System

Abstract

A system is provided.SOLUTION: A system includes generation means, acquisition means, change detection means, summarization means, notification means, evaluation transmission means, aggregation means, and report generation means.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] In recent years, many services have become available online, making it essential to review contracts and terms and conditions before using them. However, many users find the contents of these contracts and terms difficult to understand, making it difficult to grasp the full picture. As a result, users tend to hesitate to use convenient services due to feelings of "I don't understand" or "I'm scared." Furthermore, when service providers make changes to their terms and conditions, it is difficult for users to quickly and accurately grasp the details of those changes. Therefore, in order for users to use services with peace of mind, it is necessary to clearly explain the contents of contracts and terms and conditions, clearly state the risks, and provide feedback to service providers based on user evaluations. [Means for solving the problem]

[0005] The present invention provides a system including a generating means, an acquiring means, a change detecting means, a summarizing means, a notification means, an evaluation sending means, a tabulating means, and a report generating means. The acquiring means acquires terms and conditions from the service provider's website, and the change detecting means compares the previously acquired terms and conditions with the latest terms and conditions to identify changes. The generating means analyzes the identified changes and summarizes the risks. Furthermore, the generating means notifies the user's terminal of the generated summary result and transmits the user's evaluation data to the tabulating means via the evaluation sending means. The tabulating means tabulates the user's evaluations, generates a report, and provides suggestions to companies, thereby creating an environment in which users can use the service with peace of mind.

[0006] A "generation means" is a device or function that analyzes the text of a contract or terms and conditions, summarizes it in a form that is easy for users to understand, and clearly indicates the risks.

[0007] The "acquisition means" refers to a device or function that automatically acquires the terms and conditions from the service provider's website.

[0008] The "change detection means" is a device or function that compares the previously acquired terms and conditions with the latest terms and conditions to identify the changes.

[0009] The "summarization means" is a device or function that analyzes the identified changes and succinctly summarizes important information and risks.

[0010] The "notification means" is a device or function that automatically notifies the user's terminal of the generated summary results and risk information.

[0011] The "evaluation sending means" is a device or function that sends data on the user's evaluation of the contents of the terms and conditions to the server.

[0012] The "collection means" is a device or function that collects evaluation data sent from multiple users and calculates an overall evaluation or score.

[0013] The "report generation means" refers to a device or function that generates a report summarizing improvement proposals and risk avoidance measures for a company based on the collected evaluation data. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] The present invention is implemented as a system including an acquisition means, a change detection means, a generation means, a summarization means, a notification means, an evaluation sending means, a compilation means, and a report generation means. The purpose of this system is to enable users to use services with peace of mind by clearly explaining the contents of the terms and conditions and contracts of services provided on the Web, identifying changes, and clearly indicating risks.

[0036] Obtaining the terms and conditions

[0037] The server periodically accesses the service provider's website to retrieve the latest terms and conditions. This is done by first sending a request and downloading the HTML content, then using an HTML parsing library (e.g. BeautifulSoup) to extract the text portion of the terms and conditions.

[0038] Change Detection

[0039] The server compares the newly acquired terms and conditions with the previously acquired terms and conditions. As a result of this comparison, any changes are identified. A difference detection algorithm is used to extract the specific details of the changes.

[0040] Summary of changes and risk identification

[0041] The server inputs the identified changes into a generation AI, which analyzes them and summarizes them and indicates the risks. For example, by using natural language processing technology such as OpenAI, the changes can be summarized in a form that is easy for users to understand, and risks and points of caution can be highlighted.

[0042] Notification of summary results

[0043] The server then sends the generated summary to the user's device, using web sockets and push notifications to quickly notify the user of changes. Users can receive notifications via a smartphone app or web interface.

[0044] Evaluation of terms and conditions

[0045] The user can use their device to check the notification and evaluate the changes and the entire terms and conditions. This evaluation is done through a dedicated evaluation form, and the evaluation data is sent from the device to the server.

[0046] Aggregation of evaluation data

[0047] The server aggregates the evaluation data sent by users, and calculates the overall evaluation of the terms and conditions and user satisfaction level based on the evaluations collected from multiple users, and displays them as a score.

[0048] Generate reports

[0049] The server generates reports for companies based on the aggregated rating data and user reviews, including suggestions for improvement and risk mitigation, and can be sent to the company via email or made available on a dedicated company dashboard.

[0050] Specific examples

[0051] For example, if the terms and conditions of a particular online shopping site are updated, the system will automatically detect the updates and notify users by summarizing important changes and risks. When users review the summary and provide a rating, the rating is shared with other users, and a report including suggestions for improvement is generated for the company. This allows users to use the shopping site with peace of mind, and companies can receive feedback to improve customer satisfaction.

[0052] The above is an embodiment of the present invention. By using this system, users can easily understand complex terms and conditions and contracts, realizing an environment in which they can use online services with greater peace of mind.

[0053] The processing flow will be explained below.

[0054] Step 1:

[0055] The server accesses the service provider's website to retrieve the latest terms and conditions. This is done by sending an HTTP request and downloading the HTML content returned as a response. The terms and conditions text is extracted from the retrieved HTML content using an HTML parsing library such as BeautifulSoup.

[0056] Step 2:

[0057] The server compares the most recent version of the terms and conditions with the previous version. It uses a difference detection algorithm to identify changes in the text. Specifically, it compares the previous and current terms and conditions line by line and character by character to identify additions and deletions.

[0058] Step 3:

[0059] The server inputs the text of the changes into the generating AI, which then summarizes them and indicates the risks. For example, by using OpenAI's API, the server can summarize the detected changes in an easy-to-understand format, highlighting points that require particular attention and risks.

[0060] Step 4:

[0061] The server notifies the user of the generated summary results via web sockets or push notifications. Users can receive these notifications in real time via a smartphone app or web browser.

[0062] Step 5:

[0063] The user checks the notification displayed on the device and reads the changes to the terms and conditions. The user can check the summary of the changes and the explicit description of the risks, and understand the contents.

[0064] Step 6:

[0065] Users use their devices to evaluate the changes to the terms and conditions. Evaluations are conducted through a dedicated form, and users rate the reliability and comprehension of the changes on a scale of 1 to 5.

[0066] Step 7:

[0067] The terminal transmits the user's evaluation data, including the user's score and additional comments, to the server, where the transmitted evaluation data is stored and aggregated.

[0068] Step 8:

[0069] The server aggregates the evaluation data sent by multiple users and calculates an overall evaluation score. The aggregated results are compiled as data showing the evaluation of the service and the level of understanding of the terms and conditions, and are displayed on a dashboard.

[0070] Step 9:

[0071] The server generates a report for the company based on the aggregated rating data and user reviews. This report includes suggestions for improving the terms and conditions and avoiding risks. The report is either emailed to the company's representative or made available for review on a dedicated company dashboard.

[0072] The above is the specific processing flow in this system.

[0073] Example 1

[0074] 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."

[0075] Currently, terms and conditions and contracts for services provided online are extremely complex, making them difficult for users to understand, and even when changes are made, users often go unnoticed. As a result, users may overlook important risks or changes and fail to take appropriate action. It is also difficult for companies to efficiently collect feedback from users, improve satisfaction, and avoid risks. Therefore, there is a need for a system that allows users to easily understand changes and risks in terms and conditions and provide feedback through evaluations.

[0076] 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.

[0077] In this invention, the server includes an acquisition means, a change detection means, a generation means, a summarization means, a notification means, an evaluation transmission means, a compilation means, and a report generation means. This makes it possible to acquire the latest documents from the information provider's website, analyze their contents, summarize changes and risks, and notify users. Furthermore, users can evaluate the changes, and the evaluation data can be compiled to provide proposals to companies. This system allows users to easily understand important changes and risks, and companies can receive feedback efficiently.

[0078] The "acquisition means" is a function for acquiring the latest documents from the information provider's website.

[0079] The "change detection means" is a function for comparing a newly acquired document with a previously acquired document to identify changes.

[0080] The "generation means" is a function for analyzing the identified changes and generating summaries and risk assessments.

[0081] The "summarization means" is a function for creating a summary in a form that is easy for the user to understand, based on the analysis results of the changes.

[0082] The "notification means" is a function for promptly notifying the user of the generated summary result.

[0083] The "evaluation sending means" is a function that allows a user to input an evaluation and send the evaluation data to the server.

[0084] The "aggregation means" is a function for aggregating evaluation data sent from multiple users and calculating an overall evaluation and user satisfaction level.

[0085] The "report generation means" is a function for generating reports for companies from the collected evaluation data and user reviews.

[0086] The present invention is implemented as a system including an acquisition means, a change detection means, a generation means, a summarization means, a notification means, an evaluation transmission means, a compilation means, and a report generation means. The main purpose of this system is to provide the contents of documents (e.g., terms and conditions and contracts) for services provided over the Internet in a format that is easy for users to understand, and to clearly indicate the risks of changes, so that users can use the services with peace of mind.

[0087] System Configuration

[0088] The system includes the following main means:

[0089] Acquisition means

[0090] The server periodically accesses the information provider's website to retrieve the latest documents, sending an HTTP request to download the HTML content and extracting the text portion of the document using an HTML parsing library such as BeautifulSoup.

[0091] Change detection method

[0092] The server compares the text of the newly retrieved document with that of the previously retrieved document, using a difference detection algorithm (e.g., difflib) to identify the specific changes.

[0093] Generators and Summarizers

[0094] The server inputs the identified changes into the generative AI model using prompt sentences, and generates a summary in a form that is easy for the user to understand based on the summary and risk assessment output by the generative AI model.

