System

Abstract

A system is provided.SOLUTION: The system includes a means for automatically collecting law revision information, a means for analyzing the collected law revision information to specify revision contents, a means for specifying an in-house document affected by the specified revision contents, a means for grouping and categorizing the specified influence range, a means for automatically generating a new and old comparison table, a means for evaluating a law compliance situation for each company or department, a means for providing a customized improvement measure and advice based on an evaluation result, and a means for notifying a user terminal of the provided result.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] It is difficult for companies and organizations to respond quickly and accurately to frequent legal changes. Understanding the content of legal changes and applying them to internal regulations and operational rules requires a lot of time and effort. Furthermore, delayed or insufficient responses increase the risk of legal violations and omissions. The present invention aims to provide an efficient means for reducing the burden associated with legal changes and ensuring compliance with laws and regulations. [Means for solving the problem]

[0005] The present invention is a system that includes means for automatically collecting legal amendment information, analyzing the collected legal amendment information to identify the amendment details, means for identifying internal documents affected by the identified amendment details, means for grouping and categorizing the identified scope of impact, means for automatically generating a comparison table of old and new versions, means for evaluating the legal compliance status of each company or department, means for providing customized improvement measures and advice based on the evaluation results, and means for notifying the user of the provided results to their terminal. This enables quick and accurate response to legal amendments and minimizes the risk of legal violations and omissions.

[0006] "Legal amendment information" refers to information regarding changes, additions, or deletions to laws and regulations announced by the government or related agencies.

[0007] "Means of collection" refers to the function of automatically obtaining legal change information from external databases and news feeds.

[0008] "Means of analysis" refers to the function of analyzing collected legal amendment information and identifying its content and scope of impact.

[0009] "Amendments" refers to changes to existing laws and regulations and newly added laws and regulations.

[0010] "Means of identification" refers to the function of identifying affected internal documents and operational rules based on analysis.

[0011] The "scope of impact" refers to the internal documents and operational rules that are related to the identified amendments.

[0012] "Grouping" refers to classifying the identified impact areas based on type and relationship.

[0013] "Categorization" refers to classifying the identified impact range into predetermined categories.

[0014] A "comparison table of old and new laws and regulations" refers to a table that compares the contents of laws and regulations before and after the revision.

[0015] "Means for automatic generation" refers to the function that automatically creates a comparison table between the old and new versions.

[0016] "Compliance status" refers to the extent to which a company or sector complies with current laws and regulations.

[0017] "Means of evaluation" refers to the function of evaluating compliance with laws and regulations using numerical values ​​and indicators.

[0018] "Improvement measures" refer to the specific measures and means necessary to comply with laws and regulations.

[0019] "Advice" refers to specific guidance or suggestions given based on a particular situation.

[0020] "Means of providing" refers to the function of communicating improvement measures and advice to users.

[0021] "User" refers to the person in charge of a company or organization that uses the system.

[0022] "Terminal" refers to an electronic device that allows a user to receive notifications and information from the system. [Brief explanation of the drawings]

[0023] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0024] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0025] First, the terms used in the following description will be explained.

[0026] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0027] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0028] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0029] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, 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), Bluetooth (registered trademark), etc.

[0030] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0031] [First embodiment]

[0032] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0033] 1, a 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.

[0034] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0035] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0036] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0037] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0038] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0039] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0041] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0042] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0043] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0044] The present invention provides a system that automatically collects and analyzes legal amendment information, enabling companies and organizations to respond to legal amendments quickly and accurately. This system includes multiple processing steps involving servers, terminals, and users.

[0045] About program processing

[0046] 1. Collecting information on legal reforms

[0047] The server automatically accesses external legal databases and related news feeds to retrieve the latest legal information, eliminating the need for companies to manually gather information.

[0048] 2. Analysis of legal reforms

[0049] The server preprocesses the legal amendment information it obtains and converts it into a format suitable for analysis. It then uses a generative AI model (e.g., BERT or GPT-3 (registered trademark)) to analyze the legal amendments and identify the specific amendments.

[0050] 3. Identifying the scope of impact

[0051] The server scans the company's internal documents and operational rules database to automatically identify areas that the identified revisions will affect, using full-text search and regular expressions in the process.

[0052] 4. Grouping and categorizing relevant sections

[0053] The server groups and categorizes the identified impact areas using a text classification algorithm, so that impact areas that belong to the same category are displayed together.

[0054] 5. Generate a comparison table of old and new versions

[0055] The server compares the old and new legal content and automatically generates a comparison table that clearly visualizes which parts of the company will be changed and how.

[0056] 6. Compliance Assessment

[0057] The server evaluates the legal compliance status of each company and each department. The evaluation results indicate the extent to which the company complies with laws and regulations and allow the user to understand the progress of necessary measures.

[0058] 7. Providing customized advice

[0059] Based on the evaluation results, the server generates specific improvement measures and advice customized for each company or department, thereby improving compliance with laws and regulations.

[0060] 8. User Notification and Confirmation

[0061] The server notifies the user's device of the analysis results, the generated comparison table of the old and new versions, and improvement measures. The user can check this information on their own device and take any necessary measures.

[0062] Specific examples

[0063] Example 1: Enactment of the new Personal Information Protection Law

[0064] 1. Collecting information on legal reforms

[0065] The server retrieves information about the new Personal Information Protection Act from the legislation database.

[0066] 2. Analysis of legal reforms

[0067] The server preprocesses the information it receives and then uses a generative AI model to identify "personal information encryption obligations."

[0068] 3. Identifying the scope of impact

[0069] The server scans the company's internal document database to identify documents related to the handling of personal information, which contain information about the current storage method (password protection).

[0070] 4. Grouping and categorizing relevant sections

[0071] The server groups and categorizes the scope of influence regarding the "storage method."

[0072] 5. Generate a comparison table of old and new versions

[0073] The server compares the new rule, "Personal information is encrypted and stored," with the old rule, "Stored in a password-protected file," and generates a comparison table of the old and new rules.

[0074] 6. Compliance Assessment

[0075] The server evaluates the compliance status of the IT and HR departments. It finds that the IT department has already implemented encryption, but the HR department has not.

[0076] 7. Providing customized advice

[0077] The server recommends that the HR department "install encryption software" and the IT department "regularly update encryption protocols."

[0078] 8. User Notification and Confirmation

[0079] The server notifies the terminals of the personnel in each department of the results of the analysis and a comparison table of the old and new data. The users can then check these on their terminals and take any necessary action.

[0080] This system allows companies and organizations to respond quickly and efficiently to legal changes and minimize the risk of non-compliance with laws and regulations or omissions.

[0081] The processing flow will be explained below.

[0082] Step 1:

[0083] The server accesses external legal databases and related news feeds to collect legal amendment information. Using APIs and crawling tools, the latest legal amendment information is automatically retrieved and stored in the database.

[0084] Step 2:

[0085] The server preprocesses the acquired legal amendment information, normalizing the data, filtering unnecessary information, standardizing the format, and converting it into a format suitable for analysis.

[0086] Step 3:

[0087] The server analyzes the preprocessed legal amendment information using a generative AI model (e.g., BERT or GPT-3), identifying specific amendments and their scope of application, and extracting them as text.

[0088] Step 4:

[0089] The server accesses the company's internal document database and identifies the documents and operational rules that will be affected based on the analysis results. It then uses full-text search and regular expressions to extract relevant text.

[0090] Step 5:

[0091] The server groups and categorizes the identified impact areas, using a text classification algorithm to categorize the impact areas into categories such as "acquisition method," "storage method," and "usage method."

[0092] Step 6:

[0093] The server generates an initial version of the comparison table, comparing the old and new legal content and creating a comparison table in a format that clearly shows the changes for each article.

[0094] Step 7:

[0095] The server verifies the old and new tables to check for omissions and duplications, automatically checking using an algorithm and making any necessary corrections.

[0096] Step 8:

[0097] The server evaluates the legal compliance status of each company and department, and evaluates the degree to which each department complies with laws and regulations using numerical values ​​and indicators based on the information in the database.

[0098] Step 9:

[0099] Based on the results of the assessment, the server generates customized improvement measures and advice, such as proposing specific measures such as "installing encryption software" and "performing regular audits" for each department.

[0100] Step 10:

[0101] The server notifies the user's device of the analysis results, the generated comparison table of old and new data, and improvement measures. Information is provided promptly via email or push notification.

[0102] Step 11:

[0103] The user checks the notification on the device, reviews the analysis results, comparison table of old and new data, and improvement measures, and takes action to implement the necessary measures.

[0104] Step 12:

[0105] Users update the progress status within the system. For items that have been completed, the status is updated within the system to manage the progress of improvements to legal compliance.

[0106] Example 1

[0107] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0108] In order for companies and organizations to respond quickly and accurately to legal changes, they must manually collect and analyze vast amounts of legal change information and identify the extent of impact within the company. This requires significant human resources and time, and specialized knowledge is also required to evaluate compliance and propose improvement measures. This puts companies and organizations at risk of violating laws and regulations and is likely to suffer disadvantages due to delayed response. To solve these issues, a system is needed that automatically collects and analyzes legal change information, identifies the extent of impact, and provides appropriate improvement measures.

[0109] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0110] In this invention, the server includes means for automatically collecting legal amendment information, means for preprocessing the collected legal amendment information, means for using a generative AI model to analyze the preprocessed legal amendment information, means for generating prompt sentences based on the analyzed amendment content, means for using a full-text search engine to search for internal documents affected by the amendment content, means for applying a text classification algorithm to group and categorize the searched documents, means for automatically generating a comparison table of old and new documents, means for evaluating the legal compliance status of each company or department, means for providing customized improvement measures and advice based on the evaluation results, and means for notifying the user's terminal of the provided results, thereby enabling companies and organizations to respond quickly and efficiently to legal amendments.

[0111] "Legal amendment information" refers to information regarding changes or amendments to laws and regulations.

[0112] "Preprocessing" refers to the process of filtering and shaping data to convert it into a form suitable for analysis.

[0113] "Generative AI models" refer to algorithms or systems that use artificial intelligence techniques to analyze data. Examples include BERT and GPT-3.

[0114] A "prompt" refers to text in the form of instructions or questions that are input into a generative AI model.

[0115] "Full-text search engine" refers to a software system for searching large amounts of document data for specific keywords or phrases. Examples include ElasticSearch (registered trademark).

[0116] "Text classification algorithm" refers to a machine learning algorithm for classifying text data into specific categories or groups. Examples include random forests and support vector machines (SVMs).

[0117] The "old and new comparison table" refers to a table that lists and compares the contents of new laws and regulations with the contents of old laws and regulations.

[0118] "Compliance with laws and regulations" refers to the extent to which a company or organization operates in compliance with current laws and regulations.

[0119] "Customized solutions and advice" refers to specific solutions and advice tailored to the specific circumstances of a company or sector.

[0120] "User device" refers to a device such as a computer, smartphone, or tablet used by the ultimate recipient.

[0121] The present invention provides a system that automatically collects and analyzes legal amendment information, enabling companies and organizations to respond to legal amendments quickly and accurately. This system includes multiple processing steps involving servers, terminals, and users.

[0122] Collecting information on legal reforms

[0123] The server automatically accesses an external legal database (e.g., "legal database") or news feed to obtain the latest legal amendment information. For example, the server can use an API to send a query to a legal database and collect the necessary legal amendment information. This automatic collection reduces the effort required for companies to manually collect information.

[0124] Pre-processing of legal amendments

[0125] The server preprocesses the collected legal amendment information and converts it into a format suitable for analysis, including removing unnecessary HTML tags and special characters, and converting it into JSON or text file format.

[0126] Analysis with generative AI models

[0127] The preprocessed legal amendment information is analyzed using a generative AI model (e.g., BERT or GPT-3). This identifies the specific content and changes of the legal amendment. For example, the server issues a prompt statement, "Please tell me the key points of the amendments to the new Personal Information Protection Act," to the generative AI model and performs analysis based on the response.

[0128] Identifying the scope of impact

[0129] The server scans the company's internal document database using a full-text search engine (e.g., "full-text search engine") to identify affected documents based on specific keywords or regular expressions. For example, it searches for documents containing keywords such as "personal information" or "encryption."

[0130] Grouping and categorizing relevant sections

[0131] The server groups and categorizes the identified impact areas using a text classification algorithm (e.g., "random forest" or "SVM"), so that impact areas that belong to the same category are displayed together.

[0132] Generate a comparison table of old and new versions

[0133] The server compares the new legal amendments with the old laws and regulations and automatically generates a comparison table. This clearly visualizes which parts of the company will change and how. Specific software tools that can be used include "DiffMatchPatch."

[0134] Compliance assessment

[0135] The server evaluates the legal compliance status of each company and each department. Based on the evaluation results, it is possible to understand the extent to which the company complies with laws and regulations and the progress of necessary measures.

[0136] Providing customized advice

[0137] Based on the results of the assessment, the server generates specific improvement measures and advice tailored to each company and department, such as recommending the latest encryption software for the IT department and training on new encryption processes for the HR department.

[0138] User notification and confirmation

[0139] The server notifies the user's device of the analysis results, the generated comparison table of old and new data, and improvement measures. The user can check this information on their own device and take any necessary measures. Notification methods include email and a dedicated app.

[0140] Specific examples

[0141] For example, let us consider the case where a new Personal Information Protection Act comes into effect.

[0142] 1. The server retrieves information about the new Personal Information Protection Act from the legislation database.

[0143] 2. Preprocess the acquired information and then use a generative AI model to identify "personal information encryption obligations."

[0144] 3. The server scans the company's internal document database to identify documents related to the handling of personal information. For example, a document titled "Current storage method (password protection)" is identified.

[0145] 4. The server groups and categorizes the impact ranges related to "storage method" using a text classification algorithm.

[0146] 5. The server compares the new policy, "Personal information is encrypted and stored," with the old policy, "Stored in a password-protected file," and generates a comparison table of the old and new policies.

[0147] 6. The server evaluates the compliance status of the IT and HR departments. It finds that the IT department has already implemented encryption, but the HR department has not.

[0148] 7. The server recommends that the HR department "install encryption software" and the IT department "regularly update encryption protocols."

[0149] 8. The server notifies the terminals of the personnel in charge of each department of the results of the analysis and a comparison table of the old and new data. The users can check these on their terminals and take any necessary action.

[0150] In this way, the system of the present invention provides a means for companies and organizations to respond quickly and efficiently to legal changes and minimize the risk of non-compliance with laws and regulations or omissions.

[0151] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0152] Step 1:

[0153] The server automatically accesses external law databases and related news feeds to collect legal amendment information. Specifically, it uses an API to send queries to the law database and obtain the latest legal amendment information. The input is a query to the law database, and the output is the obtained legal amendment information.

[0154] Step 2:

[0155] The server preprocesses the acquired legal amendment information and converts it into a format suitable for analysis. For example, it removes unnecessary HTML tags and special characters and converts the data into JSON or text format. This process reformats the data and makes it suitable for the next analysis step. The input is the acquired legal amendment information, and the output is the preprocessed legal amendment information.

[0156] Step 3:

[0157] The server analyzes the preprocessed legal amendment information using a generative AI model (e.g., BERT or GPT-3). For example, it issues a prompt statement, "Please tell me the key points of the amendments to the new Personal Information Protection Act," to the generative AI model and analyzes the response. The input is the preprocessed legal amendment information and the prompt statement, and the output is the specific changes in the legal amendments.

[0158] Step 4:

[0159] The server identifies the internal documents affected by the analyzed revisions based on the content of the revisions. Specifically, it uses a full-text search engine (e.g., Elasticsearch) to scan the internal document database and identifies relevant documents based on specific keywords or regular expressions. The input is the analyzed revisions, and the output is the affected internal documents.

[0160] Step 5:

[0161] The server groups and categorizes the identified internal documents. Specifically, it uses a text classification algorithm (e.g., random forest or SVM) to categorize the identified impact areas. The input is the affected internal documents, and the output is a list of documents classified by category.

[0162] Step 6:

[0163] The server automatically generates a comparison table between the old and new regulations. Specifically, it uses the "DiffMatchPatch" library to compare the new and old regulations and format them to clearly show the differences between the old and new. The input is the old and new legal content, and the output is the comparison table.

[0164] Step 7:

[0165] The server evaluates compliance with laws and regulations for each company and department. For example, it queries employee IDs and department information to evaluate the compliance of current measures with new regulations. The input is each department's current data storage protocols and manual processes, and the output is the compliance assessment results for each department.

[0166] Step 8:

[0167] The server provides customized improvement measures and advice based on the results of the compliance assessment. For example, it recommends "installation of the latest encryption software" to the IT department and "encryption process training" to the HR department. The input is the compliance assessment result, and the output is specific improvement measures and advice.

[0168] Step 9:

[0169] The server notifies the user's device of the analysis results, the generated comparison table between old and new versions, and improvement measures. For example, notifications can be sent to the device of the person in charge via email or a dedicated app. The input is the analysis results, the comparison table between old and new versions, and the improvement measures, and the output is a notification to the user.

[0170] In this way, each step works together to create a system that can effectively respond to legal changes.

[0171] (Application example 1)

[0172] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0173] As legal changes become more frequent in companies and organizations, it is difficult to quickly and accurately collect and analyze information on legal changes and immediately implement specific countermeasures based on that information. Another issue is the lack of a means to efficiently identify the scope of impact of the changes and update corporate security policies accordingly.

[0174] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0175] In this invention, the server includes means for automatically collecting legal amendment information, means for analyzing the collected legal amendment information to identify the amendment details, means for identifying internal documents affected by the identified amendment details, means for grouping and categorizing the identified scope of impact, means for automatically generating a comparison table of old and new information, means for evaluating the legal compliance status of each company or department, means for providing customized improvement measures and advice based on the evaluation results, means for notifying the user's terminal of the provided results, means for updating the company's security policy in real time based on the notified results and improvement measures, and means for presenting specific measures to the user regarding the updated security policy. This enables companies to respond quickly and efficiently to legal amendments, strengthen security, and improve legal compliance.

[0176] "Legal Change Information" means information about laws and regulations that have been updated or changed by governments or regulatory authorities.

[0177] A "collection method" is a mechanism by which the server automatically retrieves information from external databases and related feeds.

[0178] The "means of analysis" refers to a mechanism that uses algorithms or generative AI models to process collected legal amendment information and identify the amendments.

[0179] "Internal documents" are official documents such as reports, guidelines, and procedures used within a company.

[0180] A "grouping and categorization method" is an algorithm for classifying and organizing identified impact areas based on common characteristics.

[0181] A "comparison table of old and new laws" is a document that displays a comparison of the contents of laws and regulations before and after the change.

[0182] "Assessment tools" are mechanisms for determining how well a company or department currently complies with the law.

[0183] "Customized improvement measures and advice" refers to specific improvement methods and suggestions for each organization or department based on the results of the compliance assessment.

[0184] The "notification means" is a communication function for sending analysis results and improvement measures to the user's terminal.

[0185] "Real-time" refers to a time frame within which immediate action can be taken based on the acquisition and analysis of legal change information.

[0186] A "security policy" is a set of operational rules and standards established by a company to protect information.

[0187] "Specific measures" are concrete actions or policies that should be implemented to address a specific problem.

[0188] The present invention provides a system that automatically collects and analyzes legal amendment information, enabling companies and organizations to respond to legal amendments quickly and accurately. This system includes functions such as collecting legal amendment information, analyzing it, identifying the scope of impact, generating a comparison table of the old and new amendments, evaluating compliance with laws and regulations, and providing customized improvement measures and advice. Below, we will explain in detail how each of these steps is implemented.

[0189] The server uses the requests library to automatically access external legal databases and related news feeds to retrieve the latest legal changes, a process that reduces the effort required for companies to manually gather information.

[0190] The collected information is preprocessed using the transformers library to convert it into a format suitable for analysis, and then a generative AI model (e.g., BERT) is used to analyze the legal changes and identify specific amendments.

[0191] To identify the scope of impact, the system scans the company's internal documents and operational rules database, and uses full-text search and regular expressions to identify the areas affected by the identified revisions.The system then uses a text classification algorithm to group and categorize the identified scope of impact, allowing impacts belonging to the same category to be displayed together.

[0192] The server automatically generates a comparison table by comparing the old and new legal content. This comparison table clearly visualizes which parts of the company will be changed and how.

[0193] The server evaluates the compliance status of each company and each department. Based on the results of this evaluation, specific improvement measures and advice customized for each company and department are generated, which helps improve compliance.

[0194] Finally, the server sends the generated analysis results, a comparison table of the old and new versions, and improvement measures to the user's device, where the user can check this information and take any necessary measures.

[0195] As a concrete example, consider the case where a new cybersecurity law has been enacted, resulting in stricter data encryption standards. The server retrieves information about the new cybersecurity law from a legal database, preprocesses the collected information, and then uses a generative AI model to identify "strengthened data encryption standards." Next, it scans the company's internal document database to identify documents related to data encryption. These documents describe the current encryption methods.

[0196] The server groups and categorizes the scope of impact related to "data encryption." It then compares the new regulation, "Enhanced Data Encryption Standard," with the old regulation, "Current Encryption Standard," and generates a comparison table of the old and new standards. The server evaluates the compliance status of the IT department and each department, and based on the evaluation results, recommends that the IT department "introduce new encryption software" and each department "update encryption protocols." The analysis results and the comparison table of the old and new standards are sent to the terminals of the personnel in each department, allowing users to check them on their terminals and take any necessary action.

[0197] This system allows companies and organizations to quickly identify the scope of impact of legal changes and take effective measures. Furthermore, the use of generative AI models and prompt sentences improves analysis accuracy and efficiency, enabling immediate updates to corporate security policies.

[0198] Example prompt sentence:

[0199] "The new Cybersecurity Law has tightened data encryption standards. How will this change affect a company's information security policy? Please suggest specific measures."

[0200] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0201] Step 1:

[0202] The server automatically collects legal change information.

[0203] Input: Legal database URL and related news feed URL.

[0204] Specific operation: Uses the requests library to periodically access legal databases and news feeds to obtain the latest legal changes.

[0205] Output: JSON data of legal amendment information.

[0206] Step 2:

[0207] The server preprocesses the collected legal amendment information and converts it into a format suitable for analysis.

[0208] Input: JSON data of legal amendment information obtained in Step 1.

[0209] Specific operation: The acquired JSON data is structured and converted into a format (text format or token format) that is easy for the generative AI model to parse.

[0210] Output: Preprocessed text data of legal amendment information.

