate and effective countermeasures. Specific embodiments for carrying out this invention are shown below.

[0302] The server, like traditional legal monitoring systems, periodically retrieves information on changes in laws and regulations using online data sources and government APIs. This ensures that the server always maintains up-to-date legal information and detects changes in real time.

[0303] Once legal information is retrieved, the server uses natural language processing technology to analyze the information and identify the key points of the changes and their impact. This allows for an assessment of the impact of legal changes on the organization and the development of necessary countermeasures.

[0304] The emotion engine, a key feature of this invention, has the ability to recognize the user's emotions in real time. This engine analyzes the user's facial expressions and tone of voice through a camera and microphone, and identifies emotions from them. Specifically, the terminal captures the user's facial expressions, and the server analyzes that data to determine the user's emotional state.

[0305] Information obtained from the emotion engine is reflected in notifications of legal changes and suggestions for countermeasures. For example, if the server detects that a user is experiencing stress, it adjusts the content of the notification to be softer and condenses the information provided to the bare minimum, thereby reducing the user's burden.

[0306] For example, if a user is concerned about a change after receiving notification of new data protection regulations, the system will soften the tone of the notification and present solutions in reassuring language.

[0307] With the above configuration, not only can the user be simply informed of the legal changes, but communication considering the user's feelings can be enabled, and more effective compliance with the law can be supported.

[0308] The process flow will be described below.

[0309] Step 1:

[0310] The server regularly accesses online data sources and government APIs that are the sources of legal information to obtain new legal change information and notices. When the server detects new information, it records it in the database and registers it as a legal document to be analyzed.

[0311] Step 2:

[0312] The server analyzes the legal information it has obtained using natural language processing (NLP) technology. The server analyzes the text of the legal document, extracts important change points, and automatically generates a summary. Through this analysis, the specific impact on the organization and business becomes clear.

[0313] Step 3:

[0314] The server formulates necessary countermeasures regarding the legal changes upon receiving the results of the impact assessment. The server lists up countermeasures for each department affected and prepares these as notifications.

[0315] Step 4:

[0316] The user's terminal utilizes the camera and microphone to record the user's current emotional state. The terminal captures the user's expression and collects digital data for analyzing the voice tone.

[0317] Step 5:

[0318] The server uses an emotion engine to analyze data sent from the terminal and identify the user's emotions. For example, it can determine whether the user is feeling at ease or stressed based on changes in voice tone and facial expressions.

[0319] Step 6:

[0320] The server adjusts the tone and content of notifications based on the user's emotional state. If the server determines that the user is stressed, it will make the notifications gentler and more concise to avoid burdening them.

[0321] Step 7:

[0322] The server sends a tailored notification to the user. The user receives the notification on their device and reviews the suggested course of action, which is presented in an emotionally sensitive manner.

[0323] Step 8:

[0324] The server stores information on legal changes, analysis results, and sentiment analysis results in a database. By using a terminal, users can easily access past legal change history, response history, and sentiment-based notification adjustment history by searching this database.

[0325] (Example 2)

[0326] 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".

[0327] Changes in laws and regulations are crucial information for organizations, and it is essential to grasp them in a timely manner and take appropriate action. However, conventional legal monitoring systems unilaterally notify users of information without considering their feelings, which can cause unnecessary stress to recipients. Furthermore, there is a challenge in providing flexible responses that take into account the emotional state of the recipients.

[0328] 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.

[0329] In this invention, the server includes information acquisition means, information analysis means, impact assessment means, emotion recognition means, and notification adjustment means. This makes it possible to adjust the content of legal change notifications to take into account the recipient's emotional state, thereby supporting more effective and user-friendly legal compliance.

[0330] "Information acquisition means" refers to an element that has the function of automatically acquiring information on changes in laws and regulations from a data set.

[0331] An "information analysis tool" is an element that has the function of analyzing acquired legal information using natural language processing technology and summarizing the changes.

[0332] An "impact assessment tool" is an element that has the function of evaluating the impact on an organization based on the analysis results.

[0333] A "means for notifying countermeasures" refers to an element that has the function of proposing countermeasures based on evaluation results and notifying the user of those countermeasures.

[0334] A "history management system" is an element that has the function of creating a database of the history of legal changes and managing them in a searchable format.

[0335] An "emotion recognition tool" is an element that has the function of recognizing and analyzing the user's emotions in real time.

[0336] A "notification adjustment mechanism" is an element that has the function of adjusting notification content based on information obtained from emotion recognition mechanisms and presenting it in a form optimized for the user.

[0337] To implement this invention, it is necessary to construct a system that monitors and notifies of changes in laws and regulations, and recognizes the emotions of users.

[0338] The server is responsible for automatically collecting information on changes in laws and regulations by utilizing online data sources and government APIs. This involves using scripts written in programming languages ​​such as Python and Java to periodically retrieve data via APIs. The retrieved legal information is then analyzed on the server using natural language processing (NLP) techniques. This analysis utilizes generative AI models such as OpenAI's GPT and Google's BERT to extract summaries of the changes and their impact.

[0339] The device plays a crucial role in recognizing the user's emotions in real time. Specifically, it uses a camera and microphone to capture the user's facial expressions and tone of voice, and sends this data to a server. The server analyzes this data using emotion recognition tools to determine the user's emotional state. Face recognition libraries such as OpenCV and dlib are useful in this process.

[0340] Based on the results of emotion recognition, the notification content is adjusted. The server utilizes a generative AI model to automatically generate notification content with appropriate language according to the user's emotional state. For example, in a notification regarding new data protection regulations, if the user is feeling anxious, the notification tone is softened and reassuring language is used to create the message. This adjusted notification content is delivered to the user via email or internal channels.

[0341] As a concrete example, a prompt message for the generating AI model could be, "New data protection regulations have been implemented. Please create an explanatory message that will reassure users." In this way, the system reduces user stress regarding notifications of legal changes and enables more effective compliance.

[0342] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0343] Step 1:

[0344] The server automatically retrieves information on legal changes from online data sources and government APIs. It receives new data retrieval requests as input and accesses the APIs using Python or Java. As output, it retrieves raw data on legal changes and stores it in a database. This ensures the server always maintains the most up-to-date legal information.

[0345] Step 2:

[0346] The server applies natural language processing to the acquired legal information to analyze the changes. The input is the legal data acquired in step 1. A generative AI model (e.g., GPT or BERT) is used to analyze the text data and summarize the changes. The output extracts the summarized changes and their impact on related organizations. This makes it easier to understand the key points of the changes.

[0347] Step 3:

[0348] The device uses a camera and microphone to capture facial expressions and tone of voice in order to recognize the user's emotions. Input consists of the user's facial expression data and voice. A facial recognition library (such as OpenCV or dlib) is used to analyze the data and identify the user's emotional state as output. This data is sent to a server and used for notification adjustments.

[0349] Step 4:

[0350] The server adjusts the content of the legal change notification based on emotion recognition data. It uses emotion data received from the terminal as input. A generative AI model is used to automatically generate a notification message with a tone and expression appropriate to the user's emotions. The output is the adjusted notification message, which is then sent to the user.

[0351] Step 5:

[0352] The server provides users with coordinated notifications and countermeasures. The notification message generated in step 4 is used as input. It is delivered to users as output via a communication tool (email or messaging app). This allows users to understand the countermeasures with reduced emotional burden in response to the legal changes.

[0353] (Application Example 2)

[0354] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0355] In proposing countermeasures in response to legal changes, conventional systems often notify users without considering their feelings, which can cause stress and anxiety. This can lead to a decrease in motivation for effective legal compliance. In particular, when legal changes have a significant impact on an organization, notifications that disregard user feelings risk increasing their burden.

[0356] 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.

[0357] In this invention, the server includes means for acquiring information to monitor changes in laws and regulations, means for analyzing the acquired information and summarizing the changes in laws and regulations, means for notifying countermeasures to propose and notify countermeasures according to the evaluation results, and means for recognizing the user's emotions and adjusting the notification content according to their state. This enables appropriate notifications and suggestions that take the user's emotions into consideration, making it possible to respond effectively to changes in laws and regulations and reduce user stress.