[0095] Example prompt: "Please summarize the updates in the latest policy and clarify the risks."

[0096] Example prompt: "Identify and summarize the changes in your service provider's terms and conditions."

[0097] Notification means

[0098] The server notifies the user of the generated summary results using a web socket or push notification service (e.g., Firebase Cloud Messaging) to deliver the information quickly.

[0099] Evaluation sending method

[0100] The user checks the notification on the device and evaluates the changes. Evaluations are made using a dedicated evaluation form, and the evaluation data is sent from the device to the server.

[0101] Aggregation method

[0102] The server aggregates the evaluation data sent by multiple users, and calculates the overall evaluation of the document and the user satisfaction level, which are then displayed as a score.

[0103] Report Generation Method

[0104] The server generates reports for companies based on the aggregated rating data and user reviews, including recommendations for improvement and risk prevention measures, which companies can receive via email or view through a dedicated dashboard.

[0105] Specific examples

[0106] For example, if the terms and conditions of a particular online shopping site are updated, the server automatically detects the updates and notifies users by summarizing important changes and risks. When users review the summary and provide a rating, the rating is shared with other users, and a report including suggestions for improvement is generated for the company. This allows users to use the shopping site with peace of mind, and companies can quickly obtain feedback to improve customer satisfaction.

[0107] As described above, by using the system of the present invention, users can easily understand complex documents, and companies can be provided with an environment in which they can efficiently collect user feedback.

[0108] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0109] Step 1:

[0110] The server periodically accesses the information provider's website to retrieve the latest documents. First, the server sends an HTTP request to a specified URL using a Cron job. It saves the received HTML content and parses the HTML using BeautifulSoup to extract the text portion of the document from specific tags and classes.

[0111] Input: URL of the informant's website, text of the previously obtained document

[0112] Output: Text of the most recent document

[0113] Step 2:

[0114] The server compares the newly retrieved document with the previously retrieved document. First, it reads both texts and uses a difference detection algorithm (e.g., difflib) to identify the changes. It then lists the changes detected as differences and prepares for the next step.

[0115] Input: Text of the latest document, text of the previously retrieved document

[0116] Output: List of changes

[0117] Step 3:

[0118] The server then formats the identified changes into prompts to be input to the generative AI model, including instructions for inserting the text of the changes and generating a summary and risk statement.

[0119] Input: List of changes

[0120] Output: prompt statement

[0121] Step 4:

[0122] The server sends the prompt to the generative AI model and obtains a summary and risk assessment. Specifically, it calls the generative AI model's API, inputs the prompt, and receives the analysis results, which include a summary of the changes and a risk assessment.

[0123] Input: prompt statement

[0124] Output: Summary results and risk assessment

[0125] Step 5:

[0126] The server notifies the user of the generated summary results. Specifically, it uses a web socket or push notification service (e.g., Firebase Cloud Messaging) to send the summary results to each user's device.

[0127] Input: Summary results and risk assessment

[0128] Output: Notification to the user's device

[0129] Step 6:

[0130] Users receive a notification on their device to confirm the changes, and can view the notification content through an application or web interface and fill out an evaluation form.

[0131] Input: Notification received on the user's device

[0132] Output: Data entered into the evaluation form

[0133] Step 7:

[0134] The user inputs evaluation data and transmits it to the server using the evaluation transmission means, and the transmitted evaluation data is stored in the server's database.

[0135] Input: Data entered into the evaluation form

[0136] Output: Rating data sent to the server

[0137] Step 8:

[0138] The server aggregates the evaluation data sent by multiple users and calculates the overall evaluation and user satisfaction. The aggregation script queries the evaluation data and generates statistical information.

[0139] Input: Multiple evaluation data

[0140] Output: Overall rating score and user satisfaction

[0141] Step 9:

[0142] The server generates reports for the company based on the aggregated data. The report generation script uses the statistical information to create reports in PDF and HTML format, including recommendations and risk management measures for the company.

[0143] Input: Overall rating score and user satisfaction

[0144] Output: Corporate reports

[0145] Step 10:

[0146] The server will send the generated report to the designated corporate contact or make it available for viewing in the corporate dashboard. It will use the email sending library to send the report or update the dashboard link.

[0147] Input: Corporate Report

[0148] Output: Send report to company or update dashboard

[0149] (Application example 1)

[0150] 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."

[0151] Documents used in factories, such as safety manuals and work procedures, are frequently updated. This makes it difficult to identify changes and identify risks, requiring workers to always understand the latest information. However, doing this manually is labor-intensive and can lead to misunderstandings and missed communications. To solve this problem, a system is needed that automatically detects changes in documents, summarizes them, identifies risks, and promptly notifies users.

[0152] 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.

[0153] In this invention, the server includes a means for periodically acquiring documents, a means for analyzing and summarizing changes in documents based on a generative AI model, and a means for notifying users in the factory of the summary results. This makes it possible to automatically identify changes in documents used in the factory, summarize risks, and promptly notify them.

[0154] The "acquisition means" is a means for periodically acquiring the latest documents from the document management system within the factory.

[0155] The "change detection means" is a means for comparing the previously acquired document with the latest document and identifying the changes.

[0156] The "generative means" is a means for analyzing the identified changes based on a generative AI model and summarizing the risks.

[0157] The "summarization means" is a means for summarizing the content of the changes in a form that is easy for the user to understand.

[0158] The "notification means" is a means for quickly notifying users in the factory of the generated summary results.

[0159] The "evaluation sending means" is a means for sending the data evaluated by the user to the server.

[0160] The "counting means" is a means for counting evaluation data sent from users.

[0161] The "report generation means" is a means for generating a report for a company based on the collected evaluation data.

[0162] This invention provides a system that manages documents such as safety manuals and work procedures used in factories, identifies changes, summarizes them, and indicates risks. This system periodically retrieves documents from the document management system, analyzes and summarizes the changes based on a generative AI model, and promptly notifies the user.

[0163] Hardware and software used

[0164] Hardware:

[0165] Server: For example, a cloud server such as Amazon EC2

[0166] User device: smartphone, tablet, or factory workstation

[0167] Factory robots: Robots that physically retrieve documents (if necessary)

[0168] software:

[0169] requests: A Python library for HTTP requests

[0170] ftplib: A Python library for FTP access

[0171] difflib: A Python library for diff detection algorithms

[0172] OpenAI GPT-3: Natural Language Processing with Generative AI Models

[0173] Firebase Cloud Messaging: a service for push notifications

[0174] React: A front-end framework for rating forms

[0175] SendGrid: a service for sending emails

[0176] The working mechanism of the invention

[0177] The server periodically accesses the document management system to retrieve the latest documents, using HTTP requests or FTP. For example, it uses the requests library to retrieve data from the API, or the ftplib library to download files from the FTP server.

[0178] The retrieved document is compared with the previous document to identify the changes. The difference detection is done using the Python difflib library, which effectively detects the differences between two pieces of text.

[0179] The identified changes are fed into a generative AI model (e.g., OpenAI's GPT-3) to summarize and identify risks. The generative AI model analyzes the input text based on a prompt and summarizes it in an understandable format. Here is an example prompt:

[0180] "Please summarize the changes in the following documents and highlight any new risk points:

[0181] "Old version: The operation buttons are green. New version: The operation buttons are red. This change is to prevent operation errors."

[0182] The generated summary results are pushed to the user's device using Firebase Cloud Messaging, and the user receives the notification on their smartphone or work device and can check the contents.

[0183] After checking the notification content, the user rates the app. The rating data is sent to the server via a rating form using React. The rating sending means passes this rating data to the aggregation means.

[0184] The server aggregates the submitted evaluation data and generates a report for the company based on the evaluation results. This report includes an overall evaluation and recommendations for the document. The report can be sent to the company via email using SendGrid or made available for viewing on the company's dashboard.

[0185] For example, when a factory's safety manual is updated, new procedures and risk points may be added. The system automatically detects these changes, summarizes them, and clearly indicates the risks. For example, a notification may be issued saying, "Due to the new procedures, the color of the operation buttons has changed, and workers need to be careful to prevent incorrect operation." Users can quickly understand this information and provide evaluation feedback to the server. This improves safety and work efficiency throughout the factory.

[0186] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0187] Step 1:

[0188] The server periodically accesses the document management system to obtain the latest documents. Specifically, it downloads the documents using HTTP requests or FTP. The input is data from the document management system, and the output is the latest documents stored in the server.

[0189] Step 2:

[0190] The server compares the latest retrieved document with the previous one, using Python's difflib library to find the differences between the text. The input is both the previous and latest document, and the output is a list of changes.

[0191] Step 3:

[0192] The server analyzes and summarizes the identified changes based on a generative AI model (e.g., OpenAI GPT-3). It uses prompts to instruct the AI ​​to specify the changes and risks. The input is the text of the changes, and the output is a text containing a summary and a description of the risks. Example prompts:

[0193] "Please summarize the changes in the following documents and highlight any new risk points:

[0194] "Old version: The operation buttons are green. New version: The operation buttons are red. This change is to prevent operation errors."

[0195] Step 4:

[0196] The server sends the generated summary results to the user's device using Firebase Cloud Messaging. The input is the text of the summary results, and the output is a push notification to the user's device. The user receives the notification content on their smartphone or work device.

[0197] Step 5:

[0198] The user checks the notification content and rates it using their workstation. The rating data is sent to the server through a dedicated rating form (e.g., a React-based web form). The input is the user's rating data, and the output is the rating data sent to the server.

[0199] Step 6:

[0200] The server aggregates the submitted rating data. It collects ratings from multiple users and calculates a rating score. The input is the user rating data, and the output is the aggregated rating score.