[0211] Step 3:

[0212] The server analyzes the preprocessed legal amendment information using a generative AI model to identify the amendment content.

[0213] Input: Preprocessed text data of legal amendment information.

[0214] What it does: Using the transformers library, we analyze the tokenized data using the BERT model to identify specific modifications.

[0215] Output: Data identifying the amendments (e.g., a list of amendments).

[0216] Step 4:

[0217] The server identifies the internal documents that will be affected by the identified revisions.

[0218] Input: Data identifying amendments and the company's internal document database.

[0219] Specific operation: Using full-text search and regular expressions, internal documents containing revision details are automatically found.

[0220] Output: A list of internal documents that fall within the scope of impact.

[0221] Step 5:

[0222] The server groups and categorizes the impact areas.

[0223] Input: List of internal documents within the scope of impact.

[0224] What it does: Uses a text classification algorithm to group and categorize impact areas based on common characteristics.

[0225] Output: A list of impact areas, grouped and categorized.

[0226] Step 6:

[0227] The server automatically generates a comparison table of the old and new versions.

[0228] Input: List of internal documents included in the amendment and their scope of impact.

[0229] Specific actions: Compare the new and current legal content and create a comparison table.

[0230] Output: Comparison table of old and new versions.

[0231] Step 7:

[0232] The server evaluates compliance with laws and regulations for each company and department.

[0233] Input: Old and new comparison table and company compliance status data.

[0234] Specific operation: Using an evaluation algorithm, determine the current compliance status.

[0235] Output: A report of the evaluation results.

[0236] Step 8:

[0237] The server provides customized remedial measures and advice based on the evaluation results.

[0238] Input: Report of evaluation results.

[0239] Specific actions: Based on the evaluation results, specific improvement measures and advice are generated for each department.

[0240] Output: A customized list of remediation measures and advice.

[0241] Step 9:

[0242] The server notifies the user's device of the analysis results, a comparison table of the old and new data, and improvement measures.

[0243] Input: A customized list of remedies and advice.

[0244] Specific operation: Send a notification to the user's device.

[0245] Output: A notification message visible to the user's device.

[0246] Step 10:

[0247] The server updates the company's security policy in real time based on the notified results and remedial measures.

[0248] Input: Notifications and remediation actions reviewed by the user.

[0249] Specific behavior: Integrates with the company's security management system and reflects necessary changes in real time.

[0250] Output: Updated security policy.

[0251] Step 11:

[0252] The server presents specific measures to the user regarding the updated security policy.

[0253] Input: The updated security policy.

[0254] Specific action: Specific countermeasures are notified to the user's device.

[0255] Output: A notification message with specific countermeasures that can be viewed on the user's device.

[0256] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0257] This invention provides a system that automatically collects and analyzes legal amendment information, enabling companies and organizations to respond to legal amendments quickly and accurately. Furthermore, by combining it with an emotion engine, it provides notifications and remedial measures that take into account the user's emotional state. This system includes multiple processing steps involving a server, terminals, and users.

[0258] About program processing

[0259] 1. Collecting information on legal reforms

[0260] The server automatically accesses external legal databases and related news feeds to obtain the latest legal information. Using APIs and crawling tools, the data is collected and stored in a database.

[0261] 2. Analysis of legal reforms

[0262] The server preprocesses the legal amendment information it obtains, normalizing the data, filtering unnecessary information, standardizing formats, etc. It then analyzes the preprocessed legal amendment information using a generative AI model (e.g., BERT or GPT-3) to identify specific amendments.

[0263] 3. Identifying the scope of impact

[0264] The server accesses the company's internal document database and identifies the affected documents and operational rules based on the analysis results. The affected range is extracted using full-text search and regular expressions.

[0265] 4. Grouping and categorizing relevant sections

[0266] The affected servers are grouped using a text classification algorithm and categorized into categories such as "method of acquisition," "method of storage," and "method of use."

[0267] 5. Generate a comparison table of old and new versions

[0268] The server compares the old and new legal content and automatically generates a comparison table, which clearly shows which parts of the company will be changed and how.

[0269] 6. Compliance Assessment

[0270] The server evaluates the legal compliance status of each company and each department. Based on the information in the database, it evaluates the degree to which each department complies with the law using numerical values ​​and indicators.

[0271] 7. Providing customized advice

[0272] Based on the assessment results, the server generates specific improvement measures and advice customized for each department, including measures tailored to each department's specific situation.

[0273] 8. User Emotion Recognition

[0274] The server uses an emotion engine to analyze the user's emotional state, using voice and text data collected from the user. Based on the analysis, the user's stress and fatigue state are identified.

[0275] 9. Emotion-Based Notification Adjustment

[0276] The server adjusts the notification content and remedial measures based on the user's emotional state. For example, for a user in a high stress state, the server may simplify the notification content or reduce the number of suggestions.

[0277] 10. Notification optimization

[0278] The server optimizes the timing and format of notifications based on the analysis results of the emotion engine, sending notifications at the optimal time to suit the user's work schedule.

[0279] 11. User Notice and Confirmation

[0280] The server notifies the user's device of the analysis results, the generated comparison table of old and new data, and improvement measures. The user can check this information on their device and take any necessary measures.

[0281] 12. Progress Management

[0282] Users update the progress status within the system. For items that have been completed, the status is updated within the system to manage the progress of improvements to legal compliance.

[0283] Specific examples

[0284] Example 1: New Personal Information Protection Law and Emotion Recognition

[0285] 1. Collecting information on legal reforms

[0286] The server retrieves information about the new Personal Information Protection Act from the legislation database.

[0287] 2. Analysis of legal reforms

[0288] The server performs preprocessing and uses a generative AI model to identify "personal information encryption obligations."

[0289] 3. Identifying the scope of impact

[0290] The server scans the company's internal document database to identify documents related to the handling of personal information. The old policy states that these documents should be stored in a password-protected file.

[0291] 4. Grouping and categorizing relevant sections

[0292] The server groups and categorizes the scope of impact regarding "storage method."

[0293] 5. Generate a comparison table of old and new versions

[0294] The server compares the new rule, "Personal information is encrypted and stored," with the old rule, "Stored in a password-protected file," and generates a comparison table of the old and new rules.

[0295] 6. Compliance Assessment

[0296] The server evaluates compliance with IT and HR departments. IT department has implemented encryption, but HR department has not.

[0297] 7. Providing customized advice

[0298] The server recommends that the HR department "install encryption software" and the IT department "regularly update encryption protocols."

[0299] 8. User Emotion Recognition

[0300] The server uses an emotion engine to analyze the voice and text data of the HR department staff member and identify their stress level. It turns out that they are experiencing high levels of stress.

[0301] 9. Emotion-Based Notification Adjustment

[0302] Based on the results of the emotion engine, the server provides HR personnel with simplified notifications and a small number of suggestions.

[0303] 10. Notification optimization

[0304] The server sends a notification after the break according to the person's work schedule.

[0305] 11. User Notice and Confirmation

[0306] The server notifies the terminals of the personnel in each department of the results of the analysis and a comparison table of the old and new data. The users can then check these on their terminals and take any necessary action.

[0307] 12. Progress Management

[0308] Users update their progress within the system and manage compliance improvements.

[0309] This system allows companies and organizations to respond to legal changes quickly and efficiently, minimizing the risk of legal violations and omissions. In addition, by utilizing the emotion engine, users can receive notifications in the most optimal state and carry out their work efficiently.

[0310] The processing flow will be explained below.

[0311] Step 1:

[0312] The server accesses external legal databases and related news feeds to collect legal amendment information. It automatically obtains the latest legal amendment information using APIs and crawling tools and stores it in the database.

[0313] Step 2:

[0314] The server performs preprocessing of the legal amendment information it obtains, normalizing the data, filtering out unnecessary information, standardizing the format, and converting it into a format suitable for analysis.

[0315] Step 3:

[0316] The server analyzes the preprocessed legal amendment information using a generative AI model (e.g., BERT or GPT-3), identifying specific amendments and their scope of application, and extracting them as text.

[0317] Step 4:

[0318] The server accesses the company's internal document database and identifies the documents and operational rules that will be affected based on the analysis results. Related text is extracted using full-text search and regular expressions.

[0319] Step 5:

[0320] The server uses a text classification algorithm to group the identified impact areas and categorize them into categories such as "acquisition method," "storage method," and "usage method."

[0321] Step 6:

[0322] The server compares the old and new legal content and automatically generates a comparison table, which clearly shows which parts of the company will be changed and how.

[0323] Step 7:

[0324] The server-generated comparison table is verified to check for omissions and duplications. An algorithm is used to automatically check and make any necessary corrections.

[0325] Step 8:

[0326] The server evaluates the legal compliance status of each company and department, and evaluates the degree to which each department complies with laws and regulations using numerical values ​​and indicators based on the information in the database.

[0327] Step 9:

[0328] Based on the assessment results, the server generates specific improvement measures and advice customized for each department, including measures tailored to the specific circumstances of each department.

[0329] Step 10:

[0330] The server uses an emotion engine to analyze the user's emotional state. It uses voice and text data collected from the user to identify the emotional state. For example, it uses voice analysis and natural language processing to determine the user's stress level and emotional state.

[0331] Step 11:

[0332] The server adjusts the notification content and improvement measures based on the user's emotional state. For users in a high stress state, the server will simplify the notification content and reduce the number of suggestions. In addition, notifications with low urgency will be postponed, allowing the user to respond in a more appropriate state.

[0333] Step 12:

[0334] The server optimizes the timing and format of notifications based on the analysis results of the emotion engine. Notifications are sent at optimal times according to the user's work schedule and emotional state. For example, notifications can be sent outside peak work hours to reduce the burden on the user.

[0335] Step 13:

[0336] The server notifies the user's device of the analysis results, the generated comparison table of old and new data, and improvement measures. Information is provided promptly via email or push notification. After receiving the notification, the user can check the detailed information on their device and take any necessary measures.

[0337] Step 14:

[0338] Users update the progress status within the system. For items that have been completed, the status is updated within the system to manage the progress of improvements to legal compliance.

[0339] Example 2

[0340] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0341] Modern companies and organizations need to respond quickly and accurately to frequent legal changes, but conventional manual processes require a huge amount of time and effort. This often increases the workload of those in charge, leading to stress and reduced efficiency. This raises concerns about the risk of legal violations and inefficiencies. There is a need for a system that can solve this problem and improve operational efficiency while maintaining legal compliance.

[0342] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for automatically collecting legal amendment information, means for analyzing the collected legal amendment information to identify the amendment content, means for identifying internal documents affected by the identified amendment content, means for grouping and categorizing the identified scope of impact, means for automatically generating a comparison table of old and new versions, means for evaluating the legal compliance status of each organization or department, means for providing customized improvement measures and advice based on the evaluation results, means for notifying the user of the provided results to the user's terminal, means for analyzing the user's emotional state and adjusting the notification content and improvement measures, and means for optimizing the timing and format of notifications. This enables companies and organizations to respond quickly and accurately to legal amendments, reduce the user's workload, and enable efficient and effective legal compliance.

[0343] "Means for automatically collecting information on legal amendments" refers to technology that automatically obtains the latest information on legal amendments from external legal databases and news feeds and stores it in a dedicated database.

[0344] "Means of analyzing collected legal amendment information and identifying the amendment content" refers to technology that preprocesses collected legal amendment information and analyzes and identifies specific amendment points using a generative AI model.

[0345] The "means for identifying internal documents affected by the identified revisions" refers to a technology for searching and identifying relevant documents in a company's internal document database based on the identified revisions.

[0346] "Means for grouping and categorizing the identified impact scope" refers to a technique for classifying the affected documents based on specific criteria and grouping them into categories.

[0347] "Means for automatically generating a comparison table between the old and new laws" refers to technology that compares the contents of the old and new laws and regulations and automatically generates a comparison table that clearly shows the differences.

[0348] "Means for assessing compliance with laws and regulations for each organization or department" refers to technology that evaluates compliance with laws and regulations within a company or each department based on numerical values ​​and indicators and displays the results visually.

[0349] "Means for providing customized improvement measures and advice based on the evaluation results" refers to a technology that provides specific improvement measures and advice individually to each department based on the evaluation results of compliance with laws and regulations.

[0350] "Means for notifying the user of the provided results" refers to technology for notifying the user of the generated analysis results and improvement measures.

[0351] "Means for analyzing the user's emotional state and adjusting notification content and suggested improvements" refers to technology that analyzes collected voice and text data to identify the user's emotional state and adjusts notification content and suggested improvements based on the analysis results.

[0352] "Means for optimizing the timing and format of notifications" refers to technology that delivers notifications at the most appropriate timing and in the most appropriate format, taking into account the user's work schedule and emotional state.

[0353] This invention provides a system that automatically collects and analyzes legal amendment information, enabling companies and organizations to respond to legal amendments quickly and accurately. Furthermore, by combining it with an emotion engine, it provides notifications and remedial measures that take into account the user's emotional state. This system includes multiple processing steps involving a server, terminals, and users.

[0354] First, the server automatically accesses external legal databases (e.g., GovInfo API or general news feeds) and uses APIs or crawling tools (e.g., Scrapy, Requests) to obtain the latest legal amendment information. The collected information is stored in a dedicated database (e.g., MySQL (registered trademark), PostgreSQL).

[0355] Next, the server analyzes the collected legal amendment information. Specifically, it performs preprocessing such as text normalization, filtering unnecessary information, and standardizing data formats. The preprocessed data is used as a prompt for a generative AI model (e.g., GPT-3, BERT), which analyzes the legal amendments and identifies specific amendments. For example, specific amendments such as "the new Personal Information Protection Act requires the encryption of personal information" may be identified.

[0356] The server then accesses the company's internal document database (e.g., Elasticsearch) to identify documents and operational rules that are affected by the identified legal changes. Using full-text search and regular expressions, it identifies documents that state, for example, that the old regulations should be "stored in a password-protected file." This identified scope of impact is then classified into categories such as "storage method" using a text classification algorithm (e.g., tf-idf, LDA).

[0357] When the server generates the comparison table, it compares the old and new legal provisions. For example, it compares the new provision "Personal information shall be encrypted and stored" with the old provision "Stored in a password-protected file," and automatically generates the comparison table. A diff tool (e.g., the Python library difflib) is used to generate this table.

[0358] To assess a company's compliance with laws and regulations, the server uses the information in the database to evaluate the compliance status of each department using numerical values ​​and indicators, and uses a dashboard tool (e.g., Tableau or Power BI) to visually display the results. For example, the server displays the results of the evaluation, such as showing that the IT department has already implemented encryption, but the HR department has not yet done so.

[0359] The server then provides customized advice and remediation measures based on the assessment results, including specific recommendations tailored to the situation of each department. For example, the server might recommend "regularly updating encryption protocols" for the IT department, or "implementing encryption software" for the HR department.

[0360] Furthermore, to understand the user's emotional state, the server performs analysis using an emotion engine (e.g., IBM Watson (registered trademark), Azure (registered trademark) Emotion API). The server analyzes the voice and text data collected from the user to identify their stress and fatigue levels. For example, the emotion engine may identify that a human resources department employee is experiencing high levels of stress.

[0361] Based on the results, the server adjusts the content of notifications and improvement measures. Based on the analysis results of the emotion engine, adjustments are made such as simplifying the content of notifications or reducing the number of suggestions for users who are in a high state of stress. The timing of notifications is also optimized, taking into account the user's work schedule and emotional state. For example, the server checks the work schedule of the person in charge and sends a notification after an appropriate break.

[0362] Finally, the server notifies the user's device of the analysis results, the generated comparison table of old and new versions, and improvement measures. The user can check this information on their device and take any necessary actions. Progress is updated within the system, and the status of items for which action has been completed is updated, allowing the improvement in compliance with laws and regulations to be managed.

[0363] A specific example is the enforcement of a new Personal Information Protection Act. The server collects information about the new Personal Information Protection Act and uses an analytical model to identify the "obligation to encrypt personal information." The internal document database is then scanned to identify and categorize the affected documents. A comparison table of the old and new documents is generated, and the IT and HR departments are assessed for compliance with the law, providing advice to each department. An emotion engine is used to analyze the stress level of employees, and notifications are sent at the appropriate time and in the appropriate format.

[0364] The above processing steps enable companies to respond quickly and accurately to legal changes, preventing legal violations and a decline in business efficiency. In addition, the use of an emotion engine reduces the burden on users and enables them to carry out their work in an optimal state.

[0365] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0366] Step 1:

[0367] The server collects legal amendment information from external legal databases and news feeds. As input, it uses API requests or crawling tools (e.g., Scrapy, Requests) to obtain the latest legal amendment information. The output is the obtained legal amendment information, which is stored in a dedicated database (e.g., MySQL, PostgreSQL).

[0368] Step 2:

[0369] The server analyzes the legal amendment information collected. The input is preprocessed by normalizing the text, filtering unnecessary information, and standardizing the format. Specifically, a text cleaning library (e.g., NLTK, spaCy) is used. The output is the preprocessed data, which is used as a prompt for a generative AI model (e.g., GPT-3, BERT).

[0370] Step 3:

[0371] The server uses the generative AI model to analyze the preprocessed legal amendment information and identify the specific amendments. The input is the data preprocessed in step 2. The output is the identified specific legal amendments. For example, the new Personal Information Protection Act identifies the "obligation to encrypt personal information."

[0372] Step 4:

[0373] The server accesses the company's internal document database and identifies documents affected by the identified legal changes. The input is the legal changes identified in step 3. This process uses full-text search and regular expressions (e.g., Elasticsearch). The output is the specific internal documents that are affected. For example, documents that state "save in a password-protected file" are identified.

[0374] Step 5:

[0375] The server groups and categorizes the identified impact scope documents. The input is the impact scope documents identified in step 4. A text classification algorithm (e.g., tf-idf, LDA) is used to classify the documents into categories such as "storage method." The output is the classified impact scope documents.

[0376] Step 6:

[0377] The server compares the old and new legal documents and generates a comparison table. The input is the old and new legal documents. Specifically, a diff tool (e.g., the Python library difflib) is used to automatically generate the comparison table. The output is the comparison table. This clearly shows which parts of the company have been changed and how.

[0378] Step 7:

[0379] The server evaluates the legal compliance status for each department of the company. The input is the legal compliance status data stored for each department of the company. Based on this data, compliance status is evaluated using numerical values ​​and indicators, and a dashboard tool (e.g., Tableau, Power BI) is used to visually display the results. The output is the evaluation results of legal compliance status for each department.

[0380] Step 8:

[0381] Based on the assessment results, the server provides customized advice and improvement measures for each department. The input is the assessment results obtained in step 7. Specific improvement measures tailored to the specific circumstances of each department are included. The output is customized advice and improvement measures. For example, the server may recommend "regularly updating encryption protocols" for the IT department and "implementing encryption software" for the HR department.

[0382] Step 9:

[0383] The server uses an emotion engine to analyze the user's emotional state. The input is voice and text data collected from the user. Analysis is performed using an emotion engine (e.g., IBM Watson, Azure Emotion API) to identify the user's stress and fatigue state. The output is the analyzed user's emotional state.

[0384] Step 10:

[0385] The server adjusts the notification content and remedial measures based on the user's emotional state. The input is the user's emotional state obtained in step 9. For users in a high stress state, the notification content is simplified or the suggestions are reduced. The output is the adjusted notification content and remedial measures.

[0386] Step 11:

[0387] The server sends the notification at the optimal timing and in the optimal format. The input is the notification content adjusted in step 10 and the user's work schedule. The output is the notification sent to the user's device. For example, the notification may be sent after a break, taking into account the person's work schedule.

[0388] Step 12:

[0389] The user checks the notified content and takes any necessary action. The input is the notification content sent from the server. Based on this, the user takes specific action to comply with the law and updates the progress status within the system. The output is the updated progress status. For items for which action has been completed, the status is updated within the system and the improvement in compliance with the law is managed.

[0390] (Application example 2)

[0391] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0392] In order for companies and organizations to respond quickly and accurately to legal changes, they need a system that automatically collects and analyzes the latest legal change information. However, with conventional systems, the process of identifying which parts of the company the change will affect and providing appropriate countermeasures is complicated, resulting in delayed responses. Furthermore, information is provided with uniform notification content and timing without taking into account the emotional state of the user receiving the notification, which increases the burden on users and risks reducing work efficiency. There is a need to solve these issues and provide a system that allows companies to respond quickly and accurately to legal change information.

[0393] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0394] In this invention, the server includes means for automatically collecting legal amendment information, means for analyzing the collected legal amendment information to identify the amendment details, means for identifying internal documents affected by the identified amendment details, means for grouping and categorizing the identified scope of impact, means for automatically generating a comparison table of old and new versions, means for evaluating the legal compliance status of each company or department, means for providing customized improvement measures and advice based on the evaluation results, means for notifying the user of the provided results to their terminal, means for analyzing the user's emotional state, means for adjusting the content and timing of notifications based on the user's emotional state, and means for managing progress. This enables companies to respond to legal amendments quickly and efficiently and reduces the burden on users by taking their emotional state into consideration.

[0395] "Means for automatically collecting information on legal amendments" refers to a function that accesses external legal databases and related news feeds, automatically obtains the latest legal amendment information, and stores it in a database.

[0396] The "means of analyzing collected legal amendment information and identifying the amendment content" refers to a function that normalizes and filters collected data and uses a generative AI model to extract specific amendment points.

[0397] "Means to identify internal documents that will be affected by the identified revisions" refers to a function that accesses a company's internal document database and identifies the documents and operational rules that will be affected based on the analysis results.

[0398] The "means for grouping and categorizing the identified impact ranges" is a function for grouping and categorizing the identified impact ranges using a text classification algorithm.

[0399] The "means for automatically generating a comparison table between the old and new laws and regulations" is a function that automatically generates a comparison table that compares the contents of the old and new laws and regulations and clearly shows which parts have been changed and how.

[0400] "Means for evaluating compliance with laws and regulations for each company and department" is a function that evaluates the degree to which each department complies with laws and regulations using numerical values ​​and indicators based on information in the database.

[0401] "Means for providing customized improvement measures and advice based on the evaluation results" refers to a function that generates specific improvement measures and advice tailored to the specific circumstances of each department based on the evaluation results.

[0402] The "means for notifying the user of the provided results" is a function for notifying the user of the analysis results, the generated comparison table between the old and new data, and improvement measures to the user's terminal.

[0403] The "means for analyzing the user's emotional state" is a function that analyzes voice and text data collected from the user and identifies the user's stress and fatigue state using an emotion engine.