[0358] "Information acquisition means" refers to a device or process that monitors changes in laws and regulations and automatically collects the latest legal information from online data sources or external APIs.

[0359] "Information analysis methods" refer to techniques that analyze acquired legal information using natural language processing technology and summarize the changes to those laws.

[0360] An "impact assessment tool" is a process that evaluates the impact of legal changes on an organization based on analyzed legal information.

[0361] A "response measure notification system" is a system that formulates necessary countermeasures for organizations and individuals based on evaluation results and notifies users of these measures.

[0362] A "history management system" is a device or process that databases the history of changes in laws and regulations and manages that information in a way that allows for quick and efficient retrieval.

[0363] An "emotion recognition system" is a system that detects a user's emotions in real time and adjusts notification content and suggestions according to that state.

[0364] This invention is constructed as a system for appropriately and effectively notifying users of changes in laws and regulations, by combining a server, a terminal, and an emotion recognition engine.

[0365] The server first uses online data sources and external APIs to collect information on changes in laws and regulations. After collection, an information analysis engine using natural language processing technology analyzes the acquired legal information and summarizes the changes and their impact.

[0366] Based on the analyzed information, the server evaluates the impact of legal changes on the organization via an impact assessment engine. Depending on the evaluation results, a response notification engine generates suggestions and notifies the user. During this notification, an emotion recognition engine detects the user's emotions through the camera and microphone on the device and generates notification content that takes those emotions into consideration. If the user is feeling stressed, the content and tone of the notification are adjusted, and softened as needed to reduce the user's anxiety.

[0367] Furthermore, changes in laws and regulations, along with their history, are stored in a database and managed in a way that allows for quick searching, making it possible to refer to past information as needed.

[0368] As a concrete example, suppose a small business using this system receives notification of a change in regulations, and the emotion recognition engine detects the business's anxiety. In this case, the server presents simple steps in calm language, such as "This is all you need to do to comply with the latest data protection regulations," thereby reducing the user's psychological burden.

[0369] An example of a prompt is: "Provide emotionally sensitive advice on how small business owners affected by legal changes can learn new security procedures while maintaining a sense of security."

[0370] Servers and terminals can utilize natural language processing libraries using Python, API servers using Node.js, and databases using MongoDB.

[0371] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0372] Step 1:

[0373] The server automatically retrieves information on legal changes from online data sources and external APIs. It takes raw data from each data source as input and generates detailed information on legal changes as output. This data is converted into a structured format and used in subsequent analysis steps. The server repeats this process periodically.

[0374] Step 2:

[0375] The server analyzes the acquired legal change information using natural language processing technology. The input is the detailed information of the legal change acquired in step 1, and the output is data summarizing the content and key points of the legal change. The server performs processes such as text tokenization, part-of-speech tagging, and semantic analysis to generate a summary of the legal change.

[0376] Step 3:

[0377] The server evaluates the impact on the organization based on the analysis results. The input is the output data from step 2, and the output is the impact analysis results. The server applies evaluation criteria and rules to assess the impact of legal changes on the organization's operations and business processes, and identifies specific impacts.

[0378] Step 4:

[0379] The server proposes countermeasures and prepares notifications based on the impact assessment results. The input is the impact analysis results from step 3, and the output is the proposed countermeasures and notification message. The server creates optimal countermeasure proposals using template generation and generation AI models.

[0380] Step 5:

[0381] The device recognizes the user's emotions in real time. The input is the user's facial expressions and voice data acquired through the camera and microphone, and the output is the user's emotional state. The device's emotion recognition engine evaluates the user's current emotions using image analysis and voice analysis.

[0382] Step 6:

[0383] The server adjusts and sends notification content according to the user's emotional state. The input is the emotional state obtained in step 5 and the notification message prepared in step 4, and the output is the adjusted notification. If the user is feeling anxious, the server softens the wording and tone of the notification to reduce the user's psychological burden.

[0384] Step 7:

[0385] Users receive notifications from their devices and act accordingly. Through coordinated notifications sent from the server, users can understand the appropriate countermeasures for new legal changes and take action based on them.

[0386] 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.

[0387] 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.

[0388] 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.

[0389] [Third Embodiment]

[0390] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0391] 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.

[0392] 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).

[0393] 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.

[0394] 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.

[0395] 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).

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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".

[0402] The legal monitoring system of the present invention consists of multiple components in order to efficiently monitor changes in laws and regulations and to facilitate appropriate responses within corporate organizations.

[0403] The server plays a central role in monitoring legal changes, automatically obtaining the latest legal updates by regularly checking online data sources and government APIs. When the server detects new information, it registers it as a change event and records it in the database.

[0404] The acquired information is analyzed by information analysis tools on the server. The server uses natural language processing (NLP) technology to analyze legal documents and automatically generates key changes and summaries. This analysis process extracts the essential points of the changes and efficiently evaluates their impact on the organization.

[0405] The impact assessment is performed using algorithms on the server to identify which departments and business processes within the organization the change affects. The server analyzes the degree of impact and assesses the impact on the relevant departments to determine appropriate countermeasures.

[0406] Next, the server uses a response notification system to propose specific countermeasures based on the analysis and evaluation results and notify the relevant parties. This is done via email or an in-system dashboard, enabling users to take necessary actions quickly.

[0407] Furthermore, all legal changes and their analysis results are stored in a database through a history management system. This database is accessible via terminals, allowing users to easily search and refer to past legal change history.

[0408] For example, if new data protection legislation is enacted, the server will detect it and notify the data management department to implement the new protective measures. In this way, cumbersome legal research is eliminated, and users can immediately understand and act on what they need to do to comply with the law.

[0409] The following describes the processing flow.

[0410] Step 1:

[0411] The server periodically accesses online data sources and government APIs that provide legal information to obtain new legal changes and notifications. The server collects data using scraping techniques and API calls, and records any updates it detects.

[0412] Step 2:

[0413] The server analyzes the acquired information using natural language processing (NLP) techniques. The server analyzes the text of legal documents and automatically generates key changes and summaries. At this stage, the information is structured and prepared for use in subsequent processes.

[0414] Step 3:

[0415] The server uses the analysis results to evaluate the impact on the organization and operations. The server matches the analysis results with business process information and departmental information stored in the internal database to identify which departments and processes may be affected.

[0416] Step 4:

[0417] The server develops appropriate countermeasures based on the assessment results. The server lists the necessary response procedures for each affected department and creates notification messages to inform relevant parties within the company.

[0418] Step 5:

[0419] The server notifies users of the necessary actions. The server sends notifications to users in the relevant department via email or the system's dashboard, enabling them to take the required action quickly.

[0420] Step 6:

[0421] The server stores information on legal changes, analysis results, and impact assessment results in a database. Users can easily access past legal change history and response history by searching this database using their terminals.

[0422] (Example 1)

[0423] 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."

[0424] Changes in legislation are often complex, and it is crucial for organizations to quickly carry out processes of proper information gathering, analysis, impact assessment, and proposal of countermeasures. However, doing this manually is extremely time-consuming and resource-intensive. Furthermore, continuously tracking and making available the history of legal changes is not easy.

[0425] 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.

[0426] In this invention, the server includes an information acquisition means for automatically collecting information from a data source, an information analysis means that utilizes natural language processing technology to analyze the collected data and summarize the changes in laws and regulations, and an impact assessment means that identifies and evaluates the impact on each department and operation within the organization based on the analysis results. This enables rapid and efficient monitoring and response to changes in laws and regulations.

[0427] "Information acquisition means" refers to processes or devices that automatically collect relevant information from data sources.

[0428] "Information analysis means" refers to the process or apparatus of analyzing collected data and summarizing the changes in laws and regulations using natural language processing technology.

[0429] "Impact assessment tools" refer to processes and devices used to identify and evaluate the impact on various departments and operations within an organization based on analysis results.

[0430] A "means of notifying about countermeasures" refers to a system or device that proposes necessary actions based on an evaluation and notifies relevant parties.

[0431] "History management means" refers to a process or device for accumulating changes in laws and regulations and their analysis results, and enabling searching and referencing them via terminals.

[0432] "Natural language processing technology" refers to the technology used to enable computers to understand and process human language.