[0201] Step 7:

[0202] The server generates reports for companies based on the aggregated rating scores. The reports are sent to companies via email using SendGrid or displayed on a company dashboard. The input is the aggregated rating scores and user reviews, and the output is a report for the company.

[0203] Through the above processing steps, the present invention realizes a system that automates document management and risk assessment within a factory, notifies users quickly, and provides appropriate feedback.

[0204] 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.

[0205] The present invention is embodied as a system including an acquisition means, a change detection means, a generation means, a summarization means, a notification means, a rating transmission means, a compilation means, a report generation means, and an emotion engine. The system aims to improve the user experience by enabling users to easily understand terms and conditions and contracts of online services and by recognizing the user's emotions and providing feedback.

[0206] Obtaining the terms and conditions

[0207] The server periodically accesses the service provider's website to retrieve the latest terms and conditions. This is done by sending an HTTP request and parsing the HTML content returned as a response. An HTML parsing library is used to extract the terms and conditions text.

[0208] Change Detection

[0209] The server compares the newly retrieved clause with the previously retrieved clause and uses a difference detection algorithm to identify any changes in the text.

[0210] Summary of changes and risk identification

[0211] The server inputs the text of the changes into the generation AI, which then summarizes them and identifies the risks. Specifically, natural language processing technology is used to summarize the changes in a way that is easy for users to understand, highlighting points that require particular attention and risks.

[0212] Notification of summary results

[0213] The server notifies the user of the generated summary results via web sockets or push notifications. Users can receive these notifications in real time via a smartphone app or web browser.

[0214] Evaluation of terms and conditions

[0215] The user checks the notification displayed on the device and reads the changes to the terms and conditions. The user can check the summary of the changes and the explicit description of the risks, and understand the contents.

[0216] User Emotion Recognition

[0217] When a user uses a device to evaluate the policy changes, the emotion engine recognizes emotions from the user's facial expressions, voice, and text input. This emotion data is recorded along with the user's evaluation.

[0218] Sending emotional data

[0219] The terminal transmits the user's evaluation data and emotion data to the server, which aggregates the emotion data along with the evaluation data.

[0220] Aggregation of rating and sentiment data

[0221] The server aggregates the evaluation data and emotion data sent by multiple users, thereby generating data that indicates the overall evaluation of the terms and conditions and the emotional state of the users.

[0222] Generate reports

[0223] The server generates a report for the company based on the aggregated evaluation and sentiment data, including suggestions for policy improvements and risk avoidance. The report is then emailed to the company's contact person or made available for review on a dedicated company dashboard.

[0224] Specific examples

[0225] For example, if the terms and conditions of a particular online education service are updated, the system automatically detects the changes and notifies the user by summarizing important changes and risks. When the user reviews and evaluates the changes, the emotion engine identifies the user's emotions and sends this data to the server. A report is then generated for the company based on the aggregated results, and the company is provided with improvement suggestions that include the user's emotional state. This allows users to use the education service with peace of mind and allows companies to receive more specific feedback.

[0226] The above is an embodiment of the present invention. By combining an emotion engine, it is possible to recognize the user's emotions and provide more personalized feedback, further improving the user experience.

[0227] The processing flow will be explained below.

[0228] Step 1:

[0229] The server accesses the service provider's website to retrieve the latest terms and conditions. This is done by sending an HTTP request and downloading the HTML content returned as a response. An HTML parsing library (e.g., BeautifulSoup) is used to extract the terms and conditions text from the retrieved HTML content.

[0230] Step 2:

[0231] The server compares the newly retrieved terms and conditions with the previously retrieved terms and conditions. It uses a difference detection algorithm to identify changes in words and sentences. Specifically, it compares the previous and current terms and conditions line by line or character by character to identify additions and deletions.

[0232] Step 3:

[0233] The server inputs the changes into the generation AI, which then summarizes the changes and identifies the risks. For example, it can use OpenAI's API to analyze the detected changes and summarize them in a way that is easy for users to understand, highlighting points to be aware of and risks.

[0234] Step 4:

[0235] The server notifies the generated summary results to the user's device using web socket and push notification technologies, and the user can receive the notification in real time via a smartphone app or web browser.

[0236] Step 5:

[0237] The user checks the notification displayed on their device and reads the changes to the terms and conditions. They then view the generated summary and risk information to understand the content. During this process, the emotion engine monitors the user's facial expressions and voice to recognize their emotions.

[0238] Step 6:

[0239] Users use their devices to evaluate the changes to the terms and conditions. Evaluations are made through an evaluation form, where users enter a score from 1 to 5 and any additional comments. At the same time, an emotion engine analyzes the user's emotions in real time and generates emotion data.

[0240] Step 7:

[0241] The device transmits user evaluation data and emotion data to the server. The transmitted data includes evaluation scores, comments, and emotion recognition data. The server then aggregates the data.

[0242] Step 8:

[0243] The server aggregates the evaluation data and emotional data sent by multiple users. This generates data showing the overall evaluation of the terms and conditions and the emotional state of the users. The aggregated results are displayed on a dashboard.

[0244] Step 9:

[0245] The server generates a report for the company based on the aggregated evaluation and sentiment data, including suggestions for improving the terms and conditions and avoiding risks. The report is then emailed to the company's representative or made available for review on a dedicated company dashboard.

[0246] Example 2

[0247] 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."

[0248] In recent years, with the spread of online services, users are increasingly required to understand complex and lengthy terms and conditions and contracts. However, these documents contain many technical and legal terms, making them difficult for average users to understand. Furthermore, frequently changing terms and conditions place a significant burden on users, who must constantly review them and recognize risks. Furthermore, it is not easy for companies to properly understand user sentiment and feedback and use it to improve terms and conditions. Therefore, there is a need for a system that can automatically detect and summarize changes in terms and conditions, indicate risks, and recognize user sentiment to improve the user experience.

[0249] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0250] In this invention, the server includes an acquisition means, a change detection means, a generation means, a summarization means, a notification means, an evaluation transmission means, a compilation means, a report generation means, and an emotion recognition means, which enables the following:

[0251] 1. The server periodically retrieves the latest terms and conditions from the service provider's website and compares them with the previous terms and conditions to identify any changes.

[0252] 2. Use a generative AI model to analyze the identified changes and summarize the risks, providing information in a way that is easy for users to understand.

[0253] 3. Notify the user of the summary results in real time and collect user evaluation data and sentiment data.

[0254] 4. Generate reports for companies based on the collected evaluation and sentiment data and provide specific improvement suggestions.

[0255] This allows users to quickly respond to complex changes in terms and conditions and understand the risks, while also allowing companies to obtain useful feedback to improve their terms and conditions based on user sentiment and evaluations, thereby improving the user experience.

[0256] "Acquisition means" refers to a means that has the function of periodically acquiring the latest terms and conditions from the service provider's website.

[0257] The "change detection means" is a means having a function of comparing the previously acquired terms and conditions with the latest terms and conditions and identifying the changed parts.

[0258] "Generative means" means a means having the function of analyzing the changes identified using a generative AI model and summarizing the risks.

[0259] The "summarization means" is a means having a function of recording and storing the generated summary results and risk information.

[0260] The "notification means" is a means having a function of notifying the generated summary result to the user's terminal in real time.

[0261] The "evaluation sending means" is a means having a function of sending data evaluated by the user to the server.

[0262] The "aggregation means" is a means having a function of aggregating evaluation data and emotion data sent from a plurality of users and generating statistical information.

[0263] The "report generation means" is a means having a function of creating a report for a company based on the collected evaluation data and emotion data.

[0264] The "emotion recognition means" is a means having a function of recognizing emotions from the user's facial expressions, voice, and text input, and recording the emotion data.

[0265] A "generative AI model" is a model that uses natural language processing technology to summarize and identify risks based on input text.

[0266] A "prompt" is text used as input to a generative AI model, and is an instruction used to summarize and clarify risks.

[0267] The present invention is embodied as a system including an acquisition means, a change detection means, a generation means, a summarization means, a notification means, a rating transmission means, a compilation means, a report generation means, and an emotion recognition means, which not only makes it easier for users to understand terms and conditions and contracts of online services, but also recognizes the user's emotions and provides appropriate feedback to improve the user experience.

[0268] Obtaining the terms and conditions

[0269] The server periodically accesses the service provider's website to retrieve the latest terms and conditions. This process is performed by sending an HTTP request and analyzing the HTML content received as a response. Specifically, the "requests" library is used to send the HTTP request, and the "BeautifulSoup" library is used to parse the HTML content and retrieve the text of the terms and conditions.

[0270] Change Detection

[0271] The server compares the newly acquired terms and conditions with the previously acquired terms and conditions. The comparison is performed using a difference detection algorithm such as DiffMatchPatch to identify changes. This makes it possible to clearly identify important changes for the user.

[0272] Summary of changes and risk identification

[0273] The server inputs the text of the changes as a prompt to the generative AI model (e.g., OpenAI's GPT-3), summarizing the changes and highlighting the risks. Specific examples of prompts include:

[0274] To help users understand the latest changes, please summarize the following text and highlight any important caveats or risks:

[0275] [Insert text of change to terms here]

[0276] The generative AI model provides a concise summary of the revised terms and conditions, highlighting any risks that require particular attention.

[0277] Notification of summary results

[0278] The server notifies the user of the generated summary results via web sockets or push notifications, allowing the user to receive important change information in real time via a smartphone app or web browser.

[0279] Evaluation of terms and conditions

[0280] The user checks the notification on their device and views the changes to the terms and conditions. They then review a summary of the changes and a statement of the risks, and understand the details. This allows the user to quickly grasp important changes.