[0404] "Means for adjusting notification content and timing based on emotional state" refers to a function that optimizes the timing and format of notifications by taking into account the user's emotional state, simplifying the notification content, or reducing the amount of suggested content.

[0405] The "means for managing progress" is a function that updates the progress of items handled by the user on the system and manages the progress of improvements in compliance with laws and regulations.

[0406] This invention is a system that automatically collects and analyzes legal amendment information, enabling companies and organizations to respond to legal amendments quickly and accurately. Furthermore, by combining it with an emotion engine, it is possible to provide notifications and remedial measures that take into account the user's emotional state. This system is configured using the following means.

[0407] 1. Collecting information on legal reforms

[0408] The server automatically accesses external legal databases and related news feeds to obtain the latest legal information. Specific technologies used include APIs and crawling tools. The obtained data is stored on the server.

[0409] 2. Analysis of legal reforms

[0410] The server normalizes and filters the collected data and identifies specific corrections using generative AI models (e.g., BERT or GPT-3). The data preprocessing stage removes unnecessary information and standardizes the format.

[0411] 3. Identifying the scope of impact

[0412] The server accesses the company's internal document database and identifies the documents and operational rules that will be affected based on the analysis results. It then uses full-text search and regular expressions to extract the relevant documents from the database.

[0413] 4. Grouping and Categorization

[0414] The server uses a text classification algorithm to group the identified impact areas into categories such as "acquisition method," "storage method," and "usage method."

[0415] 5. Generate a comparison table of old and new versions

[0416] The server compares the old and new legal content and automatically generates a comparison table, which clearly shows which parts have been changed and how.

[0417] 6. Compliance Assessment

[0418] The server evaluates the legal compliance status of each company and each department, and based on the information in the database, evaluates the degree to which each department complies with the law using numerical values ​​and indicators.

[0419] 7. Providing customized advice

[0420] Based on the evaluation results, the server generates specific improvement measures and advice tailored to each department's specific situation.

[0421] 8. User Emotion Recognition

[0422] The server uses an emotion engine to analyze the user's emotional state, using voice and text data collected from the user. Based on the analysis, the user's stress and fatigue state are identified.

[0423] 9. Adjustment of notification content and timing

[0424] The server optimizes the timing and format of notifications by simplifying the content of notifications or reducing the number of suggestions, taking into account the user's emotional state.

[0425] 10. User Notice and Confirmation

[0426] The server notifies the user's device of the analysis results, the generated comparison table of old and new data, and improvement measures. The user can check this information on their device and take the necessary measures.

[0427] 11. Progress Management

[0428] Users can update the progress of their actions within the system and manage their compliance improvement.

[0429] This system is built using cloud servers (e.g., AWS (registered trademark), Google (registered trademark) Cloud) and external APIs (legal database API, sentiment analysis API). BERT and GPT-3 are used as generative AI models.

[0430] Examples:

[0431] The new Personal Information Protection Law requires employees' personal information to be encrypted rather than kept in password-protected files. The system automatically detects this and advises IT departments to regularly update their existing encryption systems and HR departments to implement encryption software. It also uses emotion recognition to streamline notifications for stressed users.

[0432] Example prompts to input to a generative AI model:

[0433] "Analyze the latest changes to the Personal Information Protection Act and identify how they affect your company's internal documents."

[0434] This system allows companies and organizations to respond quickly and efficiently to legal changes and reduces the burden on users by taking into account their emotional state.

[0435] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0436] Step 1:

[0437] Automatic collection of legal reform information

[0438] The server automatically accesses external legal databases and news feeds to obtain the latest legal amendment information. Specifically, it sends API requests and stores the obtained data in an internal database. The input is the API request, and the output is legal amendment information data.

[0439] Step 2:

[0440] Analysis of legal reforms

[0441] The server normalizes the collected legal amendment information and filters unnecessary information. Next, it uses a generative AI model (e.g., BERT or GPT-3) to identify specific amendments. The input is the collected legal amendment information, and the output is the analyzed amendment content. The specific operations are text normalization, filtering, and application of the AI ​​model.

[0442] Step 3:

[0443] Identifying the scope of impact

[0444] The server accesses the company's internal document database and identifies the affected documents and operational rules based on the analysis results. Specifically, it extracts the relevant documents from the database using full-text search and regular expressions. The input is the analyzed revision details, and the output is a list of affected internal documents.

[0445] Step 4:

[0446] Grouping and Categorization

[0447] The server uses a text classification algorithm to group the identified impact areas and categorize them into categories such as acquisition method, storage method, and usage method. The input is a list of affected internal documents, and the output is a categorized document list. The specific operation is the application of a text classification algorithm.

[0448] Step 5:

[0449] Generate a comparison table of old and new versions

[0450] The server compares the old and new legal content and automatically generates a comparison table. The input is the old and new legal content, and the output is the comparison table. The specific operation is to compare data and generate a format.

[0451] Step 6:

[0452] Compliance assessment

[0453] The server evaluates the legal compliance status of each company and each department based on the information in the database. The input is a categorized document list and operational data within the company, and the output is the evaluation result of the legal compliance status. Specific operations include analyzing the data and generating evaluation indicators.

[0454] Step 7:

[0455] Providing customized advice

[0456] Based on the evaluation results, the server generates specific improvement measures and advice tailored to the specific circumstances of each department. The input is the evaluation results of compliance status, and the output is advice customized for each department. The specific operation is to analyze the evaluation results and generate advice.

[0457] Step 8:

[0458] User Emotion Recognition

[0459] The server uses an emotion engine to analyze the user's emotional state. The input is voice and text data collected from the user, and the output is the analysis result of the emotional state. The specific operation involves analyzing the voice and text data and applying the emotion recognition engine.

[0460] Step 9:

[0461] Adjust notification content and timing

[0462] The server optimizes the timing and format of notifications by simplifying the content of notifications or reducing the number of suggestions, taking into account the user's emotional state. The input is the analysis result of the emotional state and advice on improvement measures, and the output is the adjusted notification content. The specific operation is to edit the notification content and adjust the timing.

[0463] Step 10:

[0464] User notification and confirmation

[0465] The server notifies the user's device of the analysis results, the generated comparison table of old and new data, and improvement measures. The input is the adjusted notification content, and the output is the notification information displayed on the user's device. The specific operation is to send and display the notification.

[0466] Step 11:

[0467] Progress Management

[0468] Users update the progress of corresponding items within the system and manage the progress of improvements to legal compliance. The input is the user's updated progress data, and the output is the updated progress data. The specific operation is to update the progress database.

[0469] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0470] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0471] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0472] [Second embodiment]

[0473] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0474] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0475] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0476] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0477] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0478] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0479] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0480] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0481] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0482] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0483] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0484] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0485] The present invention provides a system that automatically collects and analyzes legal amendment information, enabling companies and organizations to respond to legal amendments quickly and accurately. This system includes multiple processing steps involving servers, terminals, and users.

[0486] About program processing

[0487] 1. Collecting information on legal reforms

[0488] The server automatically accesses external legal databases and related news feeds to retrieve the latest legal information, eliminating the need for companies to manually gather information.

[0489] 2. Analysis of legal reforms

[0490] The server preprocesses the legal amendment information it obtains and converts it into a format suitable for analysis. It then uses a generative AI model (e.g., BERT or GPT-3) to analyze the legal amendments and identify the specific amendments.

[0491] 3. Identifying the scope of impact

[0492] The server scans the company's internal documents and operational rules database to automatically identify areas that the identified revisions will affect, using full-text search and regular expressions in the process.

[0493] 4. Grouping and categorizing relevant sections

[0494] The server groups and categorizes the identified impact areas using a text classification algorithm, so that impact areas that belong to the same category are displayed together.

[0495] 5. Generate a comparison table of old and new versions

[0496] The server compares the old and new legal content and automatically generates a comparison table that clearly visualizes which parts of the company will be changed and how.

[0497] 6. Compliance Assessment

[0498] The server evaluates the legal compliance status of each company and each department. The evaluation results indicate the extent to which the company complies with laws and regulations and allow the user to understand the progress of necessary measures.

[0499] 7. Providing customized advice

[0500] Based on the evaluation results, the server generates specific improvement measures and advice customized for each company or department, thereby improving compliance with laws and regulations.

[0501] 8. User Notification and Confirmation

[0502] The server notifies the user's device of the analysis results, the generated comparison table of the old and new versions, and improvement measures. The user can check this information on their own device and take any necessary measures.

[0503] Specific examples

[0504] Example 1: Enactment of the new Personal Information Protection Law

[0505] 1. Collecting information on legal reforms

[0506] The server retrieves information about the new Personal Information Protection Act from the legislation database.

[0507] 2. Analysis of legal reforms

[0508] The server preprocesses the information it receives and then uses a generative AI model to identify "personal information encryption obligations."

[0509] 3. Identifying the scope of impact

[0510] The server scans the company's internal document database to identify documents related to the handling of personal information, which contain information about the current storage method (password protection).

[0511] 4. Grouping and categorizing relevant sections

[0512] The server groups and categorizes the scope of influence regarding the "storage method."

[0513] 5. Generate a comparison table of old and new versions

[0514] The server compares the new rule, "Personal information is encrypted and stored," with the old rule, "Stored in a password-protected file," and generates a comparison table of the old and new rules.

[0515] 6. Compliance Assessment

[0516] The server evaluates the compliance status of the IT and HR departments. It finds that the IT department has already implemented encryption, but the HR department has not.

[0517] 7. Providing customized advice

[0518] The server recommends that the HR department "install encryption software" and the IT department "regularly update encryption protocols."

[0519] 8. User Notification and Confirmation

[0520] The server notifies the terminals of the personnel in each department of the results of the analysis and a comparison table of the old and new data. The users can then check these on their terminals and take any necessary action.

[0521] This system allows companies and organizations to respond quickly and efficiently to legal changes and minimize the risk of non-compliance with laws and regulations or omissions.

[0522] The processing flow will be explained below.

[0523] Step 1:

[0524] The server accesses external legal databases and related news feeds to collect legal amendment information. Using APIs and crawling tools, the latest legal amendment information is automatically retrieved and stored in the database.

[0525] Step 2:

[0526] The server preprocesses the acquired legal amendment information, normalizing the data, filtering unnecessary information, standardizing the format, and converting it into a format suitable for analysis.

[0527] Step 3:

[0528] The server analyzes the preprocessed legal amendment information using a generative AI model (e.g., BERT or GPT-3), identifying specific amendments and their scope of application, and extracting them as text.

[0529] Step 4:

[0530] The server accesses the company's internal document database and identifies the documents and operational rules that will be affected based on the analysis results. It then uses full-text search and regular expressions to extract relevant text.

[0531] Step 5:

[0532] The server groups and categorizes the identified impact areas, using a text classification algorithm to categorize the impact areas into categories such as "acquisition method," "storage method," and "usage method."

[0533] Step 6:

[0534] The server generates an initial version of the comparison table, comparing the old and new legal content and creating a comparison table in a format that clearly shows the changes for each article.

[0535] Step 7:

[0536] The server verifies the old and new tables to check for omissions and duplications, automatically checking using an algorithm and making any necessary corrections.

[0537] Step 8:

[0538] The server evaluates the legal compliance status of each company and department, and evaluates the degree to which each department complies with laws and regulations using numerical values ​​and indicators based on the information in the database.

[0539] Step 9:

[0540] Based on the results of the assessment, the server generates customized improvement measures and advice, such as proposing specific measures such as "installing encryption software" and "performing regular audits" for each department.

[0541] Step 10:

[0542] The server notifies the user's device of the analysis results, the generated comparison table of old and new data, and improvement measures. Information is provided promptly via email or push notification.

[0543] Step 11:

[0544] The user checks the notification on the device, reviews the analysis results, comparison table of old and new data, and improvement measures, and takes action to implement the necessary measures.

[0545] Step 12:

[0546] Users update the progress status within the system. For items that have been completed, the status is updated within the system to manage the progress of improvements to legal compliance.

[0547] Example 1

[0548] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0549] In order for companies and organizations to respond quickly and accurately to legal changes, they must manually collect and analyze vast amounts of legal change information and identify the extent of impact within the company. This requires significant human resources and time, and specialized knowledge is also required to evaluate compliance and propose improvement measures. This puts companies and organizations at risk of violating laws and regulations and is likely to suffer disadvantages due to delayed response. To solve these issues, a system is needed that automatically collects and analyzes legal change information, identifies the extent of impact, and provides appropriate improvement measures.

[0550] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0551] In this invention, the server includes means for automatically collecting legal amendment information, means for preprocessing the collected legal amendment information, means for using a generative AI model to analyze the preprocessed legal amendment information, means for generating prompt sentences based on the analyzed amendment content, means for using a full-text search engine to search for internal documents affected by the amendment content, means for applying a text classification algorithm to group and categorize the searched documents, means for automatically generating a comparison table of old and new documents, means for evaluating the legal compliance status of each company or department, means for providing customized improvement measures and advice based on the evaluation results, and means for notifying the user's terminal of the provided results, thereby enabling companies and organizations to respond quickly and efficiently to legal amendments.

[0552] "Legal amendment information" refers to information regarding changes or amendments to laws and regulations.

[0553] "Preprocessing" refers to the process of filtering and shaping data to convert it into a form suitable for analysis.

[0554] "Generative AI models" refer to algorithms or systems that use artificial intelligence techniques to analyze data. Examples include BERT and GPT-3.

[0555] A "prompt" refers to text in the form of instructions or questions that are input into a generative AI model.

[0556] A "full-text search engine" refers to a software system for searching large amounts of text data for specific keywords or phrases. Specific examples include Elasticsearch.

[0557] "Text classification algorithm" refers to a machine learning algorithm for classifying text data into specific categories or groups. Examples include random forests and support vector machines (SVMs).

[0558] The "old and new comparison table" refers to a table that lists and compares the contents of new laws and regulations with the contents of old laws and regulations.

[0559] "Compliance with laws and regulations" refers to the extent to which a company or organization operates in compliance with current laws and regulations.

[0560] "Customized solutions and advice" refers to specific solutions and advice tailored to the specific circumstances of a company or sector.

[0561] "User device" refers to a device such as a computer, smartphone, or tablet used by the ultimate recipient.

[0562] The present invention provides a system that automatically collects and analyzes legal amendment information, enabling companies and organizations to respond to legal amendments quickly and accurately. This system includes multiple processing steps involving servers, terminals, and users.

[0563] Collecting information on legal reforms

[0564] The server automatically accesses an external legal database (e.g., "legal database") or news feed to obtain the latest legal amendment information. For example, the server can use an API to send a query to a legal database and collect the necessary legal amendment information. This automatic collection reduces the effort required for companies to manually collect information.

[0565] Pre-processing of legal amendments

[0566] The server preprocesses the collected legal amendment information and converts it into a format suitable for analysis, including removing unnecessary HTML tags and special characters, and converting it into JSON or text file format.

[0567] Analysis with generative AI models

[0568] The preprocessed legal amendment information is analyzed using a generative AI model (e.g., BERT or GPT-3). This identifies the specific content and changes of the legal amendment. For example, the server issues a prompt statement, "Please tell me the key points of the amendments to the new Personal Information Protection Act," to the generative AI model and performs analysis based on the response.

[0569] Identifying the scope of impact

[0570] The server scans the company's internal document database using a full-text search engine (e.g., "full-text search engine") to identify affected documents based on specific keywords or regular expressions. For example, it searches for documents containing keywords such as "personal information" or "encryption."

[0571] Grouping and categorizing relevant sections

[0572] The server groups and categorizes the identified impact areas using a text classification algorithm (e.g., "random forest" or "SVM"), so that impact areas that belong to the same category are displayed together.

[0573] Generate a comparison table of old and new versions

[0574] The server compares the new legal amendments with the old laws and regulations and automatically generates a comparison table. This clearly visualizes which parts of the company will change and how. Specific software tools that can be used include "DiffMatchPatch."

[0575] Compliance assessment

[0576] The server evaluates the legal compliance status of each company and each department. Based on the evaluation results, it is possible to understand the extent to which the company complies with laws and regulations and the progress of necessary measures.

[0577] Providing customized advice

[0578] Based on the results of the assessment, the server generates specific improvement measures and advice tailored to each company and department, such as recommending the latest encryption software for the IT department and training on new encryption processes for the HR department.

[0579] User notification and confirmation

[0580] The server notifies the user's device of the analysis results, the generated comparison table of old and new data, and improvement measures. The user can check this information on their own device and take any necessary measures. Notification methods include email and a dedicated app.

[0581] Specific examples

[0582] For example, let us consider the case where a new Personal Information Protection Act comes into effect.

[0583] 1. The server retrieves information about the new Personal Information Protection Act from the legislation database.

[0584] 2. Preprocess the acquired information and then use a generative AI model to identify "personal information encryption obligations."

[0585] 3. The server scans the company's internal document database to identify documents related to the handling of personal information. For example, a document titled "Current storage method (password protection)" is identified.

[0586] 4. The server groups and categorizes the impact ranges related to "storage method" using a text classification algorithm.

[0587] 5. The server compares the new policy, "Personal information is encrypted and stored," with the old policy, "Stored in a password-protected file," and generates a comparison table of the old and new policies.

[0588] 6. The server evaluates the compliance status of the IT and HR departments. It finds that the IT department has already implemented encryption, but the HR department has not.

[0589] 7. The server recommends that the HR department "install encryption software" and the IT department "regularly update encryption protocols."

[0590] 8. The server notifies the terminals of the personnel in charge of each department of the results of the analysis and a comparison table of the old and new data. The users can check these on their terminals and take any necessary action.

[0591] In this way, the system of the present invention provides a means for companies and organizations to respond quickly and efficiently to legal changes and minimize the risk of non-compliance with laws and regulations or omissions.

[0592] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0593] Step 1:

[0594] The server automatically accesses external law databases and related news feeds to collect legal amendment information. Specifically, it uses an API to send queries to the law database and obtain the latest legal amendment information. The input is a query to the law database, and the output is the obtained legal amendment information.

[0595] Step 2:

[0596] The server preprocesses the acquired legal amendment information and converts it into a format suitable for analysis. For example, it removes unnecessary HTML tags and special characters and converts the data into JSON or text format. This process reformats the data and makes it suitable for the next analysis step. The input is the acquired legal amendment information, and the output is the preprocessed legal amendment information.

[0597] Step 3:

[0598] The server analyzes the preprocessed legal amendment information using a generative AI model (e.g., BERT or GPT-3). For example, it issues a prompt statement, "Please tell me the key points of the amendments to the new Personal Information Protection Act," to the generative AI model and analyzes the response. The input is the preprocessed legal amendment information and the prompt statement, and the output is the specific changes in the legal amendments.

[0599] Step 4:

[0600] The server identifies the internal documents affected by the analyzed revisions based on the content of the revisions. Specifically, it uses a full-text search engine (e.g., Elasticsearch) to scan the internal document database and identifies relevant documents based on specific keywords or regular expressions. The input is the analyzed revisions, and the output is the affected internal documents.

[0601] Step 5:

[0602] The server groups and categorizes the identified internal documents. Specifically, it uses a text classification algorithm (e.g., random forest or SVM) to categorize the identified impact areas. The input is the affected internal documents, and the output is a list of documents classified by category.

[0603] Step 6:

[0604] The server automatically generates a comparison table between the old and new regulations. Specifically, it uses the "DiffMatchPatch" library to compare the new and old regulations and format them to clearly show the differences between the old and new. The input is the old and new legal content, and the output is the comparison table.

[0605] Step 7:

[0606] The server evaluates compliance with laws and regulations for each company and department. For example, it queries employee IDs and department information to evaluate the compliance of current measures with new regulations. The input is each department's current data storage protocols and manual processes, and the output is the compliance assessment results for each department.

[0607] Step 8:

[0608] The server provides customized improvement measures and advice based on the results of the compliance assessment. For example, it recommends "installation of the latest encryption software" to the IT department and "encryption process training" to the HR department. The input is the compliance assessment result, and the output is specific improvement measures and advice.

[0609] Step 9:

[0610] The server notifies the user's device of the analysis results, the generated comparison table between old and new versions, and improvement measures. For example, notifications can be sent to the device of the person in charge via email or a dedicated app. The input is the analysis results, the comparison table between old and new versions, and the improvement measures, and the output is a notification to the user.

[0611] In this way, each step works together to create a system that can effectively respond to legal changes.

[0612] (Application example 1)

[0613] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0614] As legal changes become more frequent in companies and organizations, it is difficult to quickly and accurately collect and analyze information on legal changes and immediately implement specific countermeasures based on that information. Another issue is the lack of a means to efficiently identify the scope of impact of the changes and update corporate security policies accordingly.

[0615] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0616] In this invention, the server includes means for automatically collecting legal amendment information, means for analyzing the collected legal amendment information to identify the amendment details, means for identifying internal documents affected by the identified amendment details, means for grouping and categorizing the identified scope of impact, means for automatically generating a comparison table of old and new information, means for evaluating the legal compliance status of each company or department, means for providing customized improvement measures and advice based on the evaluation results, means for notifying the user's terminal of the provided results, means for updating the company's security policy in real time based on the notified results and improvement measures, and means for presenting specific measures to the user regarding the updated security policy. This enables companies to respond quickly and efficiently to legal amendments, strengthen security, and improve legal compliance.

[0617] "Legal Change Information" means information about laws and regulations that have been updated or changed by governments or regulatory authorities.

[0618] A "collection method" is a mechanism by which the server automatically retrieves information from external databases and related feeds.

[0619] The "means of analysis" refers to a mechanism that uses algorithms or generative AI models to process collected legal amendment information and identify the amendments.

[0620] "Internal documents" are official documents such as reports, guidelines, and procedures used within a company.

[0621] A "grouping and categorization method" is an algorithm for classifying and organizing identified impact areas based on common characteristics.

[0622] A "comparison table of old and new laws" is a document that displays a comparison of the contents of laws and regulations before and after the change.

[0623] "Assessment tools" are mechanisms for determining how well a company or department currently complies with the law.

[0624] "Customized improvement measures and advice" refers to specific improvement methods and suggestions for each organization or department based on the results of the compliance assessment.

[0625] The "notification means" is a communication function for sending analysis results and improvement measures to the user's terminal.

[0626] "Real-time" refers to a time frame within which immediate action can be taken based on the acquisition and analysis of legal change information.

[0627] A "security policy" is a set of operational rules and standards established by a company to protect information.

[0628] "Specific measures" are concrete actions or policies that should be implemented to address a specific problem.