[0433] This invention aims to provide a legal monitoring system that enables organizations to quickly and efficiently monitor changes in laws and regulations and to facilitate appropriate responses. This system primarily consists of server-centric functions and operates as follows:

[0434] The server obtains information on changes in laws and regulations via web services and online databases. This is achieved through various means of information retrieval, such as periodically scanning data sources using APIs to obtain the latest legal information.

[0435] Furthermore, the server processes the acquired legal information using information analysis tools. This analysis utilizes natural language processing technologies such as SpaCy and NLTK libraries. This makes it possible to tokenize changes in the laws and extract and summarize the main points.

[0436] Furthermore, the server uses impact assessment tools to evaluate the analyzed data and determine which departments and business processes within the organization are affected. This involves using analytical algorithms to compare the changes with the organization's business profile.

[0437] Based on analysis and impact assessment, the server notifies stakeholders of the necessary countermeasures through its countermeasure notification system. This notification is provided to users via email or the system's dashboard, allowing them to take necessary actions quickly.

[0438] The terminal is used to allow users to access past legal change information through history management mechanisms and search for necessary data. This makes it easier for users to manage their access to past information.

[0439] For example, if new data protection legislation is enacted, the server will detect it. The server will then notify the data management department of the necessary countermeasures, including changes to new processes and procedures. This allows the organization to comply with the new legislation quickly.

[0440] The following prompt could be used as input for the generative AI model: "Please explain the main points of the newly enacted data protection law and the corresponding actions organizations should take."

[0441] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0442] Step 1:

[0443] The server accesses data sources using information acquisition methods based on a pre-configured schedule, automatically retrieving the latest legal change information from online databases and APIs. The input is connection information such as URLs and API keys, and the output is the latest legal change information. Specifically, the server periodically executes API calls and retrieves response data.

[0444] Step 2:

[0445] The server analyzes legal data acquired using information analysis tools. The input is the legal change information acquired in step 1. The server uses natural language processing technology (e.g., SpaCy or NLTK) to tokenize and analyze the legal document and extract important changes. The output is a summary of the main changes to the law. Specifically, the data is tokenized, noun phrases and verb phrases are extracted, and the important parts of the law are summarized.

[0446] Step 3:

[0447] The server uses impact assessment tools to evaluate the impact within the organization based on the analysis results. The input is the changes in legislation summarized in step 2. Based on the organization's business profile, the server identifies which departments and business processes will be affected. The output is the affected areas and the degree of impact. Specifically, an evaluation algorithm is used to identify the relevant departments within the organization.

[0448] Step 4:

[0449] The server uses a response notification mechanism to notify relevant parties of the necessary countermeasures. The input is the impact assessment results identified in step 3. The output is notification information sent to relevant departments and personnel within the organization. Specifically, the server sets up notification messages to be automatically generated by the server and immediately distributes them to relevant parties via email or dashboard.

[0450] Step 5:

[0451] Using a terminal, users access the history of legal changes through a history management system. Input consists of keywords and dates / times for the user's search. Output is detailed data of past legal change history. Specifically, the user logs into the system from the terminal, searches the database using a GUI, and retrieves the necessary information.

[0452] (Application Example 1)

[0453] 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."

[0454] Modern businesses are required to quickly and accurately grasp frequently changing laws and regulations and take appropriate countermeasures based on them. However, because legal information changes are so diverse, manual management is difficult, leading to risks of delayed compliance and incorrect responses. Furthermore, there are insufficient means to quickly assess the impact of legal changes on specific departments within an organization and to effectively notify those departments. As a result, there is a need to minimize the impact on legal compliance and security measures across the entire organization.

[0455] 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.

[0456] In this invention, the server includes information acquisition means for monitoring information on changes in laws and regulations, information analysis means for analyzing the acquired information and generating a summary, and impact assessment means for evaluating the impact based on the analysis results. This makes it possible to grasp changes in laws and regulations in real time and automatically provide accurate notifications to the relevant departments.

[0457] "Changes to laws and regulations" refer to changes in the content of laws and regulations enacted or amended by the government or administrative agencies.

[0458] "Information acquisition means" refers to a function that automatically acquires the latest information on legal changes from online data sources or APIs.

[0459] "Information analysis means" refers to technology that analyzes acquired legal information and generates key changes and summaries of it.

[0460] "Impact assessment tools" refer to the function that evaluates which departments or business processes within an organization will be affected by the analyzed legal changes.

[0461] "Means of notifying countermeasures" refers to the means of communicating necessary countermeasures to relevant departments based on changes in laws and regulations.

[0462] "History management means" refers to a function that stores the history of legal changes and analysis results in a database and manages them in a way that allows them to be referenced later.

[0463] An "application that notifies machines and devices in real time" refers to software that immediately notifies users of changes in laws and regulations on their devices, making relevant information accessible.

[0464] "Machine learning" refers to the technology that allows computers to learn from past data and patterns to predict or classify future data and events.

[0465] A "security policy" refers to a set of guidelines and procedures designed to help an organization protect its information and comply with laws and regulations.

[0466] To implement this invention, a system is constructed in which a server plays a central role. The server is equipped with means for automatically obtaining information on changes in laws and regulations from online data sources and government APIs. In this process, cloud services such as AWS and Google Cloud are utilized for data acquisition and storage. The acquired legal information is analyzed using NLP (Natural Language Processing) technology to generate a summary of the changes. Open-source libraries such as SpaCy and NLTK are used for this analysis.

[0467] The server also includes an impact assessment tool that uses machine learning techniques to evaluate the impact of legal changes on the organization's security policies. The results of this assessment are sent as push notifications to user terminals in relevant departments via notification services such as Firebase Cloud Messaging. Users who receive the notification can then check the content on their smartphones or desktop devices and take necessary countermeasures quickly.

[0468] For example, if new data protection regulations are announced, the server will immediately detect them, perform an impact analysis, and notify users of the necessary actions. This process allows users to accurately understand the situation and ensure legal compliance while improving organizational security.

[0469] To gain specific insights into the impact of new legislation on an organization using a generative AI model, the following prompt statements can be used:

[0470] "New data protection legislation has been announced. What changes will this have on how companies manage their data?"

[0471] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0472] Step 1:

[0473] The server periodically accesses online data sources and government APIs to retrieve information on changes in legislation. The input is API requests, and the output is raw data of the legislation. The server stores this data in a database. This process is performed as an automatically scheduled task.

[0474] Step 2:

[0475] The server analyzes the acquired legal data using natural language processing (NLP) techniques. The input is the raw legal data obtained in step 1, and the output is a summary of legal changes. The server extracts characteristic keywords and phrases and summarizes the important changes. The SpaCy library is often used for this process.

[0476] Step 3:

[0477] The server assesses the impact on each department of an organization based on a summary of the legal changes. The input is the summary information generated in step 2, and the output is a list of affected departments and the degree of impact. A machine learning model is used for the impact assessment, making decisions based on past data and impact patterns.

[0478] Step 4:

[0479] The server develops necessary countermeasures based on the impact assessment results and notifies the user. The input is the impact assessment results from step 3, and the output is a push notification message. Firebase Cloud Messaging is used to send the notification directly to the user's device.

[0480] Step 5:

[0481] Users check notifications on their devices and refer to recommended actions to take the necessary steps. The input is the notification message, and the output is the actual status of the implemented actions. This ensures rapid compliance with regulations and appropriate measures within the organization.

[0482] Step 6:

[0483] The server stores all legal change information, its analysis results, and a history of countermeasures in a database. Input is the data obtained in each step so far, and output is searchable historical data. Users can refer to past history as needed.

[0484] The above steps enable a swift and appropriate response to changes in legislation. Downstream users can use prompts for the generated AI model, such as, "New data protection legislation has been announced. What changes will affect our company's data management?", to conduct a detailed impact analysis.

[0485] 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.

[0486] This invention combines a system for monitoring and analyzing changes in laws and regulations with an emotion engine that recognizes user emotions, thereby enabling the notification of more appropriate and effective countermeasures. Specific embodiments for carrying out this invention are shown below.

[0487] The server, like traditional legal monitoring systems, periodically retrieves information on changes in laws and regulations using online data sources and government APIs. This ensures that the server always maintains up-to-date legal information and detects changes in real time.