[0281] User Emotion Recognition

[0282] When a user uses the device to evaluate the policy changes, the device uses emotion recognition means to capture the user's facial expressions and voice to recognize their emotions, for example, by using the OpenCV library or Microsoft Azure Cognitive Services to recognize and record the user's emotions in real time.

[0283] Sending emotional data

[0284] The device sends the user's rating and emotion data to the server by sending an HTTP POST request using the "requests" library.

[0285] Aggregation of evaluation data and emotion data

[0286] The server collects and aggregates the evaluation and emotion data sent by multiple users into a database. Specifically, it uses the Pandas library to analyze the evaluation and emotion data and generate statistical information.

[0287] Generate reports

[0288] The server generates a report for the company based on the aggregated evaluation data and sentiment data. This report includes suggestions for improving terms and conditions and avoiding risks. The generated report is either sent to the company's personnel via email or provided for viewing on a dedicated corporate dashboard. Specifically, the report is created in HTML format using the "Jinja2" template engine, and emailed using the "smtplib" library. The corporate dashboard is also built using "Flask" as a web framework.

[0289] Specific examples

[0290] For example, if the terms and conditions of a particular online education service are updated, the system automatically detects the changes and notifies the user by summarizing important changes and risks. When the user reviews and evaluates the changes, the emotion engine identifies the user's emotions and sends this data to the server. The server generates a report for the company based on the collected data, and provides the company with improvement suggestions that include the user's emotional state. This allows users to use the education service with peace of mind and allows companies to receive specific feedback.

[0291] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0292] Step 1:

[0293] The server accesses the service provider's website at 2:00 AM every day to retrieve the latest terms and conditions. The input for this process is the website URL, and an HTTP request is sent to retrieve the HTML content. The HTTP request is sent using the "requests" library, and the HTML content received as a response is analyzed using the "BeautifulSoup" library to extract the terms and conditions text. As an output, the extracted terms and conditions text is saved in a database.

[0294] Step 2:

[0295] The server retrieves the newly acquired clause text and the previously acquired clause text from the database. The input to this process is the two clause texts retrieved from the database. Next, a difference detection algorithm such as DiffMatchPatch is used to identify the changes in the text. As an output, the server generates the text of the identified changes and stores it in the database.

[0296] Step 3:

[0297] The server inputs the text of the change to the generative AI model as a prompt. The input for this process is the text of the change. Here is an example of a prompt sentence to input to the generative AI model:

[0298] To help users understand the latest changes, please summarize the following text and highlight any important caveats or risks:

[0299] [Insert text of change to terms here]

[0300] The generative AI model analyzes this input, produces a summary and identifies risks, and stores the resulting summary and risk information in a database as output.

[0301] Step 4:

[0302] The server notifies the user's device of the generated summary results. The input for this process is the summary results and risk information. Web sockets and push notifications are used as notification methods. The server uses the "WebSocket" library and "Firebase Cloud Messaging" to send notifications to the user's device in real time. As an output, notifications are displayed on the user's device.

[0303] Step 5:

[0304] The user checks the notification displayed on their device and views the changes to the terms and conditions. The input to this process is a summary of the terms and conditions received as a notification. The user then views this information using a smartphone app or web browser to understand the content. The output is a deeper understanding of the user.

[0305] Step 6:

[0306] When a user uses a device to evaluate changes to the terms and conditions, the device captures the user's facial expressions and voice using a camera and microphone, and analyzes them using emotion recognition. The input for this process is the text to be evaluated and the captured user's facial expressions and voice data. The device recognizes emotions and records the emotion data using OpenCV and Microsoft Azure Cognitive Services. As an output, emotion data is generated and recorded.

[0307] Step 7:

[0308] The device sends the user's rating data and emotion data to the server. The input of this process is the rating data and emotion data. The device uses the "requests" library to send an HTTP POST request to the server's API endpoint, and the data is sent to the server as output.

[0309] Step 8:

[0310] The server imports rating data and emotion data submitted by multiple users into a database and aggregates them. The input for this process is the rating data and emotion data stored in the database. The "Pandas" library is used to analyze the data and generate statistical information. The aggregated results are generated as the output.

[0311] Step 9:

[0312] The server generates a report for the company based on the aggregated evaluation data and sentiment data. The input for this process is the aggregated data. The report is created in HTML format using the "Jinja2" template engine and sent by email using the "smtplib" library. In addition, a dashboard for the company is provided using "Flask" as a web framework. The generated report is provided to the company's personnel as an output.

[0313] (Application example 2)

[0314] 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."

[0315] Changes to terms of service are frequent in modern online services, making it difficult for users to keep up with them in a timely and accurate manner. Understanding user sentiment regarding changes to terms of service provides important feedback for service providers, but a system for efficiently collecting and analyzing this information has yet to be established. Furthermore, there is a lack of a system for clearly communicating the risks and importance of changes to users, which often results in a poor user experience.

[0316] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a generating means, an acquiring means, a change detecting means, a summarizing means, a notifying means, an emotion recognizing means, an evaluation sending means, a tabulating means, and a report generating means. This makes it possible to quickly detect changes in the terms of use, summarize the risks, and notify the user, and further collect user emotion data, aggregate this data, and provide feedback to the company.

[0317] The "generation means" is a means for analyzing the identified changes and summarizing them in a form that is easy for the user to understand.

[0318] "Means for Acquisition" means a means for periodically obtaining the latest terms and conditions from the website of the online service provider.

[0319] The "change detection means" is a means for comparing the previously acquired terms and conditions with the latest terms and conditions to identify any changes.

[0320] The "summarization means" is a means for summarizing important points and risks of the changes identified by the change detection means.

[0321] The "notification means" is a means for notifying the user of the generated summary result.

[0322] An "emotion recognition means" is a means for recognizing emotions from a user's facial expressions, voice, text input, etc., and acquiring that data.

[0323] The "evaluation sending means" is a means for sending the data rated by the user and the emotion data acquired by the emotion recognition means to the server.

[0324] The "aggregation means" is a means for aggregating the evaluation data and emotion data sent from a plurality of users.

[0325] The "report generation means" is a means for generating a report for a company based on the collected evaluation data and emotion data.

[0326] A system for carrying out the present invention includes the following means:

[0327] 1. Acquisition method:

[0328] The server periodically retrieves the latest terms and conditions from the online service provider's website by sending an HTTP request and parsing the HTML content returned as a response. Specifically, it uses an HTML parsing library such as BeautifulSoup to extract the terms and conditions text.

[0329] 2. Change detection methods:

[0330] The server compares the previously retrieved contract with the newly retrieved contract, using a difference detection algorithm such as difflib to detect text differences and identify changes.

[0331] 3. Summary tools:

[0332] The server uses a generative AI model to summarize the risks of the changes. It generates prompt sentences, inputs them into the generative AI model, and summarizes the changes using natural language processing technology.

[0333] 4. Means of notification:

[0334] The generated summary results are sent to the user's device (smartphone app or web browser) via web socket or push notification, allowing the user to receive real-time updates.

[0335] 5. Emotion recognition means:

[0336] As users evaluate the policy changes, the device recognizes their emotions from their facial expressions, voice, and text input. This emotional data is obtained using an emotion recognition engine such as EmotionEngine.

[0337] 6. Evaluation submission method:

[0338] The device transmits user rating and emotion data to the server, which is then aggregated.

[0339] 7. Aggregation method:

[0340] The server aggregates the evaluation data and emotional data sent by multiple users, thereby generating data that indicates the overall evaluation of the terms and conditions and the emotional state of the users.

[0341] 8. Report Generation Methods:

[0342] The server generates reports for companies based on the aggregated evaluation and sentiment data, including recommendations for policy improvements and risk avoidance, which are emailed to company representatives or made available for review on a dedicated company dashboard.

[0343] Specific examples

[0344] For example, if the terms and conditions of a particular online education service are updated, the system automatically detects the changes and notifies the user by summarizing important changes and risks. When the user reviews and evaluates the changes, the emotion engine identifies the user's emotions and sends this data to the server. A report is then generated for the company based on the aggregated results, and the company is provided with improvement suggestions that take into account the user's emotional state. This allows users to use the service with peace of mind and allows companies to receive more specific feedback.

[0345] Prompt Sentence Examples

[0346] Changes:

[0347] The usage fee will increase from 3% to 5%.

[0348] Please summarize."

[0349] Notification summary and risk statement:

[0350] "

[0351] notification:

[0352] Latest Terms and Conditions Changes:

[0353] The service fee has increased from 3% to 5%, a change that may directly affect the payment costs of users.

[0354] "

[0355] In this way, by using the system of the present invention, it is possible to quickly grasp changes to the terms of use and the risks involved, and also to provide feedback that reflects the user's feelings.

[0356] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0357] Step 1:

[0358] The server retrieves the latest terms and conditions from the online service provider's website by sending an HTTP request and parsing the HTML content returned as a response. A library such as BeautifulSoup is used for HTML parsing, and the latest terms and conditions text is extracted.

[0359] Input: Service provider website URL

[0360] Output: Latest extracted clause text

[0361] Step 2:

[0362] The server compares the previously retrieved clause with the latest clause to identify any changes. This comparison uses a difference detection algorithm such as difflib, and a change detection means detects the differences in the text.

[0363] Input: Latest clause text, previously retrieved clause text

[0364] Output: List of changes

[0365] Step 3:

[0366] The server uses a generative AI model to summarize the risks of the changes. A prompt is input into the generative AI model, which then uses natural language processing technology to summarize the changes. Based on the input prompt, the generative AI outputs text summarizing the important points and risks of the changes.