[0629] The present invention provides a system that automatically collects and analyzes legal amendment information, enabling companies and organizations to respond to legal amendments quickly and accurately. This system includes functions such as collecting legal amendment information, analyzing it, identifying the scope of impact, generating a comparison table of the old and new amendments, evaluating compliance with laws and regulations, and providing customized improvement measures and advice. Below, we will explain in detail how each of these steps is implemented.

[0630] The server uses the requests library to automatically access external legal databases and related news feeds to retrieve the latest legal changes, a process that reduces the effort required for companies to manually gather information.

[0631] The collected information is preprocessed using the transformers library to convert it into a format suitable for analysis, and then a generative AI model (e.g., BERT) is used to analyze the legal changes and identify specific amendments.

[0632] To identify the scope of impact, the system scans the company's internal documents and operational rules database, and uses full-text search and regular expressions to identify the areas affected by the identified revisions.The system then uses a text classification algorithm to group and categorize the identified scope of impact, allowing impacts belonging to the same category to be displayed together.

[0633] The server automatically generates a comparison table by comparing the old and new legal content. This comparison table clearly visualizes which parts of the company will be changed and how.

[0634] The server evaluates the compliance status of each company and each department. Based on the results of this evaluation, specific improvement measures and advice customized for each company and department are generated, which helps improve compliance.

[0635] Finally, the server sends the generated analysis results, a comparison table of the old and new versions, and improvement measures to the user's device, where the user can check this information and take any necessary measures.

[0636] As a concrete example, consider the case where a new cybersecurity law has been enacted, resulting in stricter data encryption standards. The server retrieves information about the new cybersecurity law from a legal database, preprocesses the collected information, and then uses a generative AI model to identify "strengthened data encryption standards." Next, it scans the company's internal document database to identify documents related to data encryption. These documents describe the current encryption methods.

[0637] The server groups and categorizes the scope of impact related to "data encryption." It then compares the new regulation, "Enhanced Data Encryption Standard," with the old regulation, "Current Encryption Standard," and generates a comparison table of the old and new standards. The server evaluates the compliance status of the IT department and each department, and based on the evaluation results, recommends that the IT department "introduce new encryption software" and each department "update encryption protocols." The analysis results and the comparison table of the old and new standards are sent to the terminals of the personnel in each department, allowing users to check them on their terminals and take any necessary action.

[0638] This system allows companies and organizations to quickly identify the scope of impact of legal changes and take effective measures. Furthermore, the use of generative AI models and prompt sentences improves analysis accuracy and efficiency, enabling immediate updates to corporate security policies.

[0639] Example prompt sentence:

[0640] "The new Cybersecurity Law has tightened data encryption standards. How will this change affect a company's information security policy? Please suggest specific measures."

[0641] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0642] Step 1:

[0643] The server automatically collects legal change information.

[0644] Input: Legal database URL and related news feed URL.

[0645] Specific operation: Uses the requests library to periodically access legal databases and news feeds to obtain the latest legal changes.

[0646] Output: JSON data of legal amendment information.

[0647] Step 2:

[0648] The server preprocesses the collected legal amendment information and converts it into a format suitable for analysis.

[0649] Input: JSON data of legal amendment information obtained in Step 1.

[0650] Specific operation: The acquired JSON data is structured and converted into a format (text format or token format) that is easy for the generative AI model to parse.

[0651] Output: Preprocessed text data of legal amendment information.

[0652] Step 3:

[0653] The server analyzes the preprocessed legal amendment information using a generative AI model to identify the amendment content.

[0654] Input: Preprocessed text data of legal amendment information.

[0655] What it does: Using the transformers library, we analyze the tokenized data using the BERT model to identify specific modifications.

[0656] Output: Data identifying the amendments (e.g., a list of amendments).

[0657] Step 4:

[0658] The server identifies the internal documents that will be affected by the identified revisions.

[0659] Input: Data identifying amendments and the company's internal document database.

[0660] Specific operation: Using full-text search and regular expressions, internal documents containing revision details are automatically found.

[0661] Output: A list of internal documents that fall within the scope of impact.

[0662] Step 5:

[0663] The server groups and categorizes the impact areas.

[0664] Input: List of internal documents within the scope of impact.

[0665] What it does: Uses a text classification algorithm to group and categorize impact areas based on common characteristics.

[0666] Output: A list of impact areas, grouped and categorized.

[0667] Step 6:

[0668] The server automatically generates a comparison table of the old and new versions.

[0669] Input: List of internal documents included in the amendment and their scope of impact.

[0670] Specific actions: Compare the new and current legal content and create a comparison table.

[0671] Output: Comparison table of old and new versions.

[0672] Step 7:

[0673] The server evaluates compliance with laws and regulations for each company and department.

[0674] Input: Old and new comparison table and company compliance status data.

[0675] Specific operation: Using an evaluation algorithm, determine the current compliance status.

[0676] Output: A report of the evaluation results.

[0677] Step 8:

[0678] The server provides customized remedial measures and advice based on the evaluation results.

[0679] Input: Report of evaluation results.

[0680] Specific actions: Based on the evaluation results, specific improvement measures and advice are generated for each department.

[0681] Output: A customized list of remediation measures and advice.

[0682] Step 9:

[0683] The server notifies the user's device of the analysis results, a comparison table of the old and new data, and improvement measures.

[0684] Input: A customized list of remedies and advice.

[0685] Specific operation: Send a notification to the user's device.

[0686] Output: A notification message visible to the user's device.

[0687] Step 10:

[0688] The server updates the company's security policy in real time based on the notified results and remedial measures.

[0689] Input: Notifications and remediation actions reviewed by the user.

[0690] Specific behavior: Integrates with the company's security management system and reflects necessary changes in real time.

[0691] Output: Updated security policy.

[0692] Step 11:

[0693] The server presents specific measures to the user regarding the updated security policy.

[0694] Input: The updated security policy.

[0695] Specific action: Specific countermeasures are notified to the user's device.

[0696] Output: A notification message with specific countermeasures that can be viewed on the user's device.

[0697] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0698] This invention provides a system that automatically collects and analyzes legal amendment information, enabling companies and organizations to respond to legal amendments quickly and accurately. Furthermore, by combining it with an emotion engine, it provides notifications and remedial measures that take into account the user's emotional state. This system includes multiple processing steps involving a server, terminals, and users.

[0699] About program processing

[0700] 1. Collecting information on legal reforms

[0701] The server automatically accesses external legal databases and related news feeds to obtain the latest legal information. Using APIs and crawling tools, the data is collected and stored in a database.

[0702] 2. Analysis of legal reforms

[0703] The server preprocesses the legal amendment information it obtains, normalizing the data, filtering unnecessary information, standardizing formats, etc. It then analyzes the preprocessed legal amendment information using a generative AI model (e.g., BERT or GPT-3) to identify specific amendments.

[0704] 3. Identifying the scope of impact

[0705] The server accesses the company's internal document database and identifies the affected documents and operational rules based on the analysis results. The affected range is extracted using full-text search and regular expressions.

[0706] 4. Grouping and categorizing relevant sections

[0707] The affected servers are grouped using a text classification algorithm and categorized into categories such as "method of acquisition," "method of storage," and "method of use."

[0708] 5. Generate a comparison table of old and new versions

[0709] The server compares the old and new legal content and automatically generates a comparison table, which clearly shows which parts of the company will be changed and how.

[0710] 6. Compliance Assessment

[0711] The server evaluates the legal compliance status of each company and each department. Based on the information in the database, it evaluates the degree to which each department complies with the law using numerical values ​​and indicators.

[0712] 7. Providing customized advice

[0713] Based on the assessment results, the server generates specific improvement measures and advice customized for each department, including measures tailored to each department's specific situation.

[0714] 8. User Emotion Recognition

[0715] The server uses an emotion engine to analyze the user's emotional state, using voice and text data collected from the user. Based on the analysis, the user's stress and fatigue state are identified.

[0716] 9. Emotion-Based Notification Adjustment

[0717] The server adjusts the notification content and remedial measures based on the user's emotional state. For example, for a user in a high stress state, the server may simplify the notification content or reduce the number of suggestions.

[0718] 10. Notification optimization

[0719] The server optimizes the timing and format of notifications based on the analysis results of the emotion engine, sending notifications at the optimal time to suit the user's work schedule.

[0720] 11. User Notice and Confirmation

[0721] The server notifies the user's device of the analysis results, the generated comparison table of old and new data, and improvement measures. The user can check this information on their device and take any necessary measures.

[0722] 12. Progress Management

[0723] Users update the progress status within the system. For items that have been completed, the status is updated within the system to manage the progress of improvements to legal compliance.

[0724] Specific examples

[0725] Example 1: New Personal Information Protection Law and Emotion Recognition

[0726] 1. Collecting information on legal reforms

[0727] The server retrieves information about the new Personal Information Protection Act from the legislation database.

[0728] 2. Analysis of legal reforms

[0729] The server performs preprocessing and uses a generative AI model to identify "personal information encryption obligations."

[0730] 3. Identifying the scope of impact

[0731] The server scans the company's internal document database to identify documents related to the handling of personal information. The old policy states that these documents should be stored in a password-protected file.

[0732] 4. Grouping and categorizing relevant sections

[0733] The server groups and categorizes the scope of impact regarding "storage method."

[0734] 5. Generate a comparison table of old and new versions

[0735] The server compares the new rule, "Personal information is encrypted and stored," with the old rule, "Stored in a password-protected file," and generates a comparison table of the old and new rules.

[0736] 6. Compliance Assessment

[0737] The server evaluates compliance with IT and HR departments. IT department has implemented encryption, but HR department has not.

[0738] 7. Providing customized advice

[0739] The server recommends that the HR department "install encryption software" and the IT department "regularly update encryption protocols."

[0740] 8. User Emotion Recognition

[0741] The server uses an emotion engine to analyze the voice and text data of the HR department staff member and identify their stress level. It turns out that they are experiencing high levels of stress.

[0742] 9. Emotion-Based Notification Adjustment

[0743] Based on the results of the emotion engine, the server provides HR personnel with simplified notifications and a small number of suggestions.

[0744] 10. Notification optimization

[0745] The server sends a notification after the break according to the person's work schedule.

[0746] 11. User Notice and Confirmation

[0747] The server notifies the terminals of the personnel in each department of the results of the analysis and a comparison table of the old and new data. The users can then check these on their terminals and take any necessary action.

[0748] 12. Progress Management

[0749] Users update their progress within the system and manage compliance improvements.

[0750] This system allows companies and organizations to respond to legal changes quickly and efficiently, minimizing the risk of legal violations and omissions. In addition, by utilizing the emotion engine, users can receive notifications in the most optimal state and carry out their work efficiently.

[0751] The processing flow will be explained below.

[0752] Step 1:

[0753] The server accesses external legal databases and related news feeds to collect legal amendment information. It automatically obtains the latest legal amendment information using APIs and crawling tools and stores it in the database.

[0754] Step 2:

[0755] The server performs preprocessing of the legal amendment information it obtains, normalizing the data, filtering out unnecessary information, standardizing the format, and converting it into a format suitable for analysis.

[0756] Step 3:

[0757] The server analyzes the preprocessed legal amendment information using a generative AI model (e.g., BERT or GPT-3), identifying specific amendments and their scope of application, and extracting them as text.

[0758] Step 4:

[0759] The server accesses the company's internal document database and identifies the documents and operational rules that will be affected based on the analysis results. Related text is extracted using full-text search and regular expressions.

[0760] Step 5:

[0761] The server uses a text classification algorithm to group the identified impact areas and categorize them into categories such as "acquisition method," "storage method," and "usage method."

[0762] Step 6:

[0763] The server compares the old and new legal content and automatically generates a comparison table, which clearly shows which parts of the company will be changed and how.

[0764] Step 7:

[0765] The server-generated comparison table is verified to check for omissions and duplications. An algorithm is used to automatically check and make any necessary corrections.

[0766] Step 8:

[0767] The server evaluates the legal compliance status of each company and department, and evaluates the degree to which each department complies with laws and regulations using numerical values ​​and indicators based on the information in the database.

[0768] Step 9:

[0769] Based on the assessment results, the server generates specific improvement measures and advice customized for each department, including measures tailored to the specific circumstances of each department.

[0770] Step 10:

[0771] The server uses an emotion engine to analyze the user's emotional state. It uses voice and text data collected from the user to identify the emotional state. For example, it uses voice analysis and natural language processing to determine the user's stress level and emotional state.

[0772] Step 11:

[0773] The server adjusts the notification content and improvement measures based on the user's emotional state. For users in a high stress state, the server will simplify the notification content and reduce the number of suggestions. In addition, notifications with low urgency will be postponed, allowing the user to respond in a more appropriate state.

[0774] Step 12:

[0775] The server optimizes the timing and format of notifications based on the analysis results of the emotion engine. Notifications are sent at optimal times according to the user's work schedule and emotional state. For example, notifications can be sent outside peak work hours to reduce the burden on the user.

[0776] Step 13:

[0777] The server notifies the user's device of the analysis results, the generated comparison table of old and new data, and improvement measures. Information is provided promptly via email or push notification. After receiving the notification, the user can check the detailed information on their device and take any necessary measures.

[0778] Step 14:

[0779] Users update the progress status within the system. For items that have been completed, the status is updated within the system to manage the progress of improvements to legal compliance.

[0780] Example 2

[0781] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0782] Modern companies and organizations need to respond quickly and accurately to frequent legal changes, but conventional manual processes require a huge amount of time and effort. This often increases the workload of those in charge, leading to stress and reduced efficiency. This raises concerns about the risk of legal violations and inefficiencies. There is a need for a system that can solve this problem and improve operational efficiency while maintaining legal compliance.

[0783] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for automatically collecting legal amendment information, means for analyzing the collected legal amendment information to identify the amendment content, means for identifying internal documents affected by the identified amendment content, means for grouping and categorizing the identified scope of impact, means for automatically generating a comparison table of old and new versions, means for evaluating the legal compliance status of each organization or department, means for providing customized improvement measures and advice based on the evaluation results, means for notifying the user of the provided results to the user's terminal, means for analyzing the user's emotional state and adjusting the notification content and improvement measures, and means for optimizing the timing and format of notifications. This enables companies and organizations to respond quickly and accurately to legal amendments, reduce the user's workload, and enable efficient and effective legal compliance.

[0784] "Means for automatically collecting information on legal amendments" refers to technology that automatically obtains the latest information on legal amendments from external legal databases and news feeds and stores it in a dedicated database.

[0785] "Means of analyzing collected legal amendment information and identifying the amendment content" refers to technology that preprocesses collected legal amendment information and analyzes and identifies specific amendment points using a generative AI model.

[0786] The "means for identifying internal documents affected by the identified revisions" refers to a technology for searching and identifying relevant documents in a company's internal document database based on the identified revisions.

[0787] "Means for grouping and categorizing the identified impact scope" refers to a technique for classifying the affected documents based on specific criteria and grouping them into categories.

[0788] "Means for automatically generating a comparison table between the old and new laws" refers to technology that compares the contents of the old and new laws and regulations and automatically generates a comparison table that clearly shows the differences.

[0789] "Means for assessing compliance with laws and regulations for each organization or department" refers to technology that evaluates compliance with laws and regulations within a company or each department based on numerical values ​​and indicators and displays the results visually.

[0790] "Means for providing customized improvement measures and advice based on the evaluation results" refers to a technology that provides specific improvement measures and advice individually to each department based on the evaluation results of compliance with laws and regulations.

[0791] "Means for notifying the user of the provided results" refers to technology for notifying the user of the generated analysis results and improvement measures.

[0792] "Means for analyzing the user's emotional state and adjusting notification content and suggested improvements" refers to technology that analyzes collected voice and text data to identify the user's emotional state and adjusts notification content and suggested improvements based on the analysis results.

[0793] "Means for optimizing the timing and format of notifications" refers to technology that delivers notifications at the most appropriate timing and in the most appropriate format, taking into account the user's work schedule and emotional state.

[0794] This invention provides a system that automatically collects and analyzes legal amendment information, enabling companies and organizations to respond to legal amendments quickly and accurately. Furthermore, by combining it with an emotion engine, it provides notifications and remedial measures that take into account the user's emotional state. This system includes multiple processing steps involving a server, terminals, and users.

[0795] First, the server automatically accesses external legal databases (e.g., GovInfo API or general news feeds) and uses APIs or crawling tools (e.g., Scrapy, Requests) to retrieve the latest legal change information. The collected information is then stored in a dedicated database (e.g., MySQL, PostgreSQL).

[0796] Next, the server analyzes the collected legal amendment information. Specifically, it performs preprocessing such as text normalization, filtering unnecessary information, and standardizing data formats. The preprocessed data is used as a prompt for a generative AI model (e.g., GPT-3, BERT), which analyzes the legal amendments and identifies specific amendments. For example, specific amendments such as "the new Personal Information Protection Act requires the encryption of personal information" may be identified.

[0797] The server then accesses the company's internal document database (e.g., Elasticsearch) to identify documents and operational rules that are affected by the identified legal changes. Using full-text search and regular expressions, it identifies documents that state, for example, that the old regulations should be "stored in a password-protected file." This identified scope of impact is then classified into categories such as "storage method" using a text classification algorithm (e.g., tf-idf, LDA).

[0798] When the server generates the comparison table, it compares the old and new legal provisions. For example, it compares the new provision "Personal information shall be encrypted and stored" with the old provision "Stored in a password-protected file," and automatically generates the comparison table. A diff tool (e.g., the Python library difflib) is used to generate this table.

[0799] To assess a company's compliance with laws and regulations, the server uses the information in the database to evaluate the compliance status of each department using numerical values ​​and indicators, and uses a dashboard tool (e.g., Tableau or Power BI) to visually display the results. For example, the server displays the results of the evaluation, such as showing that the IT department has already implemented encryption, but the HR department has not yet done so.

[0800] The server then provides customized advice and remediation measures based on the assessment results, including specific recommendations tailored to the situation of each department. For example, the server might recommend "regularly updating encryption protocols" for the IT department, or "implementing encryption software" for the HR department.

[0801] Furthermore, to understand the user's emotional state, the server performs analysis using an emotion engine (e.g., IBM Watson, Azure Emotion API). The voice and text data collected from the user is analyzed to identify stress and fatigue levels. For example, the emotion engine may identify that a human resources department employee is experiencing high levels of stress.

[0802] Based on the results, the server adjusts the content of notifications and improvement measures. Based on the analysis results of the emotion engine, adjustments are made such as simplifying the content of notifications or reducing the number of suggestions for users who are in a high state of stress. The timing of notifications is also optimized, taking into account the user's work schedule and emotional state. For example, the server checks the work schedule of the person in charge and sends a notification after an appropriate break.

[0803] Finally, the server notifies the user's device of the analysis results, the generated comparison table of old and new versions, and improvement measures. The user can check this information on their device and take any necessary actions. Progress is updated within the system, and the status of items for which action has been completed is updated, allowing the improvement in compliance with laws and regulations to be managed.

[0804] A specific example is the enforcement of a new Personal Information Protection Act. The server collects information about the new Personal Information Protection Act and uses an analytical model to identify the "obligation to encrypt personal information." The internal document database is then scanned to identify and categorize the affected documents. A comparison table of the old and new documents is generated, and the IT and HR departments are assessed for compliance with the law, providing advice to each department. An emotion engine is used to analyze the stress level of employees, and notifications are sent at the appropriate time and in the appropriate format.

[0805] The above processing steps enable companies to respond quickly and accurately to legal changes, preventing legal violations and a decline in business efficiency. In addition, the use of an emotion engine reduces the burden on users and enables them to carry out their work in an optimal state.

[0806] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0807] Step 1:

[0808] The server collects legal amendment information from external legal databases and news feeds. As input, it uses API requests or crawling tools (e.g., Scrapy, Requests) to obtain the latest legal amendment information. The output is the obtained legal amendment information, which is stored in a dedicated database (e.g., MySQL, PostgreSQL).

[0809] Step 2:

[0810] The server analyzes the legal amendment information collected. The input is preprocessed by normalizing the text, filtering unnecessary information, and standardizing the format. Specifically, a text cleaning library (e.g., NLTK, spaCy) is used. The output is the preprocessed data, which is used as a prompt for a generative AI model (e.g., GPT-3, BERT).

[0811] Step 3:

[0812] The server uses the generative AI model to analyze the preprocessed legal amendment information and identify the specific amendments. The input is the data preprocessed in step 2. The output is the identified specific legal amendments. For example, the new Personal Information Protection Act identifies the "obligation to encrypt personal information."

[0813] Step 4:

[0814] The server accesses the company's internal document database and identifies documents affected by the identified legal changes. The input is the legal changes identified in step 3. This process uses full-text search and regular expressions (e.g., Elasticsearch). The output is the specific internal documents that are affected. For example, documents that state "save in a password-protected file" are identified.

[0815] Step 5:

[0816] The server groups and categorizes the identified impact scope documents. The input is the impact scope documents identified in step 4. A text classification algorithm (e.g., tf-idf, LDA) is used to classify the documents into categories such as "storage method." The output is the classified impact scope documents.

[0817] Step 6:

[0818] The server compares the old and new legal documents and generates a comparison table. The input is the old and new legal documents. Specifically, a diff tool (e.g., the Python library difflib) is used to automatically generate the comparison table. The output is the comparison table. This clearly shows which parts of the company have been changed and how.

[0819] Step 7:

[0820] The server evaluates the legal compliance status for each department of the company. The input is the legal compliance status data stored for each department of the company. Based on this data, compliance status is evaluated using numerical values ​​and indicators, and a dashboard tool (e.g., Tableau, Power BI) is used to visually display the results. The output is the evaluation results of legal compliance status for each department.

[0821] Step 8:

[0822] Based on the assessment results, the server provides customized advice and improvement measures for each department. The input is the assessment results obtained in step 7. Specific improvement measures tailored to the specific circumstances of each department are included. The output is customized advice and improvement measures. For example, the server may recommend "regularly updating encryption protocols" for the IT department and "implementing encryption software" for the HR department.

[0823] Step 9:

[0824] The server uses an emotion engine to analyze the user's emotional state. The input is voice and text data collected from the user. Analysis is performed using an emotion engine (e.g., IBM Watson, Azure Emotion API) to identify the user's stress and fatigue state. The output is the analyzed user's emotional state.

[0825] Step 10:

[0826] The server adjusts the notification content and remedial measures based on the user's emotional state. The input is the user's emotional state obtained in step 9. For users in a high stress state, the notification content is simplified or the suggestions are reduced. The output is the adjusted notification content and remedial measures.