[0488] Once legal information is retrieved, the server uses natural language processing technology to analyze the information and identify the key points of the changes and their impact. This allows for an assessment of the impact of legal changes on the organization and the development of necessary countermeasures.

[0489] The emotion engine, a key feature of this invention, has the ability to recognize the user's emotions in real time. This engine analyzes the user's facial expressions and tone of voice through a camera and microphone, and identifies emotions from them. Specifically, the terminal captures the user's facial expressions, and the server analyzes that data to determine the user's emotional state.

[0490] Information obtained from the emotion engine is reflected in notifications of legal changes and suggestions for countermeasures. For example, if the server detects that a user is experiencing stress, it adjusts the content of the notification to be softer and condenses the information provided to the bare minimum, thereby reducing the user's burden.

[0491] For example, if a user is concerned about a change after receiving notification of new data protection regulations, the system will soften the tone of the notification and present solutions in reassuring language.

[0492] This configuration allows for communication that not only informs users of legal changes but also takes their feelings into consideration, thereby supporting more effective legal compliance.

[0493] The following describes the processing flow.

[0494] Step 1:

[0495] The server periodically accesses online data sources and government APIs that provide legal information to obtain new legal changes and notifications. When the server detects new information, it records it in the database and registers it as a legal document to be analyzed.

[0496] Step 2:

[0497] The server analyzes acquired legal information using natural language processing (NLP) technology. The server analyzes the text of legal documents, extracts key changes, and automatically generates summaries. This analysis clarifies the specific impact on organizations and operations.

[0498] Step 3:

[0499] Based on the impact assessment results, the server will formulate necessary countermeasures regarding the legal changes. The server will list the countermeasures for each affected department and prepare them as notifications.

[0500] Step 4:

[0501] The user's device utilizes its camera and microphone to record the user's current emotional state. The device captures the user's facial expressions and collects digital data for analyzing their voice tone.

[0502] Step 5:

[0503] The server uses an emotion engine to analyze data sent from the terminal and identify the user's emotions. For example, it can determine whether the user is feeling at ease or stressed based on changes in voice tone and facial expressions.

[0504] Step 6:

[0505] The server adjusts the tone and content of notifications based on the user's emotional state. If the server determines that the user is stressed, it will make the notifications gentler and more concise to avoid burdening them.

[0506] Step 7:

[0507] The server sends a tailored notification to the user. The user receives the notification on their device and reviews the suggested course of action, which is presented in an emotionally sensitive manner.

[0508] Step 8:

[0509] The server stores information on legal changes, analysis results, and sentiment analysis results in a database. By using a terminal, users can easily access past legal change history, response history, and sentiment-based notification adjustment history by searching this database.

[0510] (Example 2)

[0511] 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."

[0512] Changes in laws and regulations are crucial information for organizations, and it is essential to grasp them in a timely manner and take appropriate action. However, conventional legal monitoring systems unilaterally notify users of information without considering their feelings, which can cause unnecessary stress to recipients. Furthermore, there is a challenge in providing flexible responses that take into account the emotional state of the recipients.

[0513] 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.

[0514] In this invention, the server includes information acquisition means, information analysis means, impact assessment means, emotion recognition means, and notification adjustment means. This makes it possible to adjust the content of legal change notifications to take into account the recipient's emotional state, thereby supporting more effective and user-friendly legal compliance.

[0515] "Information acquisition means" refers to an element that has the function of automatically acquiring information on changes in laws and regulations from a data set.

[0516] An "information analysis tool" is an element that has the function of analyzing acquired legal information using natural language processing technology and summarizing the changes.

[0517] An "impact assessment tool" is an element that has the function of evaluating the impact on an organization based on the analysis results.

[0518] A "means for notifying countermeasures" refers to an element that has the function of proposing countermeasures based on evaluation results and notifying the user of those countermeasures.

[0519] A "history management system" is an element that has the function of creating a database of the history of legal changes and managing them in a searchable format.

[0520] An "emotion recognition tool" is an element that has the function of recognizing and analyzing the user's emotions in real time.

[0521] A "notification adjustment mechanism" is an element that has the function of adjusting notification content based on information obtained from emotion recognition mechanisms and presenting it in a form optimized for the user.

[0522] To implement this invention, it is necessary to construct a system that monitors and notifies of changes in laws and regulations, and recognizes the emotions of users.

[0523] The server is responsible for automatically collecting information on changes in laws and regulations by utilizing online data sources and government APIs. This involves using scripts written in programming languages ​​such as Python and Java to periodically retrieve data via APIs. The retrieved legal information is then analyzed on the server using natural language processing (NLP) techniques. This analysis utilizes generative AI models such as OpenAI's GPT and Google's BERT to extract summaries of the changes and their impact.

[0524] The device plays a crucial role in recognizing the user's emotions in real time. Specifically, it uses a camera and microphone to capture the user's facial expressions and tone of voice, and sends this data to a server. The server analyzes this data using emotion recognition tools to determine the user's emotional state. Face recognition libraries such as OpenCV and dlib are useful in this process.

[0525] Based on the results of emotion recognition, the notification content is adjusted. The server utilizes a generative AI model to automatically generate notification content with appropriate language according to the user's emotional state. For example, in a notification regarding new data protection regulations, if the user is feeling anxious, the notification tone is softened and reassuring language is used to create the message. This adjusted notification content is delivered to the user via email or internal channels.

[0526] As a concrete example, a prompt message for the generating AI model could be, "New data protection regulations have been implemented. Please create an explanatory message that will reassure users." In this way, the system reduces user stress regarding notifications of legal changes and enables more effective compliance.

[0527] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0528] Step 1:

[0529] The server automatically retrieves information on legal changes from online data sources and government APIs. It receives new data retrieval requests as input and accesses the APIs using Python or Java. As output, it retrieves raw data on legal changes and stores it in a database. This ensures the server always maintains the most up-to-date legal information.

[0530] Step 2:

[0531] The server applies natural language processing to the acquired legal information to analyze the changes. The input is the legal data acquired in step 1. A generative AI model (e.g., GPT or BERT) is used to analyze the text data and summarize the changes. The output extracts the summarized changes and their impact on related organizations. This makes it easier to understand the key points of the changes.

[0532] Step 3:

[0533] The device uses a camera and microphone to capture facial expressions and tone of voice in order to recognize the user's emotions. Input consists of the user's facial expression data and voice. A facial recognition library (such as OpenCV or dlib) is used to analyze the data and identify the user's emotional state as output. This data is sent to a server and used for notification adjustments.

[0534] Step 4:

[0535] The server adjusts the content of the legal change notification based on emotion recognition data. It uses emotion data received from the terminal as input. A generative AI model is used to automatically generate a notification message with a tone and expression appropriate to the user's emotions. The output is the adjusted notification message, which is then sent to the user.

[0536] Step 5:

[0537] The server provides users with coordinated notifications and countermeasures. The notification message generated in step 4 is used as input. It is delivered to users as output via a communication tool (email or messaging app). This allows users to understand the countermeasures with reduced emotional burden in response to the legal changes.

[0538] (Application Example 2)

[0539] 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."

[0540] In proposing countermeasures in response to legal changes, conventional systems often notify users without considering their feelings, which can cause stress and anxiety. This can lead to a decrease in motivation for effective legal compliance. In particular, when legal changes have a significant impact on an organization, notifications that disregard user feelings risk increasing their burden.

[0541] 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.

[0542] In this invention, the server includes means for acquiring information to monitor changes in laws and regulations, means for analyzing the acquired information and summarizing the changes in laws and regulations, means for notifying countermeasures to propose and notify countermeasures according to the evaluation results, and means for recognizing the user's emotions and adjusting the notification content according to their state. This enables appropriate notifications and suggestions that take the user's emotions into consideration, making it possible to respond effectively to changes in laws and regulations and reduce user stress.

[0543] "Information acquisition means" refers to a device or process that monitors changes in laws and regulations and automatically collects the latest legal information from online data sources or external APIs.

[0544] "Information analysis methods" refer to techniques that analyze acquired legal information using natural language processing technology and summarize the changes to those laws.

[0545] An "impact assessment tool" is a process that evaluates the impact of legal changes on an organization based on analyzed legal information.