[0367] Input: list of changes, prompt

[0368] Output: Summarized risk text

[0369] Step 4:

[0370] The server then notifies the user of the generated summary results via web sockets or push notifications, which are sent in real time to the user's smartphone app or web browser.

[0371] Input: Abstracted risk text

[0372] Output: Notification to user's device

[0373] Step 5:

[0374] The user reviews and evaluates the changes to the terms and conditions. In addition, the user's device recognizes emotions from the user's facial expressions, voice, and text input. This emotion data is obtained using an emotion recognition engine such as EmotionEngine.

[0375] Input: User facial expressions, voice, and text input

[0376] Output: Emotion data, rating data

[0377] Step 6:

[0378] The terminal transmits the user's evaluation data and emotion data to the server via the evaluation transmitting means.

[0379] Input: Emotion data, rating data

[0380] Output: Send data to the server

[0381] Step 7:

[0382] The server aggregates the evaluation data and emotional data sent by multiple users, thereby generating data that indicates the overall evaluation of the terms and conditions and the emotional state of the users.

[0383] Input: Rating data and sentiment data from multiple users

[0384] Output: Aggregated rating data, sentiment data

[0385] Step 8:

[0386] The server generates reports for companies based on the aggregated evaluation and sentiment data, including recommendations for policy improvements and risk avoidance, which are emailed to company representatives or made available for review on a dedicated company dashboard.

[0387] Input: Aggregated rating data, sentiment data

[0388] Output: Corporate reports

[0389] This allows users to use the service with peace of mind and allows companies to receive more specific feedback.

[0390] 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.

[0391] 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.

[0392] 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.

[0393] [Second embodiment]

[0394] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0395] 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.

[0396] 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).

[0397] 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.

[0398] 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.

[0399] 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).

[0400] 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.

[0401] 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.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] 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."

[0406] The present invention is implemented as a system including an acquisition means, a change detection means, a generation means, a summarization means, a notification means, an evaluation sending means, a compilation means, and a report generation means. The purpose of this system is to enable users to use services with peace of mind by clearly explaining the contents of the terms and conditions and contracts of services provided on the Web, identifying changes, and clearly indicating risks.

[0407] Obtaining the terms and conditions

[0408] The server periodically accesses the service provider's website to retrieve the latest terms and conditions. This is done by first sending a request and downloading the HTML content, then using an HTML parsing library (e.g. BeautifulSoup) to extract the text portion of the terms and conditions.

[0409] Change Detection

[0410] The server compares the newly acquired terms and conditions with the previously acquired terms and conditions. As a result of this comparison, any changes are identified. A difference detection algorithm is used to extract the specific details of the changes.

[0411] Summary of changes and risk identification

[0412] The server inputs the identified changes into a generation AI, which analyzes them and summarizes them and indicates the risks. For example, by using natural language processing technology such as OpenAI, the changes can be summarized in a form that is easy for users to understand, and risks and points of caution can be highlighted.

[0413] Notification of summary results

[0414] The server then sends the generated summary to the user's device, using web sockets and push notifications to quickly notify the user of changes. Users can receive notifications via a smartphone app or web interface.

[0415] Evaluation of terms and conditions

[0416] The user can use their device to check the notification and evaluate the changes and the entire terms and conditions. This evaluation is done through a dedicated evaluation form, and the evaluation data is sent from the device to the server.

[0417] Aggregation of evaluation data

[0418] The server aggregates the evaluation data sent by users, and calculates the overall evaluation of the terms and conditions and user satisfaction level based on the evaluations collected from multiple users, and displays them as a score.

[0419] Generate reports

[0420] The server generates reports for companies based on the aggregated rating data and user reviews, including suggestions for improvement and risk mitigation, and can be sent to the company via email or made available on a dedicated company dashboard.

[0421] Specific examples

[0422] For example, if the terms and conditions of a particular online shopping site are updated, the system will automatically detect the updates and notify users by summarizing important changes and risks. When users review the summary and provide a rating, the rating is shared with other users, and a report including suggestions for improvement is generated for the company. This allows users to use the shopping site with peace of mind, and companies can receive feedback to improve customer satisfaction.

[0423] The above is an embodiment of the present invention. By using this system, users can easily understand complex terms and conditions and contracts, realizing an environment in which they can use online services with greater peace of mind.

[0424] The processing flow will be explained below.

[0425] Step 1:

[0426] The server accesses the service provider's website to retrieve the latest terms and conditions. This is done by sending an HTTP request and downloading the HTML content returned as a response. The terms and conditions text is extracted from the retrieved HTML content using an HTML parsing library such as BeautifulSoup.

[0427] Step 2:

[0428] The server compares the most recent version of the terms and conditions with the previous version. It uses a difference detection algorithm to identify changes in the text. Specifically, it compares the previous and current terms and conditions line by line and character by character to identify additions and deletions.

[0429] Step 3:

[0430] The server inputs the text of the changes into the generating AI, which then summarizes them and indicates the risks. For example, by using OpenAI's API, the server can summarize the detected changes in an easy-to-understand format, highlighting points that require particular attention and risks.

[0431] Step 4:

[0432] The server notifies the user of the generated summary results via web sockets or push notifications. Users can receive these notifications in real time via a smartphone app or web browser.

[0433] Step 5:

[0434] The user checks the notification displayed on the device and reads the changes to the terms and conditions. The user can check the summary of the changes and the explicit description of the risks, and understand the contents.

[0435] Step 6:

[0436] Users use their devices to evaluate the changes to the terms and conditions. Evaluations are conducted through a dedicated form, and users rate the reliability and comprehension of the changes on a scale of 1 to 5.

[0437] Step 7:

[0438] The terminal transmits the user's evaluation data, including the user's score and additional comments, to the server, where the transmitted evaluation data is stored and aggregated.

[0439] Step 8:

[0440] The server aggregates the evaluation data sent by multiple users and calculates an overall evaluation score. The aggregated results are compiled as data showing the evaluation of the service and the level of understanding of the terms and conditions, and are displayed on a dashboard.

[0441] Step 9:

[0442] The server generates a report for the company based on the aggregated rating data and user reviews. This report includes suggestions for improving the terms and conditions and avoiding risks. The report is either emailed to the company's representative or made available for review on a dedicated company dashboard.

[0443] The above is the specific processing flow in this system.

[0444] Example 1

[0445] 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."

[0446] Currently, terms and conditions and contracts for services provided online are extremely complex, making them difficult for users to understand, and even when changes are made, users often go unnoticed. As a result, users may overlook important risks or changes and fail to take appropriate action. It is also difficult for companies to efficiently collect feedback from users, improve satisfaction, and avoid risks. Therefore, there is a need for a system that allows users to easily understand changes and risks in terms and conditions and provide feedback through evaluations.

[0447] 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.

[0448] In this invention, the server includes an acquisition means, a change detection means, a generation means, a summarization means, a notification means, an evaluation transmission means, a compilation means, and a report generation means. This makes it possible to acquire the latest documents from the information provider's website, analyze their contents, summarize changes and risks, and notify users. Furthermore, users can evaluate the changes, and the evaluation data can be compiled to provide proposals to companies. This system allows users to easily understand important changes and risks, and companies can receive feedback efficiently.

[0449] The "acquisition means" is a function for acquiring the latest documents from the information provider's website.

[0450] The "change detection means" is a function for comparing a newly acquired document with a previously acquired document to identify changes.

[0451] The "generation means" is a function for analyzing the identified changes and generating summaries and risk assessments.

[0452] The "summarization means" is a function for creating a summary in a form that is easy for the user to understand, based on the analysis results of the changes.

[0453] The "notification means" is a function for promptly notifying the user of the generated summary result.

[0454] The "evaluation sending means" is a function that allows a user to input an evaluation and send the evaluation data to the server.

[0455] The "aggregation means" is a function for aggregating evaluation data sent from multiple users and calculating an overall evaluation and user satisfaction level.

[0456] The "report generation means" is a function for generating reports for companies from the collected evaluation data and user reviews.

[0457] The present invention is implemented as a system including an acquisition means, a change detection means, a generation means, a summarization means, a notification means, an evaluation transmission means, a compilation means, and a report generation means. The main purpose of this system is to provide the contents of documents (e.g., terms and conditions and contracts) for services provided over the Internet in a format that is easy for users to understand, and to clearly indicate the risks of changes, so that users can use the services with peace of mind.

[0458] System Configuration

[0459] The system includes the following main means:

[0460] Acquisition means

[0461] The server periodically accesses the information provider's website to retrieve the latest documents, sending an HTTP request to download the HTML content and extracting the text portion of the document using an HTML parsing library such as BeautifulSoup.

[0462] Change detection method

[0463] The server compares the text of the newly retrieved document with that of the previously retrieved document, using a difference detection algorithm (e.g., difflib) to identify the specific changes.

[0464] Generators and Summarizers

[0465] The server inputs the identified changes into the generative AI model using prompt sentences, and generates a summary in a form that is easy for the user to understand based on the summary and risk assessment output by the generative AI model.

[0466] Example prompt: "Please summarize the updates in the latest policy and clarify the risks."

[0467] Example prompt: "Identify and summarize the changes in your service provider's terms and conditions."

[0468] Notification means

[0469] The server notifies the user of the generated summary results using a web socket or push notification service (e.g., Firebase Cloud Messaging) to deliver the information quickly.

[0470] Evaluation sending method

[0471] The user checks the notification on the device and evaluates the changes. Evaluations are made using a dedicated evaluation form, and the evaluation data is sent from the device to the server.