[0827] Step 11:

[0828] The server sends the notification at the optimal timing and in the optimal format. The input is the notification content adjusted in step 10 and the user's work schedule. The output is the notification sent to the user's device. For example, the notification may be sent after a break, taking into account the person's work schedule.

[0829] Step 12:

[0830] The user checks the notified content and takes any necessary action. The input is the notification content sent from the server. Based on this, the user takes specific action to comply with the law and updates the progress status within the system. The output is the updated progress status. For items for which action has been completed, the status is updated within the system and the improvement in compliance with the law is managed.

[0831] (Application example 2)

[0832] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0833] In order for companies and organizations to respond quickly and accurately to legal changes, they need a system that automatically collects and analyzes the latest legal change information. However, with conventional systems, the process of identifying which parts of the company the change will affect and providing appropriate countermeasures is complicated, resulting in delayed responses. Furthermore, information is provided with uniform notification content and timing without taking into account the emotional state of the user receiving the notification, which increases the burden on users and risks reducing work efficiency. There is a need to solve these issues and provide a system that allows companies to respond quickly and accurately to legal change information.

[0834] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0835] In this invention, the server includes means for automatically collecting legal amendment information, means for analyzing the collected legal amendment information to identify the amendment details, means for identifying internal documents affected by the identified amendment details, means for grouping and categorizing the identified scope of impact, means for automatically generating a comparison table of old and new versions, means for evaluating the legal compliance status of each company or department, means for providing customized improvement measures and advice based on the evaluation results, means for notifying the user of the provided results to their terminal, means for analyzing the user's emotional state, means for adjusting the content and timing of notifications based on the user's emotional state, and means for managing progress. This enables companies to respond to legal amendments quickly and efficiently and reduces the burden on users by taking their emotional state into consideration.

[0836] "Means for automatically collecting information on legal amendments" refers to a function that accesses external legal databases and related news feeds, automatically obtains the latest legal amendment information, and stores it in a database.

[0837] The "means of analyzing collected legal amendment information and identifying the amendment content" refers to a function that normalizes and filters collected data and uses a generative AI model to extract specific amendment points.

[0838] "Means to identify internal documents that will be affected by the identified revisions" refers to a function that accesses a company's internal document database and identifies the documents and operational rules that will be affected based on the analysis results.

[0839] The "means for grouping and categorizing the identified impact ranges" is a function for grouping and categorizing the identified impact ranges using a text classification algorithm.

[0840] The "means for automatically generating a comparison table between the old and new laws and regulations" is a function that automatically generates a comparison table that compares the contents of the old and new laws and regulations and clearly shows which parts have been changed and how.

[0841] "Means for evaluating compliance with laws and regulations for each company and department" is a function that evaluates the degree to which each department complies with laws and regulations using numerical values ​​and indicators based on information in the database.

[0842] "Means for providing customized improvement measures and advice based on the evaluation results" refers to a function that generates specific improvement measures and advice tailored to the specific circumstances of each department based on the evaluation results.

[0843] The "means for notifying the user of the provided results" is a function for notifying the user of the analysis results, the generated comparison table between the old and new data, and improvement measures to the user's terminal.

[0844] The "means for analyzing the user's emotional state" is a function that analyzes voice and text data collected from the user and identifies the user's stress and fatigue state using an emotion engine.

[0845] "Means for adjusting notification content and timing based on emotional state" refers to a function that optimizes the timing and format of notifications by taking into account the user's emotional state, simplifying the notification content, or reducing the amount of suggested content.

[0846] The "means for managing progress" is a function that updates the progress of items handled by the user on the system and manages the progress of improvements in compliance with laws and regulations.

[0847] This invention is a system that automatically collects and analyzes legal amendment information, enabling companies and organizations to respond to legal amendments quickly and accurately. Furthermore, by combining it with an emotion engine, it is possible to provide notifications and remedial measures that take into account the user's emotional state. This system is configured using the following means.

[0848] 1. Collecting information on legal reforms

[0849] The server automatically accesses external legal databases and related news feeds to obtain the latest legal information. Specific technologies used include APIs and crawling tools. The obtained data is stored on the server.

[0850] 2. Analysis of legal reforms

[0851] The server normalizes and filters the collected data and identifies specific corrections using generative AI models (e.g., BERT or GPT-3). The data preprocessing stage removes unnecessary information and standardizes the format.

[0852] 3. Identifying the scope of impact

[0853] The server accesses the company's internal document database and identifies the documents and operational rules that will be affected based on the analysis results. It then uses full-text search and regular expressions to extract the relevant documents from the database.

[0854] 4. Grouping and Categorization

[0855] The server uses a text classification algorithm to group the identified impact areas into categories such as "acquisition method," "storage method," and "usage method."

[0856] 5. Generate a comparison table of old and new versions

[0857] The server compares the old and new legal content and automatically generates a comparison table, which clearly shows which parts have been changed and how.

[0858] 6. Compliance Assessment

[0859] The server evaluates the legal compliance status of each company and each department, and based on the information in the database, evaluates the degree to which each department complies with the law using numerical values ​​and indicators.

[0860] 7. Providing customized advice

[0861] Based on the evaluation results, the server generates specific improvement measures and advice tailored to each department's specific situation.

[0862] 8. User Emotion Recognition

[0863] The server uses an emotion engine to analyze the user's emotional state, using voice and text data collected from the user. Based on the analysis, the user's stress and fatigue state are identified.

[0864] 9. Adjustment of notification content and timing

[0865] The server optimizes the timing and format of notifications by simplifying the content of notifications or reducing the number of suggestions, taking into account the user's emotional state.

[0866] 10. User Notice and Confirmation

[0867] The server notifies the user's device of the analysis results, the generated comparison table of old and new data, and improvement measures. The user can check this information on their device and take the necessary measures.

[0868] 11. Progress Management

[0869] Users can update the progress of their actions within the system and manage their compliance improvement.

[0870] This system is built using cloud servers (e.g., AWS, Google Cloud) and external APIs (legal database API, sentiment analysis API). BERT and GPT-3 are used as generative AI models.

[0871] Examples:

[0872] The new Personal Information Protection Law requires employees' personal information to be encrypted rather than kept in password-protected files. The system automatically detects this and advises IT departments to regularly update their existing encryption systems and HR departments to implement encryption software. It also uses emotion recognition to streamline notifications for stressed users.

[0873] Example prompts to input to a generative AI model:

[0874] "Analyze the latest changes to the Personal Information Protection Act and identify how they affect your company's internal documents."

[0875] This system allows companies and organizations to respond quickly and efficiently to legal changes and reduces the burden on users by taking into account their emotional state.

[0876] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0877] Step 1:

[0878] Automatic collection of legal reform information

[0879] The server automatically accesses external legal databases and news feeds to obtain the latest legal amendment information. Specifically, it sends API requests and stores the obtained data in an internal database. The input is the API request, and the output is legal amendment information data.

[0880] Step 2:

[0881] Analysis of legal reforms

[0882] The server normalizes the collected legal amendment information and filters unnecessary information. Next, it uses a generative AI model (e.g., BERT or GPT-3) to identify specific amendments. The input is the collected legal amendment information, and the output is the analyzed amendment content. The specific operations are text normalization, filtering, and application of the AI ​​model.

[0883] Step 3:

[0884] Identifying the scope of impact

[0885] The server accesses the company's internal document database and identifies the affected documents and operational rules based on the analysis results. Specifically, it extracts the relevant documents from the database using full-text search and regular expressions. The input is the analyzed revision details, and the output is a list of affected internal documents.

[0886] Step 4:

[0887] Grouping and Categorization

[0888] The server uses a text classification algorithm to group the identified impact areas and categorize them into categories such as acquisition method, storage method, and usage method. The input is a list of affected internal documents, and the output is a categorized document list. The specific operation is the application of a text classification algorithm.

[0889] Step 5:

[0890] Generate a comparison table of old and new versions

[0891] The server compares the old and new legal content and automatically generates a comparison table. The input is the old and new legal content, and the output is the comparison table. The specific operation is to compare data and generate a format.

[0892] Step 6:

[0893] Compliance assessment

[0894] The server evaluates the legal compliance status of each company and each department based on the information in the database. The input is a categorized document list and operational data within the company, and the output is the evaluation result of the legal compliance status. Specific operations include analyzing the data and generating evaluation indicators.

[0895] Step 7:

[0896] Providing customized advice

[0897] Based on the evaluation results, the server generates specific improvement measures and advice tailored to the specific circumstances of each department. The input is the evaluation results of compliance status, and the output is advice customized for each department. The specific operation is to analyze the evaluation results and generate advice.

[0898] Step 8:

[0899] User Emotion Recognition

[0900] The server uses an emotion engine to analyze the user's emotional state. The input is voice and text data collected from the user, and the output is the analysis result of the emotional state. The specific operation involves analyzing the voice and text data and applying the emotion recognition engine.

[0901] Step 9:

[0902] Adjust notification content and timing

[0903] The server optimizes the timing and format of notifications by simplifying the content of notifications or reducing the number of suggestions, taking into account the user's emotional state. The input is the analysis result of the emotional state and advice on improvement measures, and the output is the adjusted notification content. The specific operation is to edit the notification content and adjust the timing.

[0904] Step 10:

[0905] User notification and confirmation

[0906] The server notifies the user's device of the analysis results, the generated comparison table of old and new data, and improvement measures. The input is the adjusted notification content, and the output is the notification information displayed on the user's device. The specific operation is to send and display the notification.

[0907] Step 11:

[0908] Progress Management

[0909] Users update the progress of corresponding items within the system and manage the progress of improvements to legal compliance. The input is the user's updated progress data, and the output is the updated progress data. The specific operation is to update the progress database.

[0910] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0911] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0912] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0913] [Third embodiment]

[0914] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0915] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0916] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0917] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0918] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0919] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0920] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0921] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0922] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0923] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0924] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0925] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0926] The present invention provides a system that automatically collects and analyzes legal amendment information, enabling companies and organizations to respond to legal amendments quickly and accurately. This system includes multiple processing steps involving servers, terminals, and users.

[0927] About program processing

[0928] 1. Collecting information on legal reforms

[0929] The server automatically accesses external legal databases and related news feeds to retrieve the latest legal information, eliminating the need for companies to manually gather information.

[0930] 2. Analysis of legal reforms

[0931] The server preprocesses the legal amendment information it obtains and converts it into a format suitable for analysis. It then uses a generative AI model (e.g., BERT or GPT-3) to analyze the legal amendments and identify the specific amendments.

[0932] 3. Identifying the scope of impact

[0933] The server scans the company's internal documents and operational rules database to automatically identify areas that the identified revisions will affect, using full-text search and regular expressions in the process.

[0934] 4. Grouping and categorizing relevant sections

[0935] The server groups and categorizes the identified impact areas using a text classification algorithm, so that impact areas that belong to the same category are displayed together.

[0936] 5. Generate a comparison table of old and new versions

[0937] The server compares the old and new legal content and automatically generates a comparison table that clearly visualizes which parts of the company will be changed and how.

[0938] 6. Compliance Assessment

[0939] The server evaluates the legal compliance status of each company and each department. The evaluation results indicate the extent to which the company complies with laws and regulations and allow the user to understand the progress of necessary measures.

[0940] 7. Providing customized advice

[0941] Based on the evaluation results, the server generates specific improvement measures and advice customized for each company or department, thereby improving compliance with laws and regulations.

[0942] 8. User Notification and Confirmation

[0943] The server notifies the user's device of the analysis results, the generated comparison table of the old and new versions, and improvement measures. The user can check this information on their own device and take any necessary measures.

[0944] Specific examples

[0945] Example 1: Enactment of the new Personal Information Protection Law

[0946] 1. Collecting information on legal reforms

[0947] The server retrieves information about the new Personal Information Protection Act from the legislation database.

[0948] 2. Analysis of legal reforms

[0949] The server preprocesses the information it receives and then uses a generative AI model to identify "personal information encryption obligations."

[0950] 3. Identifying the scope of impact

[0951] The server scans the company's internal document database to identify documents related to the handling of personal information, which contain information about the current storage method (password protection).

[0952] 4. Grouping and categorizing relevant sections

[0953] The server groups and categorizes the scope of influence regarding the "storage method."

[0954] 5. Generate a comparison table of old and new versions

[0955] The server compares the new rule, "Personal information is encrypted and stored," with the old rule, "Stored in a password-protected file," and generates a comparison table of the old and new rules.

[0956] 6. Compliance Assessment

[0957] The server evaluates the compliance status of the IT and HR departments. It finds that the IT department has already implemented encryption, but the HR department has not.

[0958] 7. Providing customized advice

[0959] The server recommends that the HR department "install encryption software" and the IT department "regularly update encryption protocols."

[0960] 8. User Notification and Confirmation

[0961] The server notifies the terminals of the personnel in each department of the results of the analysis and a comparison table of the old and new data. The users can then check these on their terminals and take any necessary action.

[0962] This system allows companies and organizations to respond quickly and efficiently to legal changes and minimize the risk of non-compliance with laws and regulations or omissions.

[0963] The processing flow will be explained below.

[0964] Step 1:

[0965] The server accesses external legal databases and related news feeds to collect legal amendment information. Using APIs and crawling tools, the latest legal amendment information is automatically retrieved and stored in the database.

[0966] Step 2:

[0967] The server preprocesses the acquired legal amendment information, normalizing the data, filtering unnecessary information, standardizing the format, and converting it into a format suitable for analysis.

[0968] Step 3:

[0969] The server analyzes the preprocessed legal amendment information using a generative AI model (e.g., BERT or GPT-3), identifying specific amendments and their scope of application, and extracting them as text.

[0970] Step 4:

[0971] The server accesses the company's internal document database and identifies the documents and operational rules that will be affected based on the analysis results. It then uses full-text search and regular expressions to extract relevant text.

[0972] Step 5:

[0973] The server groups and categorizes the identified impact areas, using a text classification algorithm to categorize the impact areas into categories such as "acquisition method," "storage method," and "usage method."

[0974] Step 6:

[0975] The server generates an initial version of the comparison table, comparing the old and new legal content and creating a comparison table in a format that clearly shows the changes for each article.

[0976] Step 7:

[0977] The server verifies the old and new tables to check for omissions and duplications, automatically checking using an algorithm and making any necessary corrections.

[0978] Step 8:

[0979] The server evaluates the legal compliance status of each company and department, and evaluates the degree to which each department complies with laws and regulations using numerical values ​​and indicators based on the information in the database.

[0980] Step 9:

[0981] Based on the results of the assessment, the server generates customized improvement measures and advice, such as proposing specific measures such as "installing encryption software" and "performing regular audits" for each department.

[0982] Step 10:

[0983] The server notifies the user's device of the analysis results, the generated comparison table of old and new data, and improvement measures. Information is provided promptly via email or push notification.

[0984] Step 11:

[0985] The user checks the notification on the device, reviews the analysis results, comparison table of old and new data, and improvement measures, and takes action to implement the necessary measures.

[0986] Step 12:

[0987] Users update the progress status within the system. For items that have been completed, the status is updated within the system to manage the progress of improvements to legal compliance.

[0988] Example 1

[0989] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0990] In order for companies and organizations to respond quickly and accurately to legal changes, they must manually collect and analyze vast amounts of legal change information and identify the extent of impact within the company. This requires significant human resources and time, and specialized knowledge is also required to evaluate compliance and propose improvement measures. This puts companies and organizations at risk of violating laws and regulations and is likely to suffer disadvantages due to delayed response. To solve these issues, a system is needed that automatically collects and analyzes legal change information, identifies the extent of impact, and provides appropriate improvement measures.

[0991] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0992] In this invention, the server includes means for automatically collecting legal amendment information, means for preprocessing the collected legal amendment information, means for using a generative AI model to analyze the preprocessed legal amendment information, means for generating prompt sentences based on the analyzed amendment content, means for using a full-text search engine to search for internal documents affected by the amendment content, means for applying a text classification algorithm to group and categorize the searched documents, means for automatically generating a comparison table of old and new documents, means for evaluating the legal compliance status of each company or department, means for providing customized improvement measures and advice based on the evaluation results, and means for notifying the user's terminal of the provided results, thereby enabling companies and organizations to respond quickly and efficiently to legal amendments.

[0993] "Legal amendment information" refers to information regarding changes or amendments to laws and regulations.

[0994] "Preprocessing" refers to the process of filtering and shaping data to convert it into a form suitable for analysis.

[0995] "Generative AI models" refer to algorithms or systems that use artificial intelligence techniques to analyze data. Examples include BERT and GPT-3.

[0996] A "prompt" refers to text in the form of instructions or questions that are input into a generative AI model.

[0997] A "full-text search engine" refers to a software system for searching large amounts of text data for specific keywords or phrases. Specific examples include Elasticsearch.

[0998] "Text classification algorithm" refers to a machine learning algorithm for classifying text data into specific categories or groups. Examples include random forests and support vector machines (SVMs).

[0999] The "old and new comparison table" refers to a table that lists and compares the contents of new laws and regulations with the contents of old laws and regulations.

[1000] "Compliance with laws and regulations" refers to the extent to which a company or organization operates in compliance with current laws and regulations.

[1001] "Customized solutions and advice" refers to specific solutions and advice tailored to the specific circumstances of a company or sector.

[1002] "User device" refers to a device such as a computer, smartphone, or tablet used by the ultimate recipient.

[1003] The present invention provides a system that automatically collects and analyzes legal amendment information, enabling companies and organizations to respond to legal amendments quickly and accurately. This system includes multiple processing steps involving servers, terminals, and users.

[1004] Collecting information on legal reforms

[1005] The server automatically accesses an external legal database (e.g., "legal database") or news feed to obtain the latest legal amendment information. For example, the server can use an API to send a query to a legal database and collect the necessary legal amendment information. This automatic collection reduces the effort required for companies to manually collect information.

[1006] Pre-processing of legal amendments

[1007] The server preprocesses the collected legal amendment information and converts it into a format suitable for analysis, including removing unnecessary HTML tags and special characters, and converting it into JSON or text file format.

[1008] Analysis with generative AI models

[1009] The preprocessed legal amendment information is analyzed using a generative AI model (e.g., BERT or GPT-3). This identifies the specific content and changes of the legal amendment. For example, the server issues a prompt statement, "Please tell me the key points of the amendments to the new Personal Information Protection Act," to the generative AI model and performs analysis based on the response.

[1010] Identifying the scope of impact

[1011] The server scans the company's internal document database using a full-text search engine (e.g., "full-text search engine") to identify affected documents based on specific keywords or regular expressions. For example, it searches for documents containing keywords such as "personal information" or "encryption."

[1012] Grouping and categorizing relevant sections

[1013] The server groups and categorizes the identified impact areas using a text classification algorithm (e.g., "random forest" or "SVM"), so that impact areas that belong to the same category are displayed together.

[1014] Generate a comparison table of old and new versions

[1015] The server compares the new legal amendments with the old laws and regulations and automatically generates a comparison table. This clearly visualizes which parts of the company will change and how. Specific software tools that can be used include "DiffMatchPatch."

[1016] Compliance assessment

[1017] The server evaluates the legal compliance status of each company and each department. Based on the evaluation results, it is possible to understand the extent to which the company complies with laws and regulations and the progress of necessary measures.

[1018] Providing customized advice

[1019] Based on the results of the assessment, the server generates specific improvement measures and advice tailored to each company and department, such as recommending the latest encryption software for the IT department and training on new encryption processes for the HR department.

[1020] User notification and confirmation

[1021] The server notifies the user's device of the analysis results, the generated comparison table of old and new data, and improvement measures. The user can check this information on their own device and take any necessary measures. Notification methods include email and a dedicated app.

[1022] Specific examples

[1023] For example, let us consider the case where a new Personal Information Protection Act comes into effect.

[1024] 1. The server retrieves information about the new Personal Information Protection Act from the legislation database.

[1025] 2. Preprocess the acquired information and then use a generative AI model to identify "personal information encryption obligations."

[1026] 3. The server scans the company's internal document database to identify documents related to the handling of personal information. For example, a document titled "Current storage method (password protection)" is identified.

[1027] 4. The server groups and categorizes the impact ranges related to "storage method" using a text classification algorithm.

[1028] 5. The server compares the new policy, "Personal information is encrypted and stored," with the old policy, "Stored in a password-protected file," and generates a comparison table of the old and new policies.

[1029] 6. The server evaluates the compliance status of the IT and HR departments. It finds that the IT department has already implemented encryption, but the HR department has not.

[1030] 7. The server recommends that the HR department "install encryption software" and the IT department "regularly update encryption protocols."

[1031] 8. The server notifies the terminals of the personnel in charge of each department of the results of the analysis and a comparison table of the old and new data. The users can check these on their terminals and take any necessary action.

[1032] In this way, the system of the present invention provides a means for companies and organizations to respond quickly and efficiently to legal changes and minimize the risk of non-compliance with laws and regulations or omissions.

[1033] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1034] Step 1:

[1035] The server automatically accesses external law databases and related news feeds to collect legal amendment information. Specifically, it uses an API to send queries to the law database and obtain the latest legal amendment information. The input is a query to the law database, and the output is the obtained legal amendment information.

[1036] Step 2:

[1037] The server preprocesses the acquired legal amendment information and converts it into a format suitable for analysis. For example, it removes unnecessary HTML tags and special characters and converts the data into JSON or text format. This process reformats the data and makes it suitable for the next analysis step. The input is the acquired legal amendment information, and the output is the preprocessed legal amendment information.

[1038] Step 3:

[1039] The server analyzes the preprocessed legal amendment information using a generative AI model (e.g., BERT or GPT-3). For example, it issues a prompt statement, "Please tell me the key points of the amendments to the new Personal Information Protection Act," to the generative AI model and analyzes the response. The input is the preprocessed legal amendment information and the prompt statement, and the output is the specific changes in the legal amendments.

[1040] Step 4:

[1041] The server identifies the internal documents affected by the analyzed revisions based on the content of the revisions. Specifically, it uses a full-text search engine (e.g., Elasticsearch) to scan the internal document database and identifies relevant documents based on specific keywords or regular expressions. The input is the analyzed revisions, and the output is the affected internal documents.

[1042] Step 5:

[1043] The server groups and categorizes the identified internal documents. Specifically, it uses a text classification algorithm (e.g., random forest or SVM) to categorize the identified impact areas. The input is the affected internal documents, and the output is a list of documents classified by category.