[0546] A "response measure notification system" is a system that formulates necessary countermeasures for organizations and individuals based on evaluation results and notifies users of these measures.

[0547] A "history management system" is a device or process that databases the history of changes in laws and regulations and manages that information in a way that allows for quick and efficient retrieval.

[0548] An "emotion recognition system" is a system that detects a user's emotions in real time and adjusts notification content and suggestions according to that state.

[0549] This invention is constructed as a system for appropriately and effectively notifying users of changes in laws and regulations, by combining a server, a terminal, and an emotion recognition engine.

[0550] The server first uses online data sources and external APIs to collect information on changes in laws and regulations. After collection, an information analysis engine using natural language processing technology analyzes the acquired legal information and summarizes the changes and their impact.

[0551] Based on the analyzed information, the server evaluates the impact of legal changes on the organization via an impact assessment engine. Depending on the evaluation results, a response notification engine generates suggestions and notifies the user. During this notification, an emotion recognition engine detects the user's emotions through the camera and microphone on the device and generates notification content that takes those emotions into consideration. If the user is feeling stressed, the content and tone of the notification are adjusted, and softened as needed to reduce the user's anxiety.

[0552] Furthermore, changes in laws and regulations, along with their history, are stored in a database and managed in a way that allows for quick searching, making it possible to refer to past information as needed.

[0553] As a concrete example, suppose a small business using this system receives notification of a change in regulations, and the emotion recognition engine detects the business's anxiety. In this case, the server presents simple steps in calm language, such as "This is all you need to do to comply with the latest data protection regulations," thereby reducing the user's psychological burden.

[0554] An example of a prompt is: "Provide emotionally sensitive advice on how small business owners affected by legal changes can learn new security procedures while maintaining a sense of security."

[0555] Servers and terminals can utilize natural language processing libraries using Python, API servers using Node.js, and databases using MongoDB.

[0556] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0557] Step 1:

[0558] The server automatically retrieves information on legal changes from online data sources and external APIs. It takes raw data from each data source as input and generates detailed information on legal changes as output. This data is converted into a structured format and used in subsequent analysis steps. The server repeats this process periodically.

[0559] Step 2:

[0560] The server analyzes the acquired legal change information using natural language processing technology. The input is the detailed information of the legal change acquired in step 1, and the output is data summarizing the content and key points of the legal change. The server performs processes such as text tokenization, part-of-speech tagging, and semantic analysis to generate a summary of the legal change.

[0561] Step 3:

[0562] The server evaluates the impact on the organization based on the analysis results. The input is the output data from step 2, and the output is the impact analysis results. The server applies evaluation criteria and rules to assess the impact of legal changes on the organization's operations and business processes, and identifies specific impacts.

[0563] Step 4:

[0564] The server proposes countermeasures and prepares notifications based on the impact assessment results. The input is the impact analysis results from step 3, and the output is the proposed countermeasures and notification message. The server creates optimal countermeasure proposals using template generation and generation AI models.

[0565] Step 5:

[0566] The device recognizes the user's emotions in real time. The input is the user's facial expressions and voice data acquired through the camera and microphone, and the output is the user's emotional state. The device's emotion recognition engine evaluates the user's current emotions using image analysis and voice analysis.

[0567] Step 6:

[0568] The server adjusts and sends notification content according to the user's emotional state. The input is the emotional state obtained in step 5 and the notification message prepared in step 4, and the output is the adjusted notification. If the user is feeling anxious, the server softens the wording and tone of the notification to reduce the user's psychological burden.

[0569] Step 7:

[0570] Users receive notifications from their devices and act accordingly. Through coordinated notifications sent from the server, users can understand the appropriate countermeasures for new legal changes and take action based on them.

[0571] 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.

[0572] 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.

[0573] 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.

[0574] [Fourth Embodiment]

[0575] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0576] 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.

[0577] 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).

[0578] 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.

[0579] 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.

[0580] 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).

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] 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.

[0586] 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.

[0587] 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".

[0588] The legal monitoring system of the present invention consists of multiple components in order to efficiently monitor changes in laws and regulations and to facilitate appropriate responses within corporate organizations.

[0589] The server plays a central role in monitoring legal changes, automatically obtaining the latest legal updates by regularly checking online data sources and government APIs. When the server detects new information, it registers it as a change event and records it in the database.

[0590] The acquired information is analyzed by information analysis tools on the server. The server uses natural language processing (NLP) technology to analyze legal documents and automatically generates key changes and summaries. This analysis process extracts the essential points of the changes and efficiently evaluates their impact on the organization.

[0591] The impact assessment is performed using algorithms on the server to identify which departments and business processes within the organization the change affects. The server analyzes the degree of impact and assesses the impact on the relevant departments to determine appropriate countermeasures.

[0592] Next, the server uses a response notification system to propose specific countermeasures based on the analysis and evaluation results and notify the relevant parties. This is done via email or an in-system dashboard, enabling users to take necessary actions quickly.

[0593] Furthermore, all legal changes and their analysis results are stored in a database through a history management system. This database is accessible via terminals, allowing users to easily search and refer to past legal change history.

[0594] For example, if new data protection legislation is enacted, the server will detect it and notify the data management department to implement the new protective measures. In this way, cumbersome legal research is eliminated, and users can immediately understand and act on what they need to do to comply with the law.

[0595] The following describes the processing flow.

[0596] Step 1:

[0597] The server periodically accesses online data sources and government APIs that provide legal information to obtain new legal changes and notifications. The server collects data using scraping techniques and API calls, and records any updates it detects.

[0598] Step 2:

[0599] The server analyzes the acquired information using natural language processing (NLP) techniques. The server analyzes the text of legal documents and automatically generates key changes and summaries. At this stage, the information is structured and prepared for use in subsequent processes.

[0600] Step 3:

[0601] The server uses the analysis results to evaluate the impact on the organization and operations. The server matches the analysis results with business process information and departmental information stored in the internal database to identify which departments and processes may be affected.

[0602] Step 4:

[0603] The server develops appropriate countermeasures based on the assessment results. The server lists the necessary response procedures for each affected department and creates notification messages to inform relevant parties within the company.

[0604] Step 5:

[0605] The server notifies users of the necessary actions. The server sends notifications to users in the relevant department via email or the system's dashboard, enabling them to take the required action quickly.

[0606] Step 6:

[0607] The server stores information on legal changes, analysis results, and impact assessment results in a database. Users can easily access past legal change history and response history by searching this database using their terminals.

[0608] (Example 1)

[0609] 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".

[0610] Changes in legislation are often complex, and it is crucial for organizations to quickly carry out processes of proper information gathering, analysis, impact assessment, and proposal of countermeasures. However, doing this manually is extremely time-consuming and resource-intensive. Furthermore, continuously tracking and making available the history of legal changes is not easy.

[0611] 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.

[0612] In this invention, the server includes an information acquisition means for automatically collecting information from a data source, an information analysis means that utilizes natural language processing technology to analyze the collected data and summarize the changes in laws and regulations, and an impact assessment means that identifies and evaluates the impact on each department and operation within the organization based on the analysis results. This enables rapid and efficient monitoring and response to changes in laws and regulations.

[0613] "Information acquisition means" refers to processes or devices that automatically collect relevant information from data sources.

[0614] "Information analysis means" refers to the process or apparatus of analyzing collected data and summarizing the changes in laws and regulations using natural language processing technology.

[0615] "Impact assessment tools" refer to processes and devices used to identify and evaluate the impact on various departments and operations within an organization based on analysis results.

[0616] A "means of notifying about countermeasures" refers to a system or device that proposes necessary actions based on an evaluation and notifies relevant parties.

[0617] "History management means" refers to a process or device for accumulating changes in laws and regulations and their analysis results, and enabling searching and referencing them via terminals.

[0618] "Natural language processing technology" refers to the technology used to enable computers to understand and process human language.

[0619] This invention aims to provide a legal monitoring system that enables organizations to quickly and efficiently monitor changes in laws and regulations and to facilitate appropriate responses. This system primarily consists of server-centric functions and operates as follows:

[0620] The server obtains information on changes in laws and regulations via web services and online databases. This is achieved through various means of information retrieval, such as periodically scanning data sources using APIs to obtain the latest legal information.