[0472] Aggregation method

[0473] The server aggregates the evaluation data sent by multiple users, and calculates the overall evaluation of the document and the user satisfaction level, which are then displayed as a score.

[0474] Report Generation Method

[0475] The server generates reports for companies based on the aggregated rating data and user reviews, including recommendations for improvement and risk prevention measures, which companies can receive via email or view through a dedicated dashboard.

[0476] Specific examples

[0477] For example, if the terms and conditions of a particular online shopping site are updated, the server automatically detects the updates and notifies users by summarizing important changes and risks. When users review the summary and provide a rating, the rating is shared with other users, and a report including suggestions for improvement is generated for the company. This allows users to use the shopping site with peace of mind, and companies can quickly obtain feedback to improve customer satisfaction.

[0478] As described above, by using the system of the present invention, users can easily understand complex documents, and companies can be provided with an environment in which they can efficiently collect user feedback.

[0479] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0480] Step 1:

[0481] The server periodically accesses the information provider's website to retrieve the latest documents. First, the server sends an HTTP request to a specified URL using a Cron job. It saves the received HTML content and parses the HTML using BeautifulSoup to extract the text portion of the document from specific tags and classes.

[0482] Input: URL of the informant's website, text of the previously obtained document

[0483] Output: Text of the most recent document

[0484] Step 2:

[0485] The server compares the newly retrieved document with the previously retrieved document. First, it reads both texts and uses a difference detection algorithm (e.g., difflib) to identify the changes. It then lists the changes detected as differences and prepares for the next step.

[0486] Input: Text of the latest document, text of the previously retrieved document

[0487] Output: List of changes

[0488] Step 3:

[0489] The server then formats the identified changes into prompts to be input to the generative AI model, including instructions for inserting the text of the changes and generating a summary and risk statement.

[0490] Input: List of changes

[0491] Output: prompt statement

[0492] Step 4:

[0493] The server sends the prompt to the generative AI model and obtains a summary and risk assessment. Specifically, it calls the generative AI model's API, inputs the prompt, and receives the analysis results, which include a summary of the changes and a risk assessment.

[0494] Input: prompt statement

[0495] Output: Summary results and risk assessment

[0496] Step 5:

[0497] The server notifies the user of the generated summary results. Specifically, it uses a web socket or push notification service (e.g., Firebase Cloud Messaging) to send the summary results to each user's device.

[0498] Input: Summary results and risk assessment

[0499] Output: Notification to the user's device

[0500] Step 6:

[0501] Users receive a notification on their device to confirm the changes, and can view the notification content through an application or web interface and fill out an evaluation form.

[0502] Input: Notification received on the user's device

[0503] Output: Data entered into the evaluation form

[0504] Step 7:

[0505] The user inputs evaluation data and transmits it to the server using the evaluation transmission means, and the transmitted evaluation data is stored in the server's database.

[0506] Input: Data entered into the evaluation form

[0507] Output: Rating data sent to the server

[0508] Step 8:

[0509] The server aggregates the evaluation data sent by multiple users and calculates the overall evaluation and user satisfaction. The aggregation script queries the evaluation data and generates statistical information.

[0510] Input: Multiple evaluation data

[0511] Output: Overall rating score and user satisfaction

[0512] Step 9:

[0513] The server generates reports for the company based on the aggregated data. The report generation script uses the statistical information to create reports in PDF and HTML format, including recommendations and risk management measures for the company.

[0514] Input: Overall rating score and user satisfaction

[0515] Output: Corporate reports

[0516] Step 10:

[0517] The server will send the generated report to the designated corporate contact or make it available for viewing in the corporate dashboard. It will use the email sending library to send the report or update the dashboard link.

[0518] Input: Corporate Report

[0519] Output: Send report to company or update dashboard

[0520] (Application example 1)

[0521] 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."

[0522] Documents used in factories, such as safety manuals and work procedures, are frequently updated. This makes it difficult to identify changes and identify risks, requiring workers to always understand the latest information. However, doing this manually is labor-intensive and can lead to misunderstandings and missed communications. To solve this problem, a system is needed that automatically detects changes in documents, summarizes them, identifies risks, and promptly notifies users.

[0523] 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.

[0524] In this invention, the server includes a means for periodically acquiring documents, a means for analyzing and summarizing changes in documents based on a generative AI model, and a means for notifying users in the factory of the summary results. This makes it possible to automatically identify changes in documents used in the factory, summarize risks, and promptly notify them.

[0525] The "acquisition means" is a means for periodically acquiring the latest documents from the document management system within the factory.

[0526] The "change detection means" is a means for comparing the previously acquired document with the latest document and identifying the changes.

[0527] The "generative means" is a means for analyzing the identified changes based on a generative AI model and summarizing the risks.

[0528] The "summarization means" is a means for summarizing the content of the changes in a form that is easy for the user to understand.

[0529] The "notification means" is a means for quickly notifying users in the factory of the generated summary results.

[0530] The "evaluation sending means" is a means for sending the data evaluated by the user to the server.

[0531] The "counting means" is a means for counting evaluation data sent from users.

[0532] The "report generation means" is a means for generating a report for a company based on the collected evaluation data.

[0533] This invention provides a system that manages documents such as safety manuals and work procedures used in factories, identifies changes, summarizes them, and indicates risks. This system periodically retrieves documents from the document management system, analyzes and summarizes the changes based on a generative AI model, and promptly notifies the user.

[0534] Hardware and software used

[0535] Hardware:

[0536] Server: For example, a cloud server such as Amazon EC2

[0537] User device: smartphone, tablet, or factory workstation

[0538] Factory robots: Robots that physically retrieve documents (if necessary)

[0539] software:

[0540] requests: A Python library for HTTP requests

[0541] ftplib: A Python library for FTP access

[0542] difflib: A Python library for diff detection algorithms

[0543] OpenAI GPT-3: Natural Language Processing with Generative AI Models

[0544] Firebase Cloud Messaging: a service for push notifications

[0545] React: A front-end framework for rating forms

[0546] SendGrid: a service for sending emails

[0547] The working mechanism of the invention

[0548] The server periodically accesses the document management system to retrieve the latest documents, using HTTP requests or FTP. For example, it uses the requests library to retrieve data from the API, or the ftplib library to download files from the FTP server.

[0549] The retrieved document is compared with the previous document to identify the changes. The difference detection is done using the Python difflib library, which effectively detects the differences between two pieces of text.

[0550] The identified changes are fed into a generative AI model (e.g., OpenAI's GPT-3) to summarize and identify risks. The generative AI model analyzes the input text based on a prompt and summarizes it in an understandable format. Here is an example prompt:

[0551] "Please summarize the changes in the following documents and highlight any new risk points:

[0552] "Old version: The operation buttons are green. New version: The operation buttons are red. This change is to prevent operation errors."

[0553] The generated summary results are pushed to the user's device using Firebase Cloud Messaging, and the user receives the notification on their smartphone or work device and can check the contents.

[0554] After checking the notification content, the user rates the app. The rating data is sent to the server via a rating form using React. The rating sending means passes this rating data to the aggregation means.

[0555] The server aggregates the submitted evaluation data and generates a report for the company based on the evaluation results. This report includes an overall evaluation and recommendations for the document. The report can be sent to the company via email using SendGrid or made available for viewing on the company's dashboard.

[0556] For example, when a factory's safety manual is updated, new procedures and risk points may be added. The system automatically detects these changes, summarizes them, and clearly indicates the risks. For example, a notification may be issued saying, "Due to the new procedures, the color of the operation buttons has changed, and workers need to be careful to prevent incorrect operation." Users can quickly understand this information and provide evaluation feedback to the server. This improves safety and work efficiency throughout the factory.

[0557] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0558] Step 1:

[0559] The server periodically accesses the document management system to obtain the latest documents. Specifically, it downloads the documents using HTTP requests or FTP. The input is data from the document management system, and the output is the latest documents stored in the server.

[0560] Step 2:

[0561] The server compares the latest retrieved document with the previous one, using Python's difflib library to find the differences between the text. The input is both the previous and latest document, and the output is a list of changes.

[0562] Step 3:

[0563] The server analyzes and summarizes the identified changes based on a generative AI model (e.g., OpenAI GPT-3). It uses prompts to instruct the AI ​​to specify the changes and risks. The input is the text of the changes, and the output is a text containing a summary and a description of the risks. Example prompts:

[0564] "Please summarize the changes in the following documents and highlight any new risk points:

[0565] "Old version: The operation buttons are green. New version: The operation buttons are red. This change is to prevent operation errors."

[0566] Step 4:

[0567] The server sends the generated summary results to the user's device using Firebase Cloud Messaging. The input is the text of the summary results, and the output is a push notification to the user's device. The user receives the notification content on their smartphone or work device.

[0568] Step 5:

[0569] The user checks the notification content and rates it using their workstation. The rating data is sent to the server through a dedicated rating form (e.g., a React-based web form). The input is the user's rating data, and the output is the rating data sent to the server.

[0570] Step 6:

[0571] The server aggregates the submitted rating data. It collects ratings from multiple users and calculates a rating score. The input is the user rating data, and the output is the aggregated rating score.

[0572] Step 7:

[0573] The server generates reports for companies based on the aggregated rating scores. The reports are sent to companies via email using SendGrid or displayed on a company dashboard. The input is the aggregated rating scores and user reviews, and the output is a report for the company.

[0574] Through the above processing steps, the present invention realizes a system that automates document management and risk assessment within a factory, notifies users quickly, and provides appropriate feedback.

[0575] 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.