[1044] Step 6:

[1045] The server automatically generates a comparison table between the old and new regulations. Specifically, it uses the "DiffMatchPatch" library to compare the new and old regulations and format them to clearly show the differences between the old and new. The input is the old and new legal content, and the output is the comparison table.

[1046] Step 7:

[1047] The server evaluates compliance with laws and regulations for each company and department. For example, it queries employee IDs and department information to evaluate the compliance of current measures with new regulations. The input is each department's current data storage protocols and manual processes, and the output is the compliance assessment results for each department.

[1048] Step 8:

[1049] The server provides customized improvement measures and advice based on the results of the compliance assessment. For example, it recommends "installation of the latest encryption software" to the IT department and "encryption process training" to the HR department. The input is the compliance assessment result, and the output is specific improvement measures and advice.

[1050] Step 9:

[1051] The server notifies the user's device of the analysis results, the generated comparison table between old and new versions, and improvement measures. For example, notifications can be sent to the device of the person in charge via email or a dedicated app. The input is the analysis results, the comparison table between old and new versions, and the improvement measures, and the output is a notification to the user.

[1052] In this way, each step works together to create a system that can effectively respond to legal changes.

[1053] (Application example 1)

[1054] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1055] As legal changes become more frequent in companies and organizations, it is difficult to quickly and accurately collect and analyze information on legal changes and immediately implement specific countermeasures based on that information. Another issue is the lack of a means to efficiently identify the scope of impact of the changes and update corporate security policies accordingly.

[1056] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1057] In this invention, the server includes means for automatically collecting legal amendment information, means for analyzing the collected legal amendment information to identify the amendment details, means for identifying internal documents affected by the identified amendment details, means for grouping and categorizing the identified scope of impact, means for automatically generating a comparison table of old and new information, means for evaluating the legal compliance status of each company or department, means for providing customized improvement measures and advice based on the evaluation results, means for notifying the user's terminal of the provided results, means for updating the company's security policy in real time based on the notified results and improvement measures, and means for presenting specific measures to the user regarding the updated security policy. This enables companies to respond quickly and efficiently to legal amendments, strengthen security, and improve legal compliance.

[1058] "Legal Change Information" means information about laws and regulations that have been updated or changed by governments or regulatory authorities.

[1059] A "collection method" is a mechanism by which the server automatically retrieves information from external databases and related feeds.

[1060] The "means of analysis" refers to a mechanism that uses algorithms or generative AI models to process collected legal amendment information and identify the amendments.

[1061] "Internal documents" are official documents such as reports, guidelines, and procedures used within a company.

[1062] A "grouping and categorization method" is an algorithm for classifying and organizing identified impact areas based on common characteristics.

[1063] A "comparison table of old and new laws" is a document that displays a comparison of the contents of laws and regulations before and after the change.

[1064] "Assessment tools" are mechanisms for determining how well a company or department currently complies with the law.

[1065] "Customized improvement measures and advice" refers to specific improvement methods and suggestions for each organization or department based on the results of the compliance assessment.

[1066] The "notification means" is a communication function for sending analysis results and improvement measures to the user's terminal.

[1067] "Real-time" refers to a time frame within which immediate action can be taken based on the acquisition and analysis of legal change information.

[1068] A "security policy" is a set of operational rules and standards established by a company to protect information.

[1069] "Specific measures" are concrete actions or policies that should be implemented to address a specific problem.

[1070] The present invention provides a system that automatically collects and analyzes legal amendment information, enabling companies and organizations to respond to legal amendments quickly and accurately. This system includes functions such as collecting legal amendment information, analyzing it, identifying the scope of impact, generating a comparison table of the old and new amendments, evaluating compliance with laws and regulations, and providing customized improvement measures and advice. Below, we will explain in detail how each of these steps is implemented.

[1071] The server uses the requests library to automatically access external legal databases and related news feeds to retrieve the latest legal changes, a process that reduces the effort required for companies to manually gather information.

[1072] The collected information is preprocessed using the transformers library to convert it into a format suitable for analysis, and then a generative AI model (e.g., BERT) is used to analyze the legal changes and identify specific amendments.

[1073] To identify the scope of impact, the system scans the company's internal documents and operational rules database, and uses full-text search and regular expressions to identify the areas affected by the identified revisions.The system then uses a text classification algorithm to group and categorize the identified scope of impact, allowing impacts belonging to the same category to be displayed together.

[1074] The server automatically generates a comparison table by comparing the old and new legal content. This comparison table clearly visualizes which parts of the company will be changed and how.

[1075] The server evaluates the compliance status of each company and each department. Based on the results of this evaluation, specific improvement measures and advice customized for each company and department are generated, which helps improve compliance.

[1076] Finally, the server sends the generated analysis results, a comparison table of the old and new versions, and improvement measures to the user's device, where the user can check this information and take any necessary measures.

[1077] As a concrete example, consider the case where a new cybersecurity law has been enacted, resulting in stricter data encryption standards. The server retrieves information about the new cybersecurity law from a legal database, preprocesses the collected information, and then uses a generative AI model to identify "strengthened data encryption standards." Next, it scans the company's internal document database to identify documents related to data encryption. These documents describe the current encryption methods.

[1078] The server groups and categorizes the scope of impact related to "data encryption." It then compares the new regulation, "Enhanced Data Encryption Standard," with the old regulation, "Current Encryption Standard," and generates a comparison table of the old and new standards. The server evaluates the compliance status of the IT department and each department, and based on the evaluation results, recommends that the IT department "introduce new encryption software" and each department "update encryption protocols." The analysis results and the comparison table of the old and new standards are sent to the terminals of the personnel in each department, allowing users to check them on their terminals and take any necessary action.

[1079] This system allows companies and organizations to quickly identify the scope of impact of legal changes and take effective measures. Furthermore, the use of generative AI models and prompt sentences improves analysis accuracy and efficiency, enabling immediate updates to corporate security policies.

[1080] Example prompt sentence:

[1081] "The new Cybersecurity Law has tightened data encryption standards. How will this change affect a company's information security policy? Please suggest specific measures."

[1082] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1083] Step 1:

[1084] The server automatically collects legal change information.

[1085] Input: Legal database URL and related news feed URL.

[1086] Specific operation: Uses the requests library to periodically access legal databases and news feeds to obtain the latest legal changes.

[1087] Output: JSON data of legal amendment information.

[1088] Step 2:

[1089] The server preprocesses the collected legal amendment information and converts it into a format suitable for analysis.

[1090] Input: JSON data of legal amendment information obtained in Step 1.

[1091] Specific operation: The acquired JSON data is structured and converted into a format (text format or token format) that is easy for the generative AI model to parse.

[1092] Output: Preprocessed text data of legal amendment information.

[1093] Step 3:

[1094] The server analyzes the preprocessed legal amendment information using a generative AI model to identify the amendment content.

[1095] Input: Preprocessed text data of legal amendment information.

[1096] What it does: Using the transformers library, we analyze the tokenized data using the BERT model to identify specific modifications.

[1097] Output: Data identifying the amendments (e.g., a list of amendments).

[1098] Step 4:

[1099] The server identifies the internal documents that will be affected by the identified revisions.

[1100] Input: Data identifying amendments and the company's internal document database.

[1101] Specific operation: Using full-text search and regular expressions, internal documents containing revision details are automatically found.

[1102] Output: A list of internal documents that fall within the scope of impact.

[1103] Step 5:

[1104] The server groups and categorizes the impact areas.

[1105] Input: List of internal documents within the scope of impact.

[1106] What it does: Uses a text classification algorithm to group and categorize impact areas based on common characteristics.

[1107] Output: A list of impact areas, grouped and categorized.

[1108] Step 6:

[1109] The server automatically generates a comparison table of the old and new versions.

[1110] Input: List of internal documents included in the amendment and their scope of impact.

[1111] Specific actions: Compare the new and current legal content and create a comparison table.

[1112] Output: Comparison table of old and new versions.

[1113] Step 7:

[1114] The server evaluates compliance with laws and regulations for each company and department.

[1115] Input: Old and new comparison table and company compliance status data.

[1116] Specific operation: Using an evaluation algorithm, determine the current compliance status.

[1117] Output: A report of the evaluation results.

[1118] Step 8:

[1119] The server provides customized remedial measures and advice based on the evaluation results.

[1120] Input: Report of evaluation results.

[1121] Specific actions: Based on the evaluation results, specific improvement measures and advice are generated for each department.

[1122] Output: A customized list of remediation measures and advice.

[1123] Step 9:

[1124] The server notifies the user's device of the analysis results, a comparison table of the old and new data, and improvement measures.

[1125] Input: A customized list of remedies and advice.

[1126] Specific operation: Send a notification to the user's device.

[1127] Output: A notification message visible to the user's device.

[1128] Step 10:

[1129] The server updates the company's security policy in real time based on the notified results and remedial measures.

[1130] Input: Notifications and remediation actions reviewed by the user.

[1131] Specific behavior: Integrates with the company's security management system and reflects necessary changes in real time.

[1132] Output: Updated security policy.

[1133] Step 11:

[1134] The server presents specific measures to the user regarding the updated security policy.

[1135] Input: The updated security policy.

[1136] Specific action: Specific countermeasures are notified to the user's device.

[1137] Output: A notification message with specific countermeasures that can be viewed on the user's device.

[1138] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1139] This invention provides a system that automatically collects and analyzes legal amendment information, enabling companies and organizations to respond to legal amendments quickly and accurately. Furthermore, by combining it with an emotion engine, it provides notifications and remedial measures that take into account the user's emotional state. This system includes multiple processing steps involving a server, terminals, and users.

[1140] About program processing

[1141] 1. Collecting information on legal reforms

[1142] The server automatically accesses external legal databases and related news feeds to obtain the latest legal information. Using APIs and crawling tools, the data is collected and stored in a database.

[1143] 2. Analysis of legal reforms

[1144] The server preprocesses the legal amendment information it obtains, normalizing the data, filtering unnecessary information, standardizing formats, etc. It then analyzes the preprocessed legal amendment information using a generative AI model (e.g., BERT or GPT-3) to identify specific amendments.

[1145] 3. Identifying the scope of impact

[1146] The server accesses the company's internal document database and identifies the affected documents and operational rules based on the analysis results. The affected range is extracted using full-text search and regular expressions.

[1147] 4. Grouping and categorizing relevant sections

[1148] The affected servers are grouped using a text classification algorithm and categorized into categories such as "method of acquisition," "method of storage," and "method of use."

[1149] 5. Generate a comparison table of old and new versions

[1150] The server compares the old and new legal content and automatically generates a comparison table, which clearly shows which parts of the company will be changed and how.

[1151] 6. Compliance Assessment

[1152] The server evaluates the legal compliance status of each company and each department. Based on the information in the database, it evaluates the degree to which each department complies with the law using numerical values ​​and indicators.

[1153] 7. Providing customized advice

[1154] Based on the assessment results, the server generates specific improvement measures and advice customized for each department, including measures tailored to each department's specific situation.

[1155] 8. User Emotion Recognition

[1156] The server uses an emotion engine to analyze the user's emotional state, using voice and text data collected from the user. Based on the analysis, the user's stress and fatigue state are identified.

[1157] 9. Emotion-Based Notification Adjustment

[1158] The server adjusts the notification content and remedial measures based on the user's emotional state. For example, for a user in a high stress state, the server may simplify the notification content or reduce the number of suggestions.

[1159] 10. Notification optimization

[1160] The server optimizes the timing and format of notifications based on the analysis results of the emotion engine, sending notifications at the optimal time to suit the user's work schedule.

[1161] 11. User Notice and Confirmation

[1162] The server notifies the user's device of the analysis results, the generated comparison table of old and new data, and improvement measures. The user can check this information on their device and take any necessary measures.

[1163] 12. Progress Management

[1164] Users update the progress status within the system. For items that have been completed, the status is updated within the system to manage the progress of improvements to legal compliance.

[1165] Specific examples

[1166] Example 1: New Personal Information Protection Law and Emotion Recognition

[1167] 1. Collecting information on legal reforms

[1168] The server retrieves information about the new Personal Information Protection Act from the legislation database.

[1169] 2. Analysis of legal reforms

[1170] The server performs preprocessing and uses a generative AI model to identify "personal information encryption obligations."

[1171] 3. Identifying the scope of impact

[1172] The server scans the company's internal document database to identify documents related to the handling of personal information. The old policy states that these documents should be stored in a password-protected file.

[1173] 4. Grouping and categorizing relevant sections

[1174] The server groups and categorizes the scope of impact regarding "storage method."

[1175] 5. Generate a comparison table of old and new versions

[1176] The server compares the new rule, "Personal information is encrypted and stored," with the old rule, "Stored in a password-protected file," and generates a comparison table of the old and new rules.

[1177] 6. Compliance Assessment

[1178] The server evaluates compliance with IT and HR departments. IT department has implemented encryption, but HR department has not.

[1179] 7. Providing customized advice

[1180] The server recommends that the HR department "install encryption software" and the IT department "regularly update encryption protocols."

[1181] 8. User Emotion Recognition

[1182] The server uses an emotion engine to analyze the voice and text data of the HR department staff member and identify their stress level. It turns out that they are experiencing high levels of stress.

[1183] 9. Emotion-Based Notification Adjustment

[1184] Based on the results of the emotion engine, the server provides HR personnel with simplified notifications and a small number of suggestions.

[1185] 10. Notification optimization

[1186] The server sends a notification after the break according to the person's work schedule.

[1187] 11. User Notice and Confirmation

[1188] The server notifies the terminals of the personnel in each department of the results of the analysis and a comparison table of the old and new data. The users can then check these on their terminals and take any necessary action.

[1189] 12. Progress Management

[1190] Users update their progress within the system and manage compliance improvements.

[1191] This system allows companies and organizations to respond to legal changes quickly and efficiently, minimizing the risk of legal violations and omissions. In addition, by utilizing the emotion engine, users can receive notifications in the most optimal state and carry out their work efficiently.

[1192] The processing flow will be explained below.

[1193] Step 1:

[1194] The server accesses external legal databases and related news feeds to collect legal amendment information. It automatically obtains the latest legal amendment information using APIs and crawling tools and stores it in the database.

[1195] Step 2:

[1196] The server performs preprocessing of the legal amendment information it obtains, normalizing the data, filtering out unnecessary information, standardizing the format, and converting it into a format suitable for analysis.

[1197] Step 3:

[1198] The server analyzes the preprocessed legal amendment information using a generative AI model (e.g., BERT or GPT-3), identifying specific amendments and their scope of application, and extracting them as text.

[1199] Step 4:

[1200] The server accesses the company's internal document database and identifies the documents and operational rules that will be affected based on the analysis results. Related text is extracted using full-text search and regular expressions.

[1201] Step 5:

[1202] The server uses a text classification algorithm to group the identified impact areas and categorize them into categories such as "acquisition method," "storage method," and "usage method."

[1203] Step 6:

[1204] The server compares the old and new legal content and automatically generates a comparison table, which clearly shows which parts of the company will be changed and how.

[1205] Step 7:

[1206] The server-generated comparison table is verified to check for omissions and duplications. An algorithm is used to automatically check and make any necessary corrections.

[1207] Step 8:

[1208] The server evaluates the legal compliance status of each company and department, and evaluates the degree to which each department complies with laws and regulations using numerical values ​​and indicators based on the information in the database.

[1209] Step 9:

[1210] Based on the assessment results, the server generates specific improvement measures and advice customized for each department, including measures tailored to the specific circumstances of each department.

[1211] Step 10:

[1212] The server uses an emotion engine to analyze the user's emotional state. It uses voice and text data collected from the user to identify the emotional state. For example, it uses voice analysis and natural language processing to determine the user's stress level and emotional state.

[1213] Step 11:

[1214] The server adjusts the notification content and improvement measures based on the user's emotional state. For users in a high stress state, the server will simplify the notification content and reduce the number of suggestions. In addition, notifications with low urgency will be postponed, allowing the user to respond in a more appropriate state.

[1215] Step 12:

[1216] The server optimizes the timing and format of notifications based on the analysis results of the emotion engine. Notifications are sent at optimal times according to the user's work schedule and emotional state. For example, notifications can be sent outside peak work hours to reduce the burden on the user.

[1217] Step 13:

[1218] The server notifies the user's device of the analysis results, the generated comparison table of old and new data, and improvement measures. Information is provided promptly via email or push notification. After receiving the notification, the user can check the detailed information on their device and take any necessary measures.

[1219] Step 14:

[1220] Users update the progress status within the system. For items that have been completed, the status is updated within the system to manage the progress of improvements to legal compliance.

[1221] Example 2

[1222] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1223] Modern companies and organizations need to respond quickly and accurately to frequent legal changes, but conventional manual processes require a huge amount of time and effort. This often increases the workload of those in charge, leading to stress and reduced efficiency. This raises concerns about the risk of legal violations and inefficiencies. There is a need for a system that can solve this problem and improve operational efficiency while maintaining legal compliance.

[1224] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for automatically collecting legal amendment information, means for analyzing the collected legal amendment information to identify the amendment content, means for identifying internal documents affected by the identified amendment content, means for grouping and categorizing the identified scope of impact, means for automatically generating a comparison table of old and new versions, means for evaluating the legal compliance status of each organization or department, means for providing customized improvement measures and advice based on the evaluation results, means for notifying the user of the provided results to the user's terminal, means for analyzing the user's emotional state and adjusting the notification content and improvement measures, and means for optimizing the timing and format of notifications. This enables companies and organizations to respond quickly and accurately to legal amendments, reduce the user's workload, and enable efficient and effective legal compliance.

[1225] "Means for automatically collecting information on legal amendments" refers to technology that automatically obtains the latest information on legal amendments from external legal databases and news feeds and stores it in a dedicated database.

[1226] "Means of analyzing collected legal amendment information and identifying the amendment content" refers to technology that preprocesses collected legal amendment information and analyzes and identifies specific amendment points using a generative AI model.

[1227] The "means for identifying internal documents affected by the identified revisions" refers to a technology for searching and identifying relevant documents in a company's internal document database based on the identified revisions.

[1228] "Means for grouping and categorizing the identified impact scope" refers to a technique for classifying the affected documents based on specific criteria and grouping them into categories.

[1229] "Means for automatically generating a comparison table between the old and new laws" refers to technology that compares the contents of the old and new laws and regulations and automatically generates a comparison table that clearly shows the differences.

[1230] "Means for assessing compliance with laws and regulations for each organization or department" refers to technology that evaluates compliance with laws and regulations within a company or each department based on numerical values ​​and indicators and displays the results visually.

[1231] "Means for providing customized improvement measures and advice based on the evaluation results" refers to a technology that provides specific improvement measures and advice individually to each department based on the evaluation results of compliance with laws and regulations.

[1232] "Means for notifying the user of the provided results" refers to technology for notifying the user of the generated analysis results and improvement measures.

[1233] "Means for analyzing the user's emotional state and adjusting notification content and suggested improvements" refers to technology that analyzes collected voice and text data to identify the user's emotional state and adjusts notification content and suggested improvements based on the analysis results.

[1234] "Means for optimizing the timing and format of notifications" refers to technology that delivers notifications at the most appropriate timing and in the most appropriate format, taking into account the user's work schedule and emotional state.

[1235] This invention provides a system that automatically collects and analyzes legal amendment information, enabling companies and organizations to respond to legal amendments quickly and accurately. Furthermore, by combining it with an emotion engine, it provides notifications and remedial measures that take into account the user's emotional state. This system includes multiple processing steps involving a server, terminals, and users.

[1236] First, the server automatically accesses external legal databases (e.g., GovInfo API or general news feeds) and uses APIs or crawling tools (e.g., Scrapy, Requests) to retrieve the latest legal change information. The collected information is then stored in a dedicated database (e.g., MySQL, PostgreSQL).

[1237] Next, the server analyzes the collected legal amendment information. Specifically, it performs preprocessing such as text normalization, filtering unnecessary information, and standardizing data formats. The preprocessed data is used as a prompt for a generative AI model (e.g., GPT-3, BERT), which analyzes the legal amendments and identifies specific amendments. For example, specific amendments such as "the new Personal Information Protection Act requires the encryption of personal information" may be identified.

[1238] The server then accesses the company's internal document database (e.g., Elasticsearch) to identify documents and operational rules that are affected by the identified legal changes. Using full-text search and regular expressions, it identifies documents that state, for example, that the old regulations should be "stored in a password-protected file." This identified scope of impact is then classified into categories such as "storage method" using a text classification algorithm (e.g., tf-idf, LDA).

[1239] When the server generates the comparison table, it compares the old and new legal provisions. For example, it compares the new provision "Personal information shall be encrypted and stored" with the old provision "Stored in a password-protected file," and automatically generates the comparison table. A diff tool (e.g., the Python library difflib) is used to generate this table.

[1240] To assess a company's compliance with laws and regulations, the server uses the information in the database to evaluate the compliance status of each department using numerical values ​​and indicators, and uses a dashboard tool (e.g., Tableau or Power BI) to visually display the results. For example, the server displays the results of the evaluation, such as showing that the IT department has already implemented encryption, but the HR department has not yet done so.

[1241] The server then provides customized advice and remediation measures based on the assessment results, including specific recommendations tailored to the situation of each department. For example, the server might recommend "regularly updating encryption protocols" for the IT department, or "implementing encryption software" for the HR department.

[1242] Furthermore, to understand the user's emotional state, the server performs analysis using an emotion engine (e.g., IBM Watson, Azure Emotion API). The voice and text data collected from the user is analyzed to identify stress and fatigue levels. For example, the emotion engine may identify that a human resources department employee is experiencing high levels of stress.

[1243] Based on the results, the server adjusts the content of notifications and improvement measures. Based on the analysis results of the emotion engine, adjustments are made such as simplifying the content of notifications or reducing the number of suggestions for users who are in a high state of stress. The timing of notifications is also optimized, taking into account the user's work schedule and emotional state. For example, the server checks the work schedule of the person in charge and sends a notification after an appropriate break.

[1244] Finally, the server notifies the user's device of the analysis results, the generated comparison table of old and new versions, and improvement measures. The user can check this information on their device and take any necessary actions. Progress is updated within the system, and the status of items for which action has been completed is updated, allowing the improvement in compliance with laws and regulations to be managed.

[1245] A specific example is the enforcement of a new Personal Information Protection Act. The server collects information about the new Personal Information Protection Act and uses an analytical model to identify the "obligation to encrypt personal information." The internal document database is then scanned to identify and categorize the affected documents. A comparison table of the old and new documents is generated, and the IT and HR departments are assessed for compliance with the law, providing advice to each department. An emotion engine is used to analyze the stress level of employees, and notifications are sent at the appropriate time and in the appropriate format.