[0621] Furthermore, the server processes the acquired legal information using information analysis tools. This analysis utilizes natural language processing technologies such as SpaCy and NLTK libraries. This makes it possible to tokenize changes in the laws and extract and summarize the main points.

[0622] Furthermore, the server uses impact assessment tools to evaluate the analyzed data and determine which departments and business processes within the organization are affected. This involves using analytical algorithms to compare the changes with the organization's business profile.

[0623] Based on analysis and impact assessment, the server notifies stakeholders of the necessary countermeasures through its countermeasure notification system. This notification is provided to users via email or the system's dashboard, allowing them to take necessary actions quickly.

[0624] The terminal is used to allow users to access past legal change information through history management mechanisms and search for necessary data. This makes it easier for users to manage their access to past information.

[0625] For example, if new data protection legislation is enacted, the server will detect it. The server will then notify the data management department of the necessary countermeasures, including changes to new processes and procedures. This allows the organization to comply with the new legislation quickly.

[0626] The following prompt could be used as input for the generative AI model: "Please explain the main points of the newly enacted data protection law and the corresponding actions organizations should take."

[0627] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0628] Step 1:

[0629] The server accesses data sources using information acquisition methods based on a pre-configured schedule, automatically retrieving the latest legal change information from online databases and APIs. The input is connection information such as URLs and API keys, and the output is the latest legal change information. Specifically, the server periodically executes API calls and retrieves response data.

[0630] Step 2:

[0631] The server analyzes legal data acquired using information analysis tools. The input is the legal change information acquired in step 1. The server uses natural language processing technology (e.g., SpaCy or NLTK) to tokenize and analyze the legal document and extract important changes. The output is a summary of the main changes to the law. Specifically, the data is tokenized, noun phrases and verb phrases are extracted, and the important parts of the law are summarized.

[0632] Step 3:

[0633] The server uses impact assessment tools to evaluate the impact within the organization based on the analysis results. The input is the changes in legislation summarized in step 2. Based on the organization's business profile, the server identifies which departments and business processes will be affected. The output is the affected areas and the degree of impact. Specifically, an evaluation algorithm is used to identify the relevant departments within the organization.

[0634] Step 4:

[0635] The server uses a response notification mechanism to notify relevant parties of the necessary countermeasures. The input is the impact assessment results identified in step 3. The output is notification information sent to relevant departments and personnel within the organization. Specifically, the server sets up notification messages to be automatically generated by the server and immediately distributes them to relevant parties via email or dashboard.

[0636] Step 5:

[0637] Using a terminal, users access the history of legal changes through a history management system. Input consists of keywords and dates / times for the user's search. Output is detailed data of past legal change history. Specifically, the user logs into the system from the terminal, searches the database using a GUI, and retrieves the necessary information.

[0638] (Application Example 1)

[0639] 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".

[0640] Modern businesses are required to quickly and accurately grasp frequently changing laws and regulations and take appropriate countermeasures based on them. However, because legal information changes are so diverse, manual management is difficult, leading to risks of delayed compliance and incorrect responses. Furthermore, there are insufficient means to quickly assess the impact of legal changes on specific departments within an organization and to effectively notify those departments. As a result, there is a need to minimize the impact on legal compliance and security measures across the entire organization.

[0641] 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.

[0642] In this invention, the server includes information acquisition means for monitoring information on changes in laws and regulations, information analysis means for analyzing the acquired information and generating a summary, and impact assessment means for evaluating the impact based on the analysis results. This makes it possible to grasp changes in laws and regulations in real time and automatically provide accurate notifications to the relevant departments.

[0643] "Changes to laws and regulations" refer to changes in the content of laws and regulations enacted or amended by the government or administrative agencies.

[0644] "Information acquisition means" refers to a function that automatically acquires the latest information on legal changes from online data sources or APIs.

[0645] "Information analysis means" refers to technology that analyzes acquired legal information and generates key changes and summaries of it.

[0646] "Impact assessment tools" refer to the function that evaluates which departments or business processes within an organization will be affected by the analyzed legal changes.

[0647] "Means of notifying countermeasures" refers to the means of communicating necessary countermeasures to relevant departments based on changes in laws and regulations.

[0648] "History management means" refers to a function that stores the history of legal changes and analysis results in a database and manages them in a way that allows them to be referenced later.

[0649] An "application that notifies machines and devices in real time" refers to software that immediately notifies users of changes in laws and regulations on their devices, making relevant information accessible.

[0650] "Machine learning" refers to the technology that allows computers to learn from past data and patterns to predict or classify future data and events.

[0651] A "security policy" refers to a set of guidelines and procedures designed to help an organization protect its information and comply with laws and regulations.

[0652] To implement this invention, a system is constructed in which a server plays a central role. The server is equipped with means for automatically obtaining information on changes in laws and regulations from online data sources and government APIs. In this process, cloud services such as AWS and Google Cloud are utilized for data acquisition and storage. The acquired legal information is analyzed using NLP (Natural Language Processing) technology to generate a summary of the changes. Open-source libraries such as SpaCy and NLTK are used for this analysis.

[0653] The server also includes an impact assessment tool that uses machine learning techniques to evaluate the impact of legal changes on the organization's security policies. The results of this assessment are sent as push notifications to user terminals in relevant departments via notification services such as Firebase Cloud Messaging. Users who receive the notification can then check the content on their smartphones or desktop devices and take necessary countermeasures quickly.

[0654] For example, if new data protection regulations are announced, the server will immediately detect them, perform an impact analysis, and notify users of the necessary actions. This process allows users to accurately understand the situation and ensure legal compliance while improving organizational security.

[0655] To gain specific insights into the impact of new legislation on an organization using a generative AI model, the following prompt statements can be used:

[0656] "New data protection legislation has been announced. What changes will this have on how companies manage their data?"

[0657] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0658] Step 1:

[0659] The server periodically accesses online data sources and government APIs to retrieve information on changes in legislation. The input is API requests, and the output is raw data of the legislation. The server stores this data in a database. This process is performed as an automatically scheduled task.

[0660] Step 2:

[0661] The server analyzes the acquired legal data using natural language processing (NLP) techniques. The input is the raw legal data obtained in step 1, and the output is a summary of legal changes. The server extracts characteristic keywords and phrases and summarizes the important changes. The SpaCy library is often used for this process.

[0662] Step 3:

[0663] The server assesses the impact on each department of an organization based on a summary of the legal changes. The input is the summary information generated in step 2, and the output is a list of affected departments and the degree of impact. A machine learning model is used for the impact assessment, making decisions based on past data and impact patterns.

[0664] Step 4:

[0665] The server develops necessary countermeasures based on the impact assessment results and notifies the user. The input is the impact assessment results from step 3, and the output is a push notification message. Firebase Cloud Messaging is used to send the notification directly to the user's device.

[0666] Step 5:

[0667] Users check notifications on their devices and refer to recommended actions to take the necessary steps. The input is the notification message, and the output is the actual status of the implemented actions. This ensures rapid compliance with regulations and appropriate measures within the organization.

[0668] Step 6:

[0669] The server stores all legal change information, its analysis results, and a history of countermeasures in a database. Input is the data obtained in each step so far, and output is searchable historical data. Users can refer to past history as needed.

[0670] The above steps enable a swift and appropriate response to changes in legislation. Downstream users can use prompts for the generated AI model, such as, "New data protection legislation has been announced. What changes will affect our company's data management?", to conduct a detailed impact analysis.

[0671] 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.

[0672] This invention combines a system for monitoring and analyzing changes in laws and regulations with an emotion engine that recognizes user emotions, thereby enabling the notification of more appropriate and effective countermeasures. Specific embodiments for carrying out this invention are shown below.

[0673] The server, like traditional legal monitoring systems, periodically retrieves information on changes in laws and regulations using online data sources and government APIs. This ensures that the server always maintains up-to-date legal information and detects changes in real time.

[0674] Once legal information is retrieved, the server uses natural language processing technology to analyze the information and identify the key points of the changes and their impact. This allows for an assessment of the impact of legal changes on the organization and the development of necessary countermeasures.