[0576] The present invention is embodied as a system including an acquisition means, a change detection means, a generation means, a summarization means, a notification means, a rating transmission means, a compilation means, a report generation means, and an emotion engine. The system aims to improve the user experience by enabling users to easily understand terms and conditions and contracts of online services and by recognizing the user's emotions and providing feedback.

[0577] Obtaining the terms and conditions

[0578] The server periodically accesses the service provider's website to retrieve the latest terms and conditions. This is done by sending an HTTP request and parsing the HTML content returned as a response. An HTML parsing library is used to extract the terms and conditions text.

[0579] Change Detection

[0580] The server compares the newly retrieved clause with the previously retrieved clause and uses a difference detection algorithm to identify any changes in the text.

[0581] Summary of changes and risk identification

[0582] The server inputs the text of the changes into the generation AI, which then summarizes them and identifies the risks. Specifically, natural language processing technology is used to summarize the changes in a way that is easy for users to understand, highlighting points that require particular attention and risks.

[0583] Notification of summary results

[0584] The server notifies the user of the generated summary results via web sockets or push notifications. Users can receive these notifications in real time via a smartphone app or web browser.

[0585] Evaluation of terms and conditions

[0586] The user checks the notification displayed on the device and reads the changes to the terms and conditions. The user can check the summary of the changes and the explicit description of the risks, and understand the contents.

[0587] User Emotion Recognition

[0588] When a user uses a device to evaluate the policy changes, the emotion engine recognizes emotions from the user's facial expressions, voice, and text input. This emotion data is recorded along with the user's evaluation.

[0589] Sending emotional data

[0590] The terminal transmits the user's evaluation data and emotion data to the server, which aggregates the emotion data along with the evaluation data.

[0591] Aggregation of rating and sentiment data

[0592] The server aggregates the evaluation data and emotion data sent by multiple users, thereby generating data that indicates the overall evaluation of the terms and conditions and the emotional state of the users.

[0593] Generate reports

[0594] The server generates a report for the company based on the aggregated evaluation and sentiment data, including suggestions for policy improvements and risk avoidance. The report is then emailed to the company's contact person or made available for review on a dedicated company dashboard.

[0595] Specific examples

[0596] For example, if the terms and conditions of a particular online education service are updated, the system automatically detects the changes and notifies the user by summarizing important changes and risks. When the user reviews and evaluates the changes, the emotion engine identifies the user's emotions and sends this data to the server. A report is then generated for the company based on the aggregated results, and the company is provided with improvement suggestions that include the user's emotional state. This allows users to use the education service with peace of mind and allows companies to receive more specific feedback.

[0597] The above is an embodiment of the present invention. By combining an emotion engine, it is possible to recognize the user's emotions and provide more personalized feedback, further improving the user experience.

[0598] The processing flow will be explained below.

[0599] Step 1:

[0600] The server accesses the service provider's website to retrieve the latest terms and conditions. This is done by sending an HTTP request and downloading the HTML content returned as a response. An HTML parsing library (e.g., BeautifulSoup) is used to extract the terms and conditions text from the retrieved HTML content.

[0601] Step 2:

[0602] The server compares the newly retrieved terms and conditions with the previously retrieved terms and conditions. It uses a difference detection algorithm to identify changes in words and sentences. Specifically, it compares the previous and current terms and conditions line by line or character by character to identify additions and deletions.

[0603] Step 3:

[0604] The server inputs the changes into the generation AI, which then summarizes the changes and identifies the risks. For example, it can use OpenAI's API to analyze the detected changes and summarize them in a way that is easy for users to understand, highlighting points to be aware of and risks.

[0605] Step 4:

[0606] The server notifies the generated summary results to the user's device using web socket and push notification technologies, and the user can receive the notification in real time via a smartphone app or web browser.

[0607] Step 5:

[0608] The user checks the notification displayed on their device and reads the changes to the terms and conditions. They then view the generated summary and risk information to understand the content. During this process, the emotion engine monitors the user's facial expressions and voice to recognize their emotions.

[0609] Step 6:

[0610] Users use their devices to evaluate the changes to the terms and conditions. Evaluations are made through an evaluation form, where users enter a score from 1 to 5 and any additional comments. At the same time, an emotion engine analyzes the user's emotions in real time and generates emotion data.

[0611] Step 7:

[0612] The device transmits user evaluation data and emotion data to the server. The transmitted data includes evaluation scores, comments, and emotion recognition data. The server then aggregates the data.

[0613] Step 8:

[0614] The server aggregates the evaluation data and emotional data sent by multiple users. This generates data showing the overall evaluation of the terms and conditions and the emotional state of the users. The aggregated results are displayed on a dashboard.

[0615] Step 9:

[0616] The server generates a report for the company based on the aggregated evaluation and sentiment data, including suggestions for improving the terms and conditions and avoiding risks. The report is then emailed to the company's representative or made available for review on a dedicated company dashboard.

[0617] Example 2

[0618] 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."

[0619] In recent years, with the spread of online services, users are increasingly required to understand complex and lengthy terms and conditions and contracts. However, these documents contain many technical and legal terms, making them difficult for average users to understand. Furthermore, frequently changing terms and conditions place a significant burden on users, who must constantly review them and recognize risks. Furthermore, it is not easy for companies to properly understand user sentiment and feedback and use it to improve terms and conditions. Therefore, there is a need for a system that can automatically detect and summarize changes in terms and conditions, indicate risks, and recognize user sentiment to improve the user experience.

[0620] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0621] In this invention, the server includes an acquisition means, a change detection means, a generation means, a summarization means, a notification means, an evaluation transmission means, a compilation means, a report generation means, and an emotion recognition means, which enables the following:

[0622] 1. The server periodically retrieves the latest terms and conditions from the service provider's website and compares them with the previous terms and conditions to identify any changes.

[0623] 2. Use a generative AI model to analyze the identified changes and summarize the risks, providing information in a way that is easy for users to understand.

[0624] 3. Notify the user of the summary results in real time and collect user evaluation data and sentiment data.

[0625] 4. Generate reports for companies based on the collected evaluation and sentiment data and provide specific improvement suggestions.

[0626] This allows users to quickly respond to complex changes in terms and conditions and understand the risks, while also allowing companies to obtain useful feedback to improve their terms and conditions based on user sentiment and evaluations, thereby improving the user experience.

[0627] "Acquisition means" refers to a means that has the function of periodically acquiring the latest terms and conditions from the service provider's website.

[0628] The "change detection means" is a means having a function of comparing the previously acquired terms and conditions with the latest terms and conditions and identifying the changed parts.

[0629] "Generative means" means a means having the function of analyzing the changes identified using a generative AI model and summarizing the risks.

[0630] The "summarization means" is a means having a function of recording and storing the generated summary results and risk information.

[0631] The "notification means" is a means having a function of notifying the generated summary result to the user's terminal in real time.

[0632] The "evaluation sending means" is a means having a function of sending data evaluated by the user to the server.

[0633] The "aggregation means" is a means having a function of aggregating evaluation data and emotion data sent from a plurality of users and generating statistical information.

[0634] The "report generation means" is a means having a function of creating a report for a company based on the collected evaluation data and emotion data.

[0635] The "emotion recognition means" is a means having a function of recognizing emotions from the user's facial expressions, voice, and text input, and recording the emotion data.

[0636] A "generative AI model" is a model that uses natural language processing technology to summarize and identify risks based on input text.

[0637] A "prompt" is text used as input to a generative AI model, and is an instruction used to summarize and clarify risks.

[0638] The present invention is embodied as a system including an acquisition means, a change detection means, a generation means, a summarization means, a notification means, a rating transmission means, a compilation means, a report generation means, and an emotion recognition means, which not only makes it easier for users to understand terms and conditions and contracts of online services, but also recognizes the user's emotions and provides appropriate feedback to improve the user experience.

[0639] Obtaining the terms and conditions

[0640] The server periodically accesses the service provider's website to retrieve the latest terms and conditions. This process is performed by sending an HTTP request and analyzing the HTML content received as a response. Specifically, the "requests" library is used to send the HTTP request, and the "BeautifulSoup" library is used to parse the HTML content and retrieve the text of the terms and conditions.

[0641] Change Detection

[0642] The server compares the newly acquired terms and conditions with the previously acquired terms and conditions. The comparison is performed using a difference detection algorithm such as DiffMatchPatch to identify changes. This makes it possible to clearly identify important changes for the user.

[0643] Summary of changes and risk identification

[0644] The server inputs the text of the changes as a prompt to the generative AI model (e.g., OpenAI's GPT-3), summarizing the changes and highlighting the risks. Specific examples of prompts include:

[0645] To help users understand the latest changes, please summarize the following text and highlight any important caveats or risks:

[0646] [Insert text of change to terms here]

[0647] The generative AI model provides a concise summary of the revised terms and conditions, highlighting any risks that require particular attention.

[0648] Notification of summary results

[0649] The server notifies the user of the generated summary results via web sockets or push notifications, allowing the user to receive important change information in real time via a smartphone app or web browser.

[0650] Evaluation of terms and conditions

[0651] The user checks the notification on their device and views the changes to the terms and conditions. They then review a summary of the changes and a statement of the risks, and understand the details. This allows the user to quickly grasp important changes.

[0652] User Emotion Recognition

[0653] When a user uses the device to evaluate the policy changes, the device uses emotion recognition means to capture the user's facial expressions and voice to recognize their emotions, for example, by using the OpenCV library or Microsoft Azure Cognitive Services to recognize and record the user's emotions in real time.

[0654] Sending emotional data

[0655] The device sends the user's rating and emotion data to the server by sending an HTTP POST request using the "requests" library.