[1246] The above processing steps enable companies to respond quickly and accurately to legal changes, preventing legal violations and a decline in business efficiency. In addition, the use of an emotion engine reduces the burden on users and enables them to carry out their work in an optimal state.

[1247] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1248] Step 1:

[1249] The server collects legal amendment information from external legal databases and news feeds. As input, it uses API requests or crawling tools (e.g., Scrapy, Requests) to obtain the latest legal amendment information. The output is the obtained legal amendment information, which is stored in a dedicated database (e.g., MySQL, PostgreSQL).

[1250] Step 2:

[1251] The server analyzes the legal amendment information collected. The input is preprocessed by normalizing the text, filtering unnecessary information, and standardizing the format. Specifically, a text cleaning library (e.g., NLTK, spaCy) is used. The output is the preprocessed data, which is used as a prompt for a generative AI model (e.g., GPT-3, BERT).

[1252] Step 3:

[1253] The server uses the generative AI model to analyze the preprocessed legal amendment information and identify the specific amendments. The input is the data preprocessed in step 2. The output is the identified specific legal amendments. For example, the new Personal Information Protection Act identifies the "obligation to encrypt personal information."

[1254] Step 4:

[1255] The server accesses the company's internal document database and identifies documents affected by the identified legal changes. The input is the legal changes identified in step 3. This process uses full-text search and regular expressions (e.g., Elasticsearch). The output is the specific internal documents that are affected. For example, documents that state "save in a password-protected file" are identified.

[1256] Step 5:

[1257] The server groups and categorizes the identified impact scope documents. The input is the impact scope documents identified in step 4. A text classification algorithm (e.g., tf-idf, LDA) is used to classify the documents into categories such as "storage method." The output is the classified impact scope documents.

[1258] Step 6:

[1259] The server compares the old and new legal documents and generates a comparison table. The input is the old and new legal documents. Specifically, a diff tool (e.g., the Python library difflib) is used to automatically generate the comparison table. The output is the comparison table. This clearly shows which parts of the company have been changed and how.

[1260] Step 7:

[1261] The server evaluates the legal compliance status for each department of the company. The input is the legal compliance status data stored for each department of the company. Based on this data, compliance status is evaluated using numerical values ​​and indicators, and a dashboard tool (e.g., Tableau, Power BI) is used to visually display the results. The output is the evaluation results of legal compliance status for each department.

[1262] Step 8:

[1263] Based on the assessment results, the server provides customized advice and improvement measures for each department. The input is the assessment results obtained in step 7. Specific improvement measures tailored to the specific circumstances of each department are included. The output is customized advice and improvement measures. For example, the server may recommend "regularly updating encryption protocols" for the IT department and "implementing encryption software" for the HR department.

[1264] Step 9:

[1265] The server uses an emotion engine to analyze the user's emotional state. The input is voice and text data collected from the user. Analysis is performed using an emotion engine (e.g., IBM Watson, Azure Emotion API) to identify the user's stress and fatigue state. The output is the analyzed user's emotional state.

[1266] Step 10:

[1267] The server adjusts the notification content and remedial measures based on the user's emotional state. The input is the user's emotional state obtained in step 9. For users in a high stress state, the notification content is simplified or the suggestions are reduced. The output is the adjusted notification content and remedial measures.

[1268] Step 11:

[1269] The server sends the notification at the optimal timing and in the optimal format. The input is the notification content adjusted in step 10 and the user's work schedule. The output is the notification sent to the user's device. For example, the notification may be sent after a break, taking into account the person's work schedule.

[1270] Step 12:

[1271] The user checks the notified content and takes any necessary action. The input is the notification content sent from the server. Based on this, the user takes specific action to comply with the law and updates the progress status within the system. The output is the updated progress status. For items for which action has been completed, the status is updated within the system and the improvement in compliance with the law is managed.

[1272] (Application example 2)

[1273] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1274] In order for companies and organizations to respond quickly and accurately to legal changes, they need a system that automatically collects and analyzes the latest legal change information. However, with conventional systems, the process of identifying which parts of the company the change will affect and providing appropriate countermeasures is complicated, resulting in delayed responses. Furthermore, information is provided with uniform notification content and timing without taking into account the emotional state of the user receiving the notification, which increases the burden on users and risks reducing work efficiency. There is a need to solve these issues and provide a system that allows companies to respond quickly and accurately to legal change information.

[1275] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1276] In this invention, the server includes means for automatically collecting legal amendment information, means for analyzing the collected legal amendment information to identify the amendment details, means for identifying internal documents affected by the identified amendment details, means for grouping and categorizing the identified scope of impact, means for automatically generating a comparison table of old and new versions, means for evaluating the legal compliance status of each company or department, means for providing customized improvement measures and advice based on the evaluation results, means for notifying the user of the provided results to their terminal, means for analyzing the user's emotional state, means for adjusting the content and timing of notifications based on the user's emotional state, and means for managing progress. This enables companies to respond to legal amendments quickly and efficiently and reduces the burden on users by taking their emotional state into consideration.

[1277] "Means for automatically collecting information on legal amendments" refers to a function that accesses external legal databases and related news feeds, automatically obtains the latest legal amendment information, and stores it in a database.

[1278] The "means of analyzing collected legal amendment information and identifying the amendment content" refers to a function that normalizes and filters collected data and uses a generative AI model to extract specific amendment points.

[1279] "Means to identify internal documents that will be affected by the identified revisions" refers to a function that accesses a company's internal document database and identifies the documents and operational rules that will be affected based on the analysis results.

[1280] The "means for grouping and categorizing the identified impact ranges" is a function for grouping and categorizing the identified impact ranges using a text classification algorithm.

[1281] The "means for automatically generating a comparison table between the old and new laws and regulations" is a function that automatically generates a comparison table that compares the contents of the old and new laws and regulations and clearly shows which parts have been changed and how.

[1282] "Means for evaluating compliance with laws and regulations for each company and department" is a function that evaluates the degree to which each department complies with laws and regulations using numerical values ​​and indicators based on information in the database.

[1283] "Means for providing customized improvement measures and advice based on the evaluation results" refers to a function that generates specific improvement measures and advice tailored to the specific circumstances of each department based on the evaluation results.

[1284] The "means for notifying the user of the provided results" is a function for notifying the user of the analysis results, the generated comparison table between the old and new data, and improvement measures to the user's terminal.

[1285] The "means for analyzing the user's emotional state" is a function that analyzes voice and text data collected from the user and identifies the user's stress and fatigue state using an emotion engine.

[1286] "Means for adjusting notification content and timing based on emotional state" refers to a function that optimizes the timing and format of notifications by taking into account the user's emotional state, simplifying the notification content, or reducing the amount of suggested content.

[1287] The "means for managing progress" is a function that updates the progress of items handled by the user on the system and manages the progress of improvements in compliance with laws and regulations.

[1288] This invention is a system that automatically collects and analyzes legal amendment information, enabling companies and organizations to respond to legal amendments quickly and accurately. Furthermore, by combining it with an emotion engine, it is possible to provide notifications and remedial measures that take into account the user's emotional state. This system is configured using the following means.

[1289] 1. Collecting information on legal reforms

[1290] The server automatically accesses external legal databases and related news feeds to obtain the latest legal information. Specific technologies used include APIs and crawling tools. The obtained data is stored on the server.

[1291] 2. Analysis of legal reforms

[1292] The server normalizes and filters the collected data and identifies specific corrections using generative AI models (e.g., BERT or GPT-3). The data preprocessing stage removes unnecessary information and standardizes the format.

[1293] 3. Identifying the scope of impact

[1294] The server accesses the company's internal document database and identifies the documents and operational rules that will be affected based on the analysis results. It then uses full-text search and regular expressions to extract the relevant documents from the database.

[1295] 4. Grouping and Categorization

[1296] The server uses a text classification algorithm to group the identified impact areas into categories such as "acquisition method," "storage method," and "usage method."

[1297] 5. Generate a comparison table of old and new versions

[1298] The server compares the old and new legal content and automatically generates a comparison table, which clearly shows which parts have been changed and how.

[1299] 6. Compliance Assessment

[1300] The server evaluates the legal compliance status of each company and each department, and based on the information in the database, evaluates the degree to which each department complies with the law using numerical values ​​and indicators.

[1301] 7. Providing customized advice

[1302] Based on the evaluation results, the server generates specific improvement measures and advice tailored to each department's specific situation.

[1303] 8. User Emotion Recognition

[1304] The server uses an emotion engine to analyze the user's emotional state, using voice and text data collected from the user. Based on the analysis, the user's stress and fatigue state are identified.

[1305] 9. Adjustment of notification content and timing

[1306] The server optimizes the timing and format of notifications by simplifying the content of notifications or reducing the number of suggestions, taking into account the user's emotional state.

[1307] 10. User Notice and Confirmation

[1308] The server notifies the user's device of the analysis results, the generated comparison table of old and new data, and improvement measures. The user can check this information on their device and take the necessary measures.

[1309] 11. Progress Management

[1310] Users can update the progress of their actions within the system and manage their compliance improvement.

[1311] This system is built using cloud servers (e.g., AWS, Google Cloud) and external APIs (legal database API, sentiment analysis API). BERT and GPT-3 are used as generative AI models.

[1312] Examples:

[1313] The new Personal Information Protection Law requires employees' personal information to be encrypted rather than kept in password-protected files. The system automatically detects this and advises IT departments to regularly update their existing encryption systems and HR departments to implement encryption software. It also uses emotion recognition to streamline notifications for stressed users.

[1314] Example prompts to input to a generative AI model:

[1315] "Analyze the latest changes to the Personal Information Protection Act and identify how they affect your company's internal documents."

[1316] This system allows companies and organizations to respond quickly and efficiently to legal changes and reduces the burden on users by taking into account their emotional state.

[1317] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1318] Step 1:

[1319] Automatic collection of legal reform information

[1320] The server automatically accesses external legal databases and news feeds to obtain the latest legal amendment information. Specifically, it sends API requests and stores the obtained data in an internal database. The input is the API request, and the output is legal amendment information data.

[1321] Step 2:

[1322] Analysis of legal reforms

[1323] The server normalizes the collected legal amendment information and filters unnecessary information. Next, it uses a generative AI model (e.g., BERT or GPT-3) to identify specific amendments. The input is the collected legal amendment information, and the output is the analyzed amendment content. The specific operations are text normalization, filtering, and application of the AI ​​model.

[1324] Step 3:

[1325] Identifying the scope of impact

[1326] The server accesses the company's internal document database and identifies the affected documents and operational rules based on the analysis results. Specifically, it extracts the relevant documents from the database using full-text search and regular expressions. The input is the analyzed revision details, and the output is a list of affected internal documents.

[1327] Step 4:

[1328] Grouping and Categorization

[1329] The server uses a text classification algorithm to group the identified impact areas and categorize them into categories such as acquisition method, storage method, and usage method. The input is a list of affected internal documents, and the output is a categorized document list. The specific operation is the application of a text classification algorithm.

[1330] Step 5:

[1331] Generate a comparison table of old and new versions

[1332] The server compares the old and new legal content and automatically generates a comparison table. The input is the old and new legal content, and the output is the comparison table. The specific operation is to compare data and generate a format.

[1333] Step 6:

[1334] Compliance assessment

[1335] The server evaluates the legal compliance status of each company and each department based on the information in the database. The input is a categorized document list and operational data within the company, and the output is the evaluation result of the legal compliance status. Specific operations include analyzing the data and generating evaluation indicators.

[1336] Step 7:

[1337] Providing customized advice

[1338] Based on the evaluation results, the server generates specific improvement measures and advice tailored to the specific circumstances of each department. The input is the evaluation results of compliance status, and the output is advice customized for each department. The specific operation is to analyze the evaluation results and generate advice.

[1339] Step 8:

[1340] User Emotion Recognition

[1341] The server uses an emotion engine to analyze the user's emotional state. The input is voice and text data collected from the user, and the output is the analysis result of the emotional state. The specific operation involves analyzing the voice and text data and applying the emotion recognition engine.

[1342] Step 9:

[1343] Adjust notification content and timing

[1344] The server optimizes the timing and format of notifications by simplifying the content of notifications or reducing the number of suggestions, taking into account the user's emotional state. The input is the analysis result of the emotional state and advice on improvement measures, and the output is the adjusted notification content. The specific operation is to edit the notification content and adjust the timing.

[1345] Step 10:

[1346] User notification and confirmation

[1347] The server notifies the user's device of the analysis results, the generated comparison table of old and new data, and improvement measures. The input is the adjusted notification content, and the output is the notification information displayed on the user's device. The specific operation is to send and display the notification.

[1348] Step 11:

[1349] Progress Management

[1350] Users update the progress of corresponding items within the system and manage the progress of improvements to legal compliance. The input is the user's updated progress data, and the output is the updated progress data. The specific operation is to update the progress database.

[1351] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1352] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1353] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1354] [Fourth embodiment]

[1355] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1356] 7, a 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.

[1357] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1358] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1359] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1360] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1361] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1362] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1363] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1364] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1365] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1366] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1367] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1368] The present invention provides a system that automatically collects and analyzes legal amendment information, enabling companies and organizations to respond to legal amendments quickly and accurately. This system includes multiple processing steps involving servers, terminals, and users.

[1369] About program processing

[1370] 1. Collecting information on legal reforms

[1371] The server automatically accesses external legal databases and related news feeds to retrieve the latest legal information, eliminating the need for companies to manually gather information.

[1372] 2. Analysis of legal reforms

[1373] The server preprocesses the legal amendment information it obtains and converts it into a format suitable for analysis. It then uses a generative AI model (e.g., BERT or GPT-3) to analyze the legal amendments and identify the specific amendments.

[1374] 3. Identifying the scope of impact

[1375] The server scans the company's internal documents and operational rules database to automatically identify areas that the identified revisions will affect, using full-text search and regular expressions in the process.

[1376] 4. Grouping and categorizing relevant sections

[1377] The server groups and categorizes the identified impact areas using a text classification algorithm, so that impact areas that belong to the same category are displayed together.

[1378] 5. Generate a comparison table of old and new versions

[1379] The server compares the old and new legal content and automatically generates a comparison table that clearly visualizes which parts of the company will be changed and how.

[1380] 6. Compliance Assessment

[1381] The server evaluates the legal compliance status of each company and each department. The evaluation results indicate the extent to which the company complies with laws and regulations and allow the user to understand the progress of necessary measures.

[1382] 7. Providing customized advice

[1383] Based on the evaluation results, the server generates specific improvement measures and advice customized for each company or department, thereby improving compliance with laws and regulations.

[1384] 8. User Notification and Confirmation

[1385] The server notifies the user's device of the analysis results, the generated comparison table of the old and new versions, and improvement measures. The user can check this information on their own device and take any necessary measures.

[1386] Specific examples

[1387] Example 1: Enactment of the new Personal Information Protection Law

[1388] 1. Collecting information on legal reforms

[1389] The server retrieves information about the new Personal Information Protection Act from the legislation database.

[1390] 2. Analysis of legal reforms

[1391] The server preprocesses the information it receives and then uses a generative AI model to identify "personal information encryption obligations."

[1392] 3. Identifying the scope of impact

[1393] The server scans the company's internal document database to identify documents related to the handling of personal information, which contain information about the current storage method (password protection).

[1394] 4. Grouping and categorizing relevant sections

[1395] The server groups and categorizes the scope of influence regarding the "storage method."

[1396] 5. Generate a comparison table of old and new versions

[1397] The server compares the new rule, "Personal information is encrypted and stored," with the old rule, "Stored in a password-protected file," and generates a comparison table of the old and new rules.

[1398] 6. Compliance Assessment

[1399] The server evaluates the compliance status of the IT and HR departments. It finds that the IT department has already implemented encryption, but the HR department has not.

[1400] 7. Providing customized advice

[1401] The server recommends that the HR department "install encryption software" and the IT department "regularly update encryption protocols."

[1402] 8. User Notification and Confirmation

[1403] The server notifies the terminals of the personnel in each department of the results of the analysis and a comparison table of the old and new data. The users can then check these on their terminals and take any necessary action.

[1404] This system allows companies and organizations to respond quickly and efficiently to legal changes and minimize the risk of non-compliance with laws and regulations or omissions.

[1405] The processing flow will be explained below.

[1406] Step 1:

[1407] The server accesses external legal databases and related news feeds to collect legal amendment information. Using APIs and crawling tools, the latest legal amendment information is automatically retrieved and stored in the database.

[1408] Step 2:

[1409] The server preprocesses the acquired legal amendment information, normalizing the data, filtering unnecessary information, standardizing the format, and converting it into a format suitable for analysis.

[1410] Step 3:

[1411] The server analyzes the preprocessed legal amendment information using a generative AI model (e.g., BERT or GPT-3), identifying specific amendments and their scope of application, and extracting them as text.

[1412] Step 4:

[1413] The server accesses the company's internal document database and identifies the documents and operational rules that will be affected based on the analysis results. It then uses full-text search and regular expressions to extract relevant text.

[1414] Step 5:

[1415] The server groups and categorizes the identified impact areas, using a text classification algorithm to categorize the impact areas into categories such as "acquisition method," "storage method," and "usage method."

[1416] Step 6:

[1417] The server generates an initial version of the comparison table, comparing the old and new legal content and creating a comparison table in a format that clearly shows the changes for each article.

[1418] Step 7:

[1419] The server verifies the old and new tables to check for omissions and duplications, automatically checking using an algorithm and making any necessary corrections.

[1420] Step 8:

[1421] The server evaluates the legal compliance status of each company and department, and evaluates the degree to which each department complies with laws and regulations using numerical values ​​and indicators based on the information in the database.

[1422] Step 9:

[1423] Based on the results of the assessment, the server generates customized improvement measures and advice, such as proposing specific measures such as "installing encryption software" and "performing regular audits" for each department.

[1424] Step 10:

[1425] The server notifies the user's device of the analysis results, the generated comparison table of old and new data, and improvement measures. Information is provided promptly via email or push notification.

[1426] Step 11:

[1427] The user checks the notification on the device, reviews the analysis results, comparison table of old and new data, and improvement measures, and takes action to implement the necessary measures.

[1428] Step 12:

[1429] Users update the progress status within the system. For items that have been completed, the status is updated within the system to manage the progress of improvements to legal compliance.

[1430] Example 1

[1431] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1432] In order for companies and organizations to respond quickly and accurately to legal changes, they must manually collect and analyze vast amounts of legal change information and identify the extent of impact within the company. This requires significant human resources and time, and specialized knowledge is also required to evaluate compliance and propose improvement measures. This puts companies and organizations at risk of violating laws and regulations and is likely to suffer disadvantages due to delayed response. To solve these issues, a system is needed that automatically collects and analyzes legal change information, identifies the extent of impact, and provides appropriate improvement measures.

[1433] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1434] In this invention, the server includes means for automatically collecting legal amendment information, means for preprocessing the collected legal amendment information, means for using a generative AI model to analyze the preprocessed legal amendment information, means for generating prompt sentences based on the analyzed amendment content, means for using a full-text search engine to search for internal documents affected by the amendment content, means for applying a text classification algorithm to group and categorize the searched documents, means for automatically generating a comparison table of old and new documents, means for evaluating the legal compliance status of each company or department, means for providing customized improvement measures and advice based on the evaluation results, and means for notifying the user's terminal of the provided results, thereby enabling companies and organizations to respond quickly and efficiently to legal amendments.

[1435] "Legal amendment information" refers to information regarding changes or amendments to laws and regulations.

[1436] "Preprocessing" refers to the process of filtering and shaping data to convert it into a form suitable for analysis.

[1437] "Generative AI models" refer to algorithms or systems that use artificial intelligence techniques to analyze data. Examples include BERT and GPT-3.

[1438] A "prompt" refers to text in the form of instructions or questions that are input into a generative AI model.

[1439] A "full-text search engine" refers to a software system for searching large amounts of text data for specific keywords or phrases. Specific examples include Elasticsearch.

[1440] "Text classification algorithm" refers to a machine learning algorithm for classifying text data into specific categories or groups. Examples include random forests and support vector machines (SVMs).

[1441] The "old and new comparison table" refers to a table that lists and compares the contents of new laws and regulations with the contents of old laws and regulations.

[1442] "Compliance with laws and regulations" refers to the extent to which a company or organization operates in compliance with current laws and regulations.

[1443] "Customized solutions and advice" refers to specific solutions and advice tailored to the specific circumstances of a company or sector.

[1444] "User device" refers to a device such as a computer, smartphone, or tablet used by the ultimate recipient.

[1445] The present invention provides a system that automatically collects and analyzes legal amendment information, enabling companies and organizations to respond to legal amendments quickly and accurately. This system includes multiple processing steps involving servers, terminals, and users.

[1446] Collecting information on legal reforms

[1447] The server automatically accesses an external legal database (e.g., "legal database") or news feed to obtain the latest legal amendment information. For example, the server can use an API to send a query to a legal database and collect the necessary legal amendment information. This automatic collection reduces the effort required for companies to manually collect information.

[1448] Pre-processing of legal amendments

[1449] The server preprocesses the collected legal amendment information and converts it into a format suitable for analysis, including removing unnecessary HTML tags and special characters, and converting it into JSON or text file format.

[1450] Analysis with generative AI models

[1451] The preprocessed legal amendment information is analyzed using a generative AI model (e.g., BERT or GPT-3). This identifies the specific content and changes of the legal amendment. For example, the server issues a prompt statement, "Please tell me the key points of the amendments to the new Personal Information Protection Act," to the generative AI model and performs analysis based on the response.

[1452] Identifying the scope of impact

[1453] The server scans the company's internal document database using a full-text search engine (e.g., "full-text search engine") to identify affected documents based on specific keywords or regular expressions. For example, it searches for documents containing keywords such as "personal information" or "encryption."

[1454] Grouping and categorizing relevant sections

[1455] The server groups and categorizes the identified impact areas using a text classification algorithm (e.g., "random forest" or "SVM"), so that impact areas that belong to the same category are displayed together.

[1456] Generate a comparison table of old and new versions

[1457] The server compares the new legal amendments with the old laws and regulations and automatically generates a comparison table. This clearly visualizes which parts of the company will change and how. Specific software tools that can be used include "DiffMatchPatch."

[1458] Compliance assessment

[1459] The server evaluates the legal compliance status of each company and each department. Based on the evaluation results, it is possible to understand the extent to which the company complies with laws and regulations and the progress of necessary measures.