[0675] The emotion engine, a key feature of this invention, has the ability to recognize the user's emotions in real time. This engine analyzes the user's facial expressions and tone of voice through a camera and microphone, and identifies emotions from them. Specifically, the terminal captures the user's facial expressions, and the server analyzes that data to determine the user's emotional state.

[0676] Information obtained from the emotion engine is reflected in notifications of legal changes and suggestions for countermeasures. For example, if the server detects that a user is experiencing stress, it adjusts the content of the notification to be softer and condenses the information provided to the bare minimum, thereby reducing the user's burden.

[0677] For example, if a user is concerned about a change after receiving notification of new data protection regulations, the system will soften the tone of the notification and present solutions in reassuring language.

[0678] This configuration allows for communication that not only informs users of legal changes but also takes their feelings into consideration, thereby supporting more effective legal compliance.

[0679] The following describes the processing flow.

[0680] Step 1:

[0681] The server periodically accesses online data sources and government APIs that provide legal information to obtain new legal changes and notifications. When the server detects new information, it records it in the database and registers it as a legal document to be analyzed.

[0682] Step 2:

[0683] The server analyzes acquired legal information using natural language processing (NLP) technology. The server analyzes the text of legal documents, extracts key changes, and automatically generates summaries. This analysis clarifies the specific impact on organizations and operations.

[0684] Step 3:

[0685] Based on the impact assessment results, the server will formulate necessary countermeasures regarding the legal changes. The server will list the countermeasures for each affected department and prepare them as notifications.

[0686] Step 4:

[0687] The user's device utilizes its camera and microphone to record the user's current emotional state. The device captures the user's facial expressions and collects digital data for analyzing their voice tone.

[0688] Step 5:

[0689] The server uses an emotion engine to analyze data sent from the terminal and identify the user's emotions. For example, it can determine whether the user is feeling at ease or stressed based on changes in voice tone and facial expressions.

[0690] Step 6:

[0691] The server adjusts the tone and content of notifications based on the user's emotional state. If the server determines that the user is stressed, it will make the notifications gentler and more concise to avoid burdening them.

[0692] Step 7:

[0693] The server sends a tailored notification to the user. The user receives the notification on their device and reviews the suggested course of action, which is presented in an emotionally sensitive manner.

[0694] Step 8:

[0695] The server stores information on legal changes, analysis results, and sentiment analysis results in a database. By using a terminal, users can easily access past legal change history, response history, and sentiment-based notification adjustment history by searching this database.

[0696] (Example 2)

[0697] 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".

[0698] Changes in laws and regulations are crucial information for organizations, and it is essential to grasp them in a timely manner and take appropriate action. However, conventional legal monitoring systems unilaterally notify users of information without considering their feelings, which can cause unnecessary stress to recipients. Furthermore, there is a challenge in providing flexible responses that take into account the emotional state of the recipients.

[0699] 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.

[0700] In this invention, the server includes information acquisition means, information analysis means, impact assessment means, emotion recognition means, and notification adjustment means. This makes it possible to adjust the content of legal change notifications to take into account the recipient's emotional state, thereby supporting more effective and user-friendly legal compliance.

[0701] "Information acquisition means" refers to an element that has the function of automatically acquiring information on changes in laws and regulations from a data set.

[0702] An "information analysis tool" is an element that has the function of analyzing acquired legal information using natural language processing technology and summarizing the changes.

[0703] An "impact assessment tool" is an element that has the function of evaluating the impact on an organization based on the analysis results.

[0704] A "means for notifying countermeasures" refers to an element that has the function of proposing countermeasures based on evaluation results and notifying the user of those countermeasures.

[0705] A "history management system" is an element that has the function of creating a database of the history of legal changes and managing them in a searchable format.

[0706] An "emotion recognition tool" is an element that has the function of recognizing and analyzing the user's emotions in real time.

[0707] A "notification adjustment mechanism" is an element that has the function of adjusting notification content based on information obtained from emotion recognition mechanisms and presenting it in a form optimized for the user.

[0708] To implement this invention, it is necessary to construct a system that monitors and notifies of changes in laws and regulations, and recognizes the emotions of users.

[0709] The server is responsible for automatically collecting information on changes in laws and regulations by utilizing online data sources and government APIs. This involves using scripts written in programming languages ​​such as Python and Java to periodically retrieve data via APIs. The retrieved legal information is then analyzed on the server using natural language processing (NLP) techniques. This analysis utilizes generative AI models such as OpenAI's GPT and Google's BERT to extract summaries of the changes and their impact.

[0710] The device plays a crucial role in recognizing the user's emotions in real time. Specifically, it uses a camera and microphone to capture the user's facial expressions and tone of voice, and sends this data to a server. The server analyzes this data using emotion recognition tools to determine the user's emotional state. Face recognition libraries such as OpenCV and dlib are useful in this process.

[0711] Based on the results of emotion recognition, the notification content is adjusted. The server utilizes a generative AI model to automatically generate notification content with appropriate language according to the user's emotional state. For example, in a notification regarding new data protection regulations, if the user is feeling anxious, the notification tone is softened and reassuring language is used to create the message. This adjusted notification content is delivered to the user via email or internal channels.

[0712] As a concrete example, a prompt message for the generating AI model could be, "New data protection regulations have been implemented. Please create an explanatory message that will reassure users." In this way, the system reduces user stress regarding notifications of legal changes and enables more effective compliance.

[0713] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0714] Step 1:

[0715] The server automatically retrieves information on legal changes from online data sources and government APIs. It receives new data retrieval requests as input and accesses the APIs using Python or Java. As output, it retrieves raw data on legal changes and stores it in a database. This ensures the server always maintains the most up-to-date legal information.

[0716] Step 2:

[0717] The server applies natural language processing to the acquired legal information to analyze the changes. The input is the legal data acquired in step 1. A generative AI model (e.g., GPT or BERT) is used to analyze the text data and summarize the changes. The output extracts the summarized changes and their impact on related organizations. This makes it easier to understand the key points of the changes.

[0718] Step 3:

[0719] The device uses a camera and microphone to capture facial expressions and tone of voice in order to recognize the user's emotions. Input consists of the user's facial expression data and voice. A facial recognition library (such as OpenCV or dlib) is used to analyze the data and identify the user's emotional state as output. This data is sent to a server and used for notification adjustments.

[0720] Step 4:

[0721] The server adjusts the content of the legal change notification based on emotion recognition data. It uses emotion data received from the terminal as input. A generative AI model is used to automatically generate a notification message with a tone and expression appropriate to the user's emotions. The output is the adjusted notification message, which is then sent to the user.

[0722] Step 5:

[0723] The server provides users with coordinated notifications and countermeasures. The notification message generated in step 4 is used as input. It is delivered to users as output via a communication tool (email or messaging app). This allows users to understand the countermeasures with reduced emotional burden in response to the legal changes.

[0724] (Application Example 2)

[0725] 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".

[0726] In proposing countermeasures in response to legal changes, conventional systems often notify users without considering their feelings, which can cause stress and anxiety. This can lead to a decrease in motivation for effective legal compliance. In particular, when legal changes have a significant impact on an organization, notifications that disregard user feelings risk increasing their burden.

[0727] 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.

[0728] In this invention, the server includes means for acquiring information to monitor changes in laws and regulations, means for analyzing the acquired information and summarizing the changes in laws and regulations, means for notifying countermeasures to propose and notify countermeasures according to the evaluation results, and means for recognizing the user's emotions and adjusting the notification content according to their state. This enables appropriate notifications and suggestions that take the user's emotions into consideration, making it possible to respond effectively to changes in laws and regulations and reduce user stress.

[0729] "Information acquisition means" refers to a device or process that monitors changes in laws and regulations and automatically collects the latest legal information from online data sources or external APIs.

[0730] "Information analysis methods" refer to techniques that analyze acquired legal information using natural language processing technology and summarize the changes to those laws.

[0731] An "impact assessment tool" is a process that evaluates the impact of legal changes on an organization based on analyzed legal information.

[0732] A "response measure notification system" is a system that formulates necessary countermeasures for organizations and individuals based on evaluation results and notifies users of these measures.

[0733] A "history management system" is a device or process that databases the history of changes in laws and regulations and manages that information in a way that allows for quick and efficient retrieval.

[0734] An "emotion recognition system" is a system that detects a user's emotions in real time and adjusts notification content and suggestions according to that state.

[0735] This invention is constructed as a system for appropriately and effectively notifying users of changes in laws and regulations, by combining a server, a terminal, and an emotion recognition engine.

[0736] The server first uses online data sources and external APIs to collect information on changes in laws and regulations. After collection, an information analysis engine using natural language processing technology analyzes the acquired legal information and summarizes the changes and their impact.

[0737] Based on the analyzed information, the server evaluates the impact of legal changes on the organization via an impact assessment engine. Depending on the evaluation results, a response notification engine generates suggestions and notifies the user. During this notification, an emotion recognition engine detects the user's emotions through the camera and microphone on the device and generates notification content that takes those emotions into consideration. If the user is feeling stressed, the content and tone of the notification are adjusted, and softened as needed to reduce the user's anxiety.

[0738] Furthermore, changes in laws and regulations, along with their history, are stored in a database and managed in a way that allows for quick searching, making it possible to refer to past information as needed.

[0739] As a concrete example, suppose a small business using this system receives notification of a change in regulations, and the emotion recognition engine detects the business's anxiety. In this case, the server presents simple steps in calm language, such as "This is all you need to do to comply with the latest data protection regulations," thereby reducing the user's psychological burden.

[0740] An example of a prompt is: "Provide emotionally sensitive advice on how small business owners affected by legal changes can learn new security procedures while maintaining a sense of security."

[0741] Servers and terminals can utilize natural language processing libraries using Python, API servers using Node.js, and databases using MongoDB.

[0742] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0743] Step 1:

[0744] The server automatically retrieves information on legal changes from online data sources and external APIs. It takes raw data from each data source as input and generates detailed information on legal changes as output. This data is converted into a structured format and used in subsequent analysis steps. The server repeats this process periodically.

[0745] Step 2:

[0746] The server analyzes the acquired legal change information using natural language processing technology. The input is the detailed information of the legal change acquired in step 1, and the output is data summarizing the content and key points of the legal change. The server performs processes such as text tokenization, part-of-speech tagging, and semantic analysis to generate a summary of the legal change.

[0747] Step 3:

[0748] The server evaluates the impact on the organization based on the analysis results. The input is the output data from step 2, and the output is the impact analysis results. The server applies evaluation criteria and rules to assess the impact of legal changes on the organization's operations and business processes, and identifies specific impacts.

[0749] Step 4:

[0750] The server proposes countermeasures and prepares notifications based on the impact assessment results. The input is the impact analysis results from step 3, and the output is the proposed countermeasures and notification message. The server creates optimal countermeasure proposals using template generation and generation AI models.

[0751] Step 5:

[0752] The device recognizes the user's emotions in real time. The input is the user's facial expressions and voice data acquired through the camera and microphone, and the output is the user's emotional state. The device's emotion recognition engine evaluates the user's current emotions using image analysis and voice analysis.

[0753] Step 6:

[0754] The server adjusts and sends notification content according to the user's emotional state. The input is the emotional state obtained in step 5 and the notification message prepared in step 4, and the output is the adjusted notification. If the user is feeling anxious, the server softens the wording and tone of the notification to reduce the user's psychological burden.

[0755] Step 7:

[0756] Users receive notifications from their devices and act accordingly. Through coordinated notifications sent from the server, users can understand the appropriate countermeasures for new legal changes and take action based on them.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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."

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] The following is further disclosed regarding the embodiments described above.

[0779] (Claim 1)

[0780] Means of obtaining information to monitor changes in laws and regulations,

[0781] Information analysis means for analyzing acquired information and summarizing changes in laws and regulations,

[0782] An impact assessment method for evaluating the impact on an organization based on the analysis results,

[0783] A means of notifying countermeasures to propose and notify countermeasures based on the evaluation results,

[0784] A history management system that databases the history of legal changes and manages that data in a searchable format,

[0785] A system that includes this.

[0786] (Claim 2)

[0787] The system according to claim 1, wherein the means for acquiring information automatically retrieves information on changes in laws and regulations from an online data source.

[0788] (Claim 3)

[0789] The system according to claim 1, wherein the information analysis means analyzes the changes using natural language processing technology.

[0790] "Example 1"

[0791] (Claim 1)

[0792] Information acquisition means that automatically collects information from a data source,

[0793] An information analysis method that utilizes natural language processing technology to analyze collected data and summarize changes in laws and regulations,

[0794] An impact assessment tool that identifies and evaluates the impact on each department and operation within the organization based on the analysis results,

[0795] A means of notifying relevant parties of necessary actions based on the evaluation and communication technology,

[0796] A history management system that stores legal changes and analysis results, and enables searching and referencing via terminals,

[0797] A system that includes this.

[0798] (Claim 2)

[0799] The system according to claim 1, wherein the information acquisition means automatically acquires information from an external data source based on a program.

[0800] (Claim 3)

[0801] The system according to claim 1, wherein the information analysis means scrutinizes data collected by natural language processing technology and clarifies the changes.

[0802] "Application Example 1"

[0803] (Claim 1)

[0804] Means of obtaining information to monitor changes in laws and regulations,

[0805] Information analysis means for analyzing acquired information and summarizing changes in laws and regulations,

[0806] An impact assessment method for evaluating the impact on an organization based on the analysis results,

[0807] A means of notifying countermeasures to propose and notify countermeasures based on the evaluation results,

[0808] A history management system that databases the history of legal changes and manages that data in a searchable format,

[0809] An application that notifies machines and devices of legal changes in real time, allowing users to easily access legal information,

[0810] A means of automatically evaluating the impact of legal changes on security policies using machine learning and directly notifying the appropriate departments within the organization,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, wherein the means for acquiring information automatically retrieves information on changes in laws and regulations from an online data source.

[0814] (Claim 3)

[0815] The system according to claim 1, wherein the information analysis means analyzes the changes using natural language processing technology.

[0816] "Example 2 of combining an emotion engine"

[0817] (Claim 1)

[0818] Means of obtaining information to monitor changes in laws and regulations,

[0819] Information analysis means for analyzing acquired information and summarizing changes in laws and regulations,

[0820] An impact assessment method for evaluating the impact on an organization based on the analysis results,

[0821] A means of notifying countermeasures to propose and notify countermeasures based on the evaluation results,

[0822] A history management system that databases the history of legal changes and manages that data in a searchable format,

[0823] A means of recognizing the emotions of the user,

[0824] A notification adjustment means for adjusting notification content based on information obtained from an emotion recognition means,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The system according to claim 1, wherein the information acquisition means automatically retrieves information on changes in laws and regulations from a data set.

[0828] (Claim 3)

[0829] The system according to claim 1, wherein the information analysis means analyzes the changes using natural language processing technology.

[0830] "Application example 2 when combining with an emotional engine"

[0831] (Claim 1)

[0832] Means of obtaining information to monitor changes in laws and regulations,

[0833] Information analysis means for analyzing acquired information and summarizing changes in laws and regulations,

[0834] An impact assessment method for evaluating the impact on an organization based on the analysis results,

[0835] A means of notifying countermeasures to propose and notify countermeasures based on the evaluation results,

[0836] A history management system that databases the history of legal changes and manages that data in a searchable format,

[0837] An emotion recognition means for recognizing the user's emotions and adjusting the notification content according to that state,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, wherein the means for acquiring information automatically retrieves information on changes in laws and regulations from an online data source.

[0841] (Claim 3)

[0842] The system according to claim 1, wherein the information analysis means analyzes the changes using natural language processing technology. [Explanation of Symbols]

[0843] 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. Means of obtaining information to monitor changes in laws and regulations, Information analysis means for analyzing acquired information and summarizing changes in laws and regulations, An impact assessment method for evaluating the impact on an organization based on the analysis results, A means of notifying countermeasures to propose and notify countermeasures based on the evaluation results, A history management system that databases the history of legal changes and manages that data in a searchable format, A system that includes this.

2. The system according to claim 1, wherein the information acquisition means automatically retrieves information on changes in laws and regulations from an online data source.

3. The system according to claim 1, wherein the information analysis means analyzes the changes using natural language processing technology.

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