[1460] Providing customized advice

[1461] Based on the results of the assessment, the server generates specific improvement measures and advice tailored to each company and department, such as recommending the latest encryption software for the IT department and training on new encryption processes for the HR department.

[1462] User notification and confirmation

[1463] The server notifies the user's device of the analysis results, the generated comparison table of old and new data, and improvement measures. The user can check this information on their own device and take any necessary measures. Notification methods include email and a dedicated app.

[1464] Specific examples

[1465] For example, let us consider the case where a new Personal Information Protection Act comes into effect.

[1466] 1. The server retrieves information about the new Personal Information Protection Act from the legislation database.

[1467] 2. Preprocess the acquired information and then use a generative AI model to identify "personal information encryption obligations."

[1468] 3. The server scans the company's internal document database to identify documents related to the handling of personal information. For example, a document titled "Current storage method (password protection)" is identified.

[1469] 4. The server groups and categorizes the impact ranges related to "storage method" using a text classification algorithm.

[1470] 5. The server compares the new policy, "Personal information is encrypted and stored," with the old policy, "Stored in a password-protected file," and generates a comparison table of the old and new policies.

[1471] 6. The server evaluates the compliance status of the IT and HR departments. It finds that the IT department has already implemented encryption, but the HR department has not.

[1472] 7. The server recommends that the HR department "install encryption software" and the IT department "regularly update encryption protocols."

[1473] 8. The server notifies the terminals of the personnel in charge of each department of the results of the analysis and a comparison table of the old and new data. The users can check these on their terminals and take any necessary action.

[1474] In this way, the system of the present invention provides a means for companies and organizations to respond quickly and efficiently to legal changes and minimize the risk of non-compliance with laws and regulations or omissions.

[1475] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1476] Step 1:

[1477] The server automatically accesses external law databases and related news feeds to collect legal amendment information. Specifically, it uses an API to send queries to the law database and obtain the latest legal amendment information. The input is a query to the law database, and the output is the obtained legal amendment information.

[1478] Step 2:

[1479] The server preprocesses the acquired legal amendment information and converts it into a format suitable for analysis. For example, it removes unnecessary HTML tags and special characters and converts the data into JSON or text format. This process reformats the data and makes it suitable for the next analysis step. The input is the acquired legal amendment information, and the output is the preprocessed legal amendment information.

[1480] Step 3:

[1481] The server analyzes the preprocessed legal amendment information using a generative AI model (e.g., BERT or GPT-3). For example, it issues a prompt statement, "Please tell me the key points of the amendments to the new Personal Information Protection Act," to the generative AI model and analyzes the response. The input is the preprocessed legal amendment information and the prompt statement, and the output is the specific changes in the legal amendments.

[1482] Step 4:

[1483] The server identifies the internal documents affected by the analyzed revisions based on the content of the revisions. Specifically, it uses a full-text search engine (e.g., Elasticsearch) to scan the internal document database and identifies relevant documents based on specific keywords or regular expressions. The input is the analyzed revisions, and the output is the affected internal documents.

[1484] Step 5:

[1485] The server groups and categorizes the identified internal documents. Specifically, it uses a text classification algorithm (e.g., random forest or SVM) to categorize the identified impact areas. The input is the affected internal documents, and the output is a list of documents classified by category.

[1486] Step 6:

[1487] The server automatically generates a comparison table between the old and new regulations. Specifically, it uses the "DiffMatchPatch" library to compare the new and old regulations and format them to clearly show the differences between the old and new. The input is the old and new legal content, and the output is the comparison table.

[1488] Step 7:

[1489] The server evaluates compliance with laws and regulations for each company and department. For example, it queries employee IDs and department information to evaluate the compliance of current measures with new regulations. The input is each department's current data storage protocols and manual processes, and the output is the compliance assessment results for each department.

[1490] Step 8:

[1491] The server provides customized improvement measures and advice based on the results of the compliance assessment. For example, it recommends "installation of the latest encryption software" to the IT department and "encryption process training" to the HR department. The input is the compliance assessment result, and the output is specific improvement measures and advice.

[1492] Step 9:

[1493] The server notifies the user's device of the analysis results, the generated comparison table between old and new versions, and improvement measures. For example, notifications can be sent to the device of the person in charge via email or a dedicated app. The input is the analysis results, the comparison table between old and new versions, and the improvement measures, and the output is a notification to the user.

[1494] In this way, each step works together to create a system that can effectively respond to legal changes.

[1495] (Application example 1)

[1496] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1497] As legal changes become more frequent in companies and organizations, it is difficult to quickly and accurately collect and analyze information on legal changes and immediately implement specific countermeasures based on that information. Another issue is the lack of a means to efficiently identify the scope of impact of the changes and update corporate security policies accordingly.

[1498] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1499] In this invention, the server includes means for automatically collecting legal amendment information, means for analyzing the collected legal amendment information to identify the amendment details, means for identifying internal documents affected by the identified amendment details, means for grouping and categorizing the identified scope of impact, means for automatically generating a comparison table of old and new information, means for evaluating the legal compliance status of each company or department, means for providing customized improvement measures and advice based on the evaluation results, means for notifying the user's terminal of the provided results, means for updating the company's security policy in real time based on the notified results and improvement measures, and means for presenting specific measures to the user regarding the updated security policy. This enables companies to respond quickly and efficiently to legal amendments, strengthen security, and improve legal compliance.

[1500] "Legal Change Information" means information about laws and regulations that have been updated or changed by governments or regulatory authorities.

[1501] A "collection method" is a mechanism by which the server automatically retrieves information from external databases and related feeds.

[1502] The "means of analysis" refers to a mechanism that uses algorithms or generative AI models to process collected legal amendment information and identify the amendments.

[1503] "Internal documents" are official documents such as reports, guidelines, and procedures used within a company.

[1504] A "grouping and categorization method" is an algorithm for classifying and organizing identified impact areas based on common characteristics.

[1505] A "comparison table of old and new laws" is a document that displays a comparison of the contents of laws and regulations before and after the change.

[1506] "Assessment tools" are mechanisms for determining how well a company or department currently complies with the law.

[1507] "Customized improvement measures and advice" refers to specific improvement methods and suggestions for each organization or department based on the results of the compliance assessment.

[1508] The "notification means" is a communication function for sending analysis results and improvement measures to the user's terminal.

[1509] "Real-time" refers to a time frame within which immediate action can be taken based on the acquisition and analysis of legal change information.

[1510] A "security policy" is a set of operational rules and standards established by a company to protect information.

[1511] "Specific measures" are concrete actions or policies that should be implemented to address a specific problem.

[1512] The present invention provides a system that automatically collects and analyzes legal amendment information, enabling companies and organizations to respond to legal amendments quickly and accurately. This system includes functions such as collecting legal amendment information, analyzing it, identifying the scope of impact, generating a comparison table of the old and new amendments, evaluating compliance with laws and regulations, and providing customized improvement measures and advice. Below, we will explain in detail how each of these steps is implemented.

[1513] The server uses the requests library to automatically access external legal databases and related news feeds to retrieve the latest legal changes, a process that reduces the effort required for companies to manually gather information.

[1514] The collected information is preprocessed using the transformers library to convert it into a format suitable for analysis, and then a generative AI model (e.g., BERT) is used to analyze the legal changes and identify specific amendments.

[1515] To identify the scope of impact, the system scans the company's internal documents and operational rules database, and uses full-text search and regular expressions to identify the areas affected by the identified revisions.The system then uses a text classification algorithm to group and categorize the identified scope of impact, allowing impacts belonging to the same category to be displayed together.

[1516] The server automatically generates a comparison table by comparing the old and new legal content. This comparison table clearly visualizes which parts of the company will be changed and how.

[1517] The server evaluates the compliance status of each company and each department. Based on the results of this evaluation, specific improvement measures and advice customized for each company and department are generated, which helps improve compliance.

[1518] Finally, the server sends the generated analysis results, a comparison table of the old and new versions, and improvement measures to the user's device, where the user can check this information and take any necessary measures.

[1519] As a concrete example, consider the case where a new cybersecurity law has been enacted, resulting in stricter data encryption standards. The server retrieves information about the new cybersecurity law from a legal database, preprocesses the collected information, and then uses a generative AI model to identify "strengthened data encryption standards." Next, it scans the company's internal document database to identify documents related to data encryption. These documents describe the current encryption methods.

[1520] The server groups and categorizes the scope of impact related to "data encryption." It then compares the new regulation, "Enhanced Data Encryption Standard," with the old regulation, "Current Encryption Standard," and generates a comparison table of the old and new standards. The server evaluates the compliance status of the IT department and each department, and based on the evaluation results, recommends that the IT department "introduce new encryption software" and each department "update encryption protocols." The analysis results and the comparison table of the old and new standards are sent to the terminals of the personnel in each department, allowing users to check them on their terminals and take any necessary action.

[1521] This system allows companies and organizations to quickly identify the scope of impact of legal changes and take effective measures. Furthermore, the use of generative AI models and prompt sentences improves analysis accuracy and efficiency, enabling immediate updates to corporate security policies.

[1522] Example prompt sentence:

[1523] "The new Cybersecurity Law has tightened data encryption standards. How will this change affect a company's information security policy? Please suggest specific measures."

[1524] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1525] Step 1:

[1526] The server automatically collects legal change information.

[1527] Input: Legal database URL and related news feed URL.

[1528] Specific operation: Uses the requests library to periodically access legal databases and news feeds to obtain the latest legal changes.

[1529] Output: JSON data of legal amendment information.

[1530] Step 2:

[1531] The server preprocesses the collected legal amendment information and converts it into a format suitable for analysis.

[1532] Input: JSON data of legal amendment information obtained in Step 1.

[1533] Specific operation: The acquired JSON data is structured and converted into a format (text format or token format) that is easy for the generative AI model to parse.

[1534] Output: Preprocessed text data of legal amendment information.

[1535] Step 3:

[1536] The server analyzes the preprocessed legal amendment information using a generative AI model to identify the amendment content.

[1537] Input: Preprocessed text data of legal amendment information.

[1538] What it does: Using the transformers library, we analyze the tokenized data using the BERT model to identify specific modifications.

[1539] Output: Data identifying the amendments (e.g., a list of amendments).

[1540] Step 4:

[1541] The server identifies the internal documents that will be affected by the identified revisions.

[1542] Input: Data identifying amendments and the company's internal document database.

[1543] Specific operation: Using full-text search and regular expressions, internal documents containing revision details are automatically found.

[1544] Output: A list of internal documents that fall within the scope of impact.

[1545] Step 5:

[1546] The server groups and categorizes the impact areas.

[1547] Input: List of internal documents within the scope of impact.

[1548] What it does: Uses a text classification algorithm to group and categorize impact areas based on common characteristics.

[1549] Output: A list of impact areas, grouped and categorized.

[1550] Step 6:

[1551] The server automatically generates a comparison table of the old and new versions.

[1552] Input: List of internal documents included in the amendment and their scope of impact.

[1553] Specific actions: Compare the new and current legal content and create a comparison table.

[1554] Output: Comparison table of old and new versions.

[1555] Step 7:

[1556] The server evaluates compliance with laws and regulations for each company and department.

[1557] Input: Old and new comparison table and company compliance status data.

[1558] Specific operation: Using an evaluation algorithm, determine the current compliance status.

[1559] Output: A report of the evaluation results.

[1560] Step 8:

[1561] The server provides customized remedial measures and advice based on the evaluation results.

[1562] Input: Report of evaluation results.

[1563] Specific actions: Based on the evaluation results, specific improvement measures and advice are generated for each department.

[1564] Output: A customized list of remediation measures and advice.

[1565] Step 9:

[1566] The server notifies the user's device of the analysis results, a comparison table of the old and new data, and improvement measures.

[1567] Input: A customized list of remedies and advice.

[1568] Specific operation: Send a notification to the user's device.

[1569] Output: A notification message visible to the user's device.

[1570] Step 10:

[1571] The server updates the company's security policy in real time based on the notified results and remedial measures.

[1572] Input: Notifications and remediation actions reviewed by the user.

[1573] Specific behavior: Integrates with the company's security management system and reflects necessary changes in real time.

[1574] Output: Updated security policy.

[1575] Step 11:

[1576] The server presents specific measures to the user regarding the updated security policy.

[1577] Input: The updated security policy.

[1578] Specific action: Specific countermeasures are notified to the user's device.

[1579] Output: A notification message with specific countermeasures that can be viewed on the user's device.

[1580] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1581] This invention provides a system that automatically collects and analyzes legal amendment information, enabling companies and organizations to respond to legal amendments quickly and accurately. Furthermore, by combining it with an emotion engine, it provides notifications and remedial measures that take into account the user's emotional state. This system includes multiple processing steps involving a server, terminals, and users.

[1582] About program processing

[1583] 1. Collecting information on legal reforms

[1584] The server automatically accesses external legal databases and related news feeds to obtain the latest legal information. Using APIs and crawling tools, the data is collected and stored in a database.

[1585] 2. Analysis of legal reforms

[1586] The server preprocesses the legal amendment information it obtains, normalizing the data, filtering unnecessary information, standardizing formats, etc. It then analyzes the preprocessed legal amendment information using a generative AI model (e.g., BERT or GPT-3) to identify specific amendments.

[1587] 3. Identifying the scope of impact

[1588] The server accesses the company's internal document database and identifies the affected documents and operational rules based on the analysis results. The affected range is extracted using full-text search and regular expressions.

[1589] 4. Grouping and categorizing relevant sections

[1590] The affected servers are grouped using a text classification algorithm and categorized into categories such as "method of acquisition," "method of storage," and "method of use."

[1591] 5. Generate a comparison table of old and new versions

[1592] The server compares the old and new legal content and automatically generates a comparison table, which clearly shows which parts of the company will be changed and how.

[1593] 6. Compliance Assessment

[1594] The server evaluates the legal compliance status of each company and each department. Based on the information in the database, it evaluates the degree to which each department complies with the law using numerical values ​​and indicators.

[1595] 7. Providing customized advice

[1596] Based on the assessment results, the server generates specific improvement measures and advice customized for each department, including measures tailored to each department's specific situation.

[1597] 8. User Emotion Recognition

[1598] The server uses an emotion engine to analyze the user's emotional state, using voice and text data collected from the user. Based on the analysis, the user's stress and fatigue state are identified.

[1599] 9. Emotion-Based Notification Adjustment

[1600] The server adjusts the notification content and remedial measures based on the user's emotional state. For example, for a user in a high stress state, the server may simplify the notification content or reduce the number of suggestions.

[1601] 10. Notification optimization

[1602] The server optimizes the timing and format of notifications based on the analysis results of the emotion engine, sending notifications at the optimal time to suit the user's work schedule.

[1603] 11. User Notice and Confirmation

[1604] The server notifies the user's device of the analysis results, the generated comparison table of old and new data, and improvement measures. The user can check this information on their device and take any necessary measures.

[1605] 12. Progress Management

[1606] Users update the progress status within the system. For items that have been completed, the status is updated within the system to manage the progress of improvements to legal compliance.

[1607] Specific examples

[1608] Example 1: New Personal Information Protection Law and Emotion Recognition

[1609] 1. Collecting information on legal reforms

[1610] The server retrieves information about the new Personal Information Protection Act from the legislation database.

[1611] 2. Analysis of legal reforms

[1612] The server performs preprocessing and uses a generative AI model to identify "personal information encryption obligations."

[1613] 3. Identifying the scope of impact

[1614] The server scans the company's internal document database to identify documents related to the handling of personal information. The old policy states that these documents should be stored in a password-protected file.

[1615] 4. Grouping and categorizing relevant sections

[1616] The server groups and categorizes the scope of impact regarding "storage method."

[1617] 5. Generate a comparison table of old and new versions

[1618] The server compares the new rule, "Personal information is encrypted and stored," with the old rule, "Stored in a password-protected file," and generates a comparison table of the old and new rules.

[1619] 6. Compliance Assessment

[1620] The server evaluates compliance with IT and HR departments. IT department has implemented encryption, but HR department has not.

[1621] 7. Providing customized advice

[1622] The server recommends that the HR department "install encryption software" and the IT department "regularly update encryption protocols."

[1623] 8. User Emotion Recognition

[1624] The server uses an emotion engine to analyze the voice and text data of the HR department staff member and identify their stress level. It turns out that they are experiencing high levels of stress.

[1625] 9. Emotion-Based Notification Adjustment

[1626] Based on the results of the emotion engine, the server provides HR personnel with simplified notifications and a small number of suggestions.

[1627] 10. Notification optimization

[1628] The server sends a notification after the break according to the person's work schedule.

[1629] 11. User Notice and Confirmation

[1630] The server notifies the terminals of the personnel in each department of the results of the analysis and a comparison table of the old and new data. The users can then check these on their terminals and take any necessary action.

[1631] 12. Progress Management

[1632] Users update their progress within the system and manage compliance improvements.

[1633] This system allows companies and organizations to respond to legal changes quickly and efficiently, minimizing the risk of legal violations and omissions. In addition, by utilizing the emotion engine, users can receive notifications in the most optimal state and carry out their work efficiently.

[1634] The processing flow will be explained below.

[1635] Step 1:

[1636] The server accesses external legal databases and related news feeds to collect legal amendment information. It automatically obtains the latest legal amendment information using APIs and crawling tools and stores it in the database.

[1637] Step 2:

[1638] The server performs preprocessing of the legal amendment information it obtains, normalizing the data, filtering out unnecessary information, standardizing the format, and converting it into a format suitable for analysis.

[1639] Step 3:

[1640] The server analyzes the preprocessed legal amendment information using a generative AI model (e.g., BERT or GPT-3), identifying specific amendments and their scope of application, and extracting them as text.

[1641] Step 4:

[1642] The server accesses the company's internal document database and identifies the documents and operational rules that will be affected based on the analysis results. Related text is extracted using full-text search and regular expressions.

[1643] Step 5:

[1644] The server uses a text classification algorithm to group the identified impact areas and categorize them into categories such as "acquisition method," "storage method," and "usage method."

[1645] Step 6:

[1646] The server compares the old and new legal content and automatically generates a comparison table, which clearly shows which parts of the company will be changed and how.

[1647] Step 7:

[1648] The server-generated comparison table is verified to check for omissions and duplications. An algorithm is used to automatically check and make any necessary corrections.

[1649] Step 8:

[1650] The server evaluates the legal compliance status of each company and department, and evaluates the degree to which each department complies with laws and regulations using numerical values ​​and indicators based on the information in the database.

[1651] Step 9:

[1652] Based on the assessment results, the server generates specific improvement measures and advice customized for each department, including measures tailored to the specific circumstances of each department.

[1653] Step 10:

[1654] The server uses an emotion engine to analyze the user's emotional state. It uses voice and text data collected from the user to identify the emotional state. For example, it uses voice analysis and natural language processing to determine the user's stress level and emotional state.

[1655] Step 11:

[1656] The server adjusts the notification content and improvement measures based on the user's emotional state. For users in a high stress state, the server will simplify the notification content and reduce the number of suggestions. In addition, notifications with low urgency will be postponed, allowing the user to respond in a more appropriate state.

[1657] Step 12:

[1658] The server optimizes the timing and format of notifications based on the analysis results of the emotion engine. Notifications are sent at optimal times according to the user's work schedule and emotional state. For example, notifications can be sent outside peak work hours to reduce the burden on the user.

[1659] Step 13:

[1660] The server notifies the user's device of the analysis results, the generated comparison table of old and new data, and improvement measures. Information is provided promptly via email or push notification. After receiving the notification, the user can check the detailed information on their device and take any necessary measures.

[1661] Step 14:

[1662] Users update the progress status within the system. For items that have been completed, the status is updated within the system to manage the progress of improvements to legal compliance.

[1663] Example 2

[1664] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1665] Modern companies and organizations need to respond quickly and accurately to frequent legal changes, but conventional manual processes require a huge amount of time and effort. This often increases the workload of those in charge, leading to stress and reduced efficiency. This raises concerns about the risk of legal violations and inefficiencies. There is a need for a system that can solve this problem and improve operational efficiency while maintaining legal compliance.

[1666] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for automatically collecting legal amendment information, means for analyzing the collected legal amendment information to identify the amendment content, means for identifying internal documents affected by the identified amendment content, means for grouping and categorizing the identified scope of impact, means for automatically generating a comparison table of old and new versions, means for evaluating the legal compliance status of each organization or department, means for providing customized improvement measures and advice based on the evaluation results, means for notifying the user of the provided results to the user's terminal, means for analyzing the user's emotional state and adjusting the notification content and improvement measures, and means for optimizing the timing and format of notifications. This enables companies and organizations to respond quickly and accurately to legal amendments, reduce the user's workload, and enable efficient and effective legal compliance.

[1667] "Means for automatically collecting information on legal amendments" refers to technology that automatically obtains the latest information on legal amendments from external legal databases and news feeds and stores it in a dedicated database.

[1668] "Means of analyzing collected legal amendment information and identifying the amendment content" refers to technology that preprocesses collected legal amendment information and analyzes and identifies specific amendment points using a generative AI model.

[1669] The "means for identifying internal documents affected by the identified revisions" refers to a technology for searching and identifying relevant documents in a company's internal document database based on the identified revisions.

[1670] "Means for grouping and categorizing the identified impact scope" refers to a technique for classifying the affected documents based on specific criteria and grouping them into categories.

[1671] "Means for automatically generating a comparison table between the old and new laws" refers to technology that compares the contents of the old and new laws and regulations and automatically generates a comparison table that clearly shows the differences.

[1672] "Means for assessing compliance with laws and regulations for each organization or department" refers to technology that evaluates compliance with laws and regulations within a company or each department based on numerical values ​​and indicators and displays the results visually.

[1673] "Means for providing customized improvement measures and advice based on the evaluation results" refers to a technology that provides specific...

Claims

1. means of automatically collecting legal change information; A means for analyzing the collected legal amendment information and identifying the amendment content; A means to identify the internal documents that will be affected by the identified amendments; A means of grouping and categorizing the identified impact areas; A means to automatically generate a comparison table between the old and new versions, A means of assessing compliance with laws and regulations by company or department, A means of providing customized remediation and advice based on the assessment results; The system includes a means for notifying the user of the provided results.

2. The system of claim 1 , further comprising means for pre-processing the legal change information into a format suitable for analysis.

3. The system of claim 1 , further comprising means for identifying internal company documents affected by the analyzed revisions using full-text search and regular expressions.

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