[0656] Aggregation of evaluation data and emotion data

[0657] The server collects and aggregates the evaluation and emotion data sent by multiple users into a database. Specifically, it uses the Pandas library to analyze the evaluation and emotion data and generate statistical information.

[0658] Generate reports

[0659] The server generates a report for the company based on the aggregated evaluation data and sentiment data. This report includes suggestions for improving terms and conditions and avoiding risks. The generated report is either sent to the company's personnel via email or provided for viewing on a dedicated corporate dashboard. Specifically, the report is created in HTML format using the "Jinja2" template engine, and emailed using the "smtplib" library. The corporate dashboard is also built using "Flask" as a web framework.

[0660] Specific examples

[0661] For example, if the terms and conditions of a particular online education service are updated, the system automatically detects the changes and notifies the user by summarizing important changes and risks. When the user reviews and evaluates the changes, the emotion engine identifies the user's emotions and sends this data to the server. The server generates a report for the company based on the collected data, and provides the company with improvement suggestions that include the user's emotional state. This allows users to use the education service with peace of mind and allows companies to receive specific feedback.

[0662] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0663] Step 1:

[0664] The server accesses the service provider's website at 2:00 AM every day to retrieve the latest terms and conditions. The input for this process is the website URL, and an HTTP request is sent to retrieve the HTML content. The HTTP request is sent using the "requests" library, and the HTML content received as a response is analyzed using the "BeautifulSoup" library to extract the terms and conditions text. As an output, the extracted terms and conditions text is saved in a database.

[0665] Step 2:

[0666] The server retrieves the newly acquired clause text and the previously acquired clause text from the database. The input to this process is the two clause texts retrieved from the database. Next, a difference detection algorithm such as DiffMatchPatch is used to identify the changes in the text. As an output, the server generates the text of the identified changes and stores it in the database.

[0667] Step 3:

[0668] The server inputs the text of the change to the generative AI model as a prompt. The input for this process is the text of the change. Here is an example of a prompt sentence to input to the generative AI model:

[0669] To help users understand the latest changes, please summarize the following text and highlight any important caveats or risks:

[0670] [Insert text of change to terms here]

[0671] The generative AI model analyzes this input, produces a summary and identifies risks, and stores the resulting summary and risk information in a database as output.

[0672] Step 4:

[0673] The server notifies the user's device of the generated summary results. The input for this process is the summary results and risk information. Web sockets and push notifications are used as notification methods. The server uses the "WebSocket" library and "Firebase Cloud Messaging" to send notifications to the user's device in real time. As an output, notifications are displayed on the user's device.

[0674] Step 5:

[0675] The user checks the notification displayed on their device and views the changes to the terms and conditions. The input to this process is a summary of the terms and conditions received as a notification. The user then views this information using a smartphone app or web browser to understand the content. The output is a deeper understanding of the user.

[0676] Step 6:

[0677] When a user uses a device to evaluate changes to the terms and conditions, the device captures the user's facial expressions and voice using a camera and microphone, and analyzes them using emotion recognition. The input for this process is the text to be evaluated and the captured user's facial expressions and voice data. The device recognizes emotions and records the emotion data using OpenCV and Microsoft Azure Cognitive Services. As an output, emotion data is generated and recorded.

[0678] Step 7:

[0679] The device sends the user's rating data and emotion data to the server. The input of this process is the rating data and emotion data. The device uses the "requests" library to send an HTTP POST request to the server's API endpoint, and the data is sent to the server as output.

[0680] Step 8:

[0681] The server imports rating data and emotion data submitted by multiple users into a database and aggregates them. The input for this process is the rating data and emotion data stored in the database. The "Pandas" library is used to analyze the data and generate statistical information. The aggregated results are generated as the output.

[0682] Step 9:

[0683] The server generates a report for the company based on the aggregated evaluation data and sentiment data. The input for this process is the aggregated data. The report is created in HTML format using the "Jinja2" template engine and sent by email using the "smtplib" library. In addition, a dashboard for the company is provided using "Flask" as a web framework. The generated report is provided to the company's personnel as an output.

[0684] (Application example 2)

[0685] 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."

[0686] Changes to terms of service are frequent in modern online services, making it difficult for users to keep up with them in a timely and accurate manner. Understanding user sentiment regarding changes to terms of service provides important feedback for service providers, but a system for efficiently collecting and analyzing this information has yet to be established. Furthermore, there is a lack of a system for clearly communicating the risks and importance of changes to users, which often results in a poor user experience.

[0687] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a generating means, an acquiring means, a change detecting means, a summarizing means, a notifying means, an emotion recognizing means, an evaluation sending means, a tabulating means, and a report generating means. This makes it possible to quickly detect changes in the terms of use, summarize the risks, and notify the user, and further collect user emotion data, aggregate this data, and provide feedback to the company.

[0688] The "generation means" is a means for analyzing the identified changes and summarizing them in a form that is easy for the user to understand.

[0689] "Means for Acquisition" means a means for periodically obtaining the latest terms and conditions from the website of the online service provider.

[0690] The "change detection means" is a means for comparing the previously acquired terms and conditions with the latest terms and conditions to identify any changes.

[0691] The "summarization means" is a means for summarizing important points and risks of the changes identified by the change detection means.

[0692] The "notification means" is a means for notifying the user of the generated summary result.

[0693] An "emotion recognition means" is a means for recognizing emotions from a user's facial expressions, voice, text input, etc., and acquiring that data.

[0694] The "evaluation sending means" is a means for sending the data rated by the user and the emotion data acquired by the emotion recognition means to the server.

[0695] The "aggregation means" is a means for aggregating the evaluation data and emotion data sent from a plurality of users.

[0696] The "report generation means" is a means for generating a report for a company based on the collected evaluation data and emotion data.

[0697] A system for carrying out the present invention includes the following means:

[0698] 1. Acquisition method:

[0699] The server periodically retrieves the latest terms and conditions from the online service provider's website by sending an HTTP request and parsing the HTML content returned as a response. Specifically, it uses an HTML parsing library such as BeautifulSoup to extract the terms and conditions text.

[0700] 2. Change detection methods:

[0701] The server compares the previously retrieved contract with the newly retrieved contract, using a difference detection algorithm such as difflib to detect text differences and identify changes.

[0702] 3. Summary tools:

[0703] The server uses a generative AI model to summarize the risks of the changes. It generates prompt sentences, inputs them into the generative AI model, and summarizes the changes using natural language processing technology.

[0704] 4. Means of notification:

[0705] The generated summary results are sent to the user's device (smartphone app or web browser) via web socket or push notification, allowing the user to receive real-time updates.

[0706] 5. Emotion recognition means:

[0707] As users evaluate the policy changes, the device recognizes their emotions from their facial expressions, voice, and text input. This emotional data is obtained using an emotion recognition engine such as EmotionEngine.

[0708] 6. Evaluation submission method:

[0709] The device transmits user rating and emotion data to the server, which is then aggregated.

[0710] 7. Aggregation method:

[0711] The server aggregates the evaluation data and emotional data sent by multiple users, thereby generating data that indicates the overall evaluation of the terms and conditions and the emotional state of the users.

[0712] 8. Report Generation Methods:

[0713] The server generates reports for companies based on the aggregated evaluation and sentiment data, including recommendations for policy improvements and risk avoidance, which are emailed to company representatives or made available for review on a dedicated company dashboard.

[0714] Specific examples

[0715] For example, if the terms and conditions of a particular online education service are updated, the system automatically detects the changes and notifies the user by summarizing important changes and risks. When the user reviews and evaluates the changes, the emotion engine identifies the user's emotions and sends this data to the server. A report is then generated for the company based on the aggregated results, and the company is provided with improvement suggestions that take into account the user's emotional state. This allows users to use the service with peace of mind and allows companies to receive more specific feedback.

[0716] Prompt Sentence Examples

[0717] Changes:

[0718] The usage fee will increase from 3% to 5%.

[0719] Please summarize."

[0720] Notification summary and risk statement:

[0721] "

[0722] notification:

[0723] Latest Terms and Conditions Changes:

[0724] The service fee has increased from 3% to 5%, a change that may directly affect the payment costs of users.

[0725] "

[0726] In this way, by using the system of the present invention, it is possible to quickly grasp changes to the terms of use and the risks involved, and also to provide feedback that reflects the user's feelings.

[0727] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0728] Step 1:

[0729] The server retrieves the latest terms and conditions from the online service provider's website by sending an HTTP request and parsing the HTML content returned as a response. A library such as BeautifulSoup is used for HTML parsing, and the latest terms and conditions text is extracted.

[0730] Input: Service provider website URL

[0731] Output: Latest extracted clause text

[0732] Step 2:

[0733] The server compares the previously retrieved clause with the latest clause to identify any changes. This comparison uses a difference detection algorithm such as difflib, and a change detection means detects the differences in the text.

[0734] Input: Latest clause text, previously retrieved clause text

[0735] Output: List of changes

[0736] Step 3:

[0737] The server uses a generative AI model to summarize the risks of the changes. A prompt is input into the generative AI model, which then uses natural language processing technology to summarize the changes. Based on the input prompt, the generative AI outputs text summarizing the important points and risks of the changes.

[0738] Input: list of changes, prompt

[0739] Output: Summarized risk text

[0740] Step 4:

[0741] The server then notifies the user of the generated summary results via web sockets or push notifications, which are sent in real time to the user's smartphone app or web browser.

[0742] Input: Abstracted risk text

[0743] Output: Notification to user's device

[0744] Step 5:

[0745] The user reviews and evaluates the changes to the terms and conditions. In addition, the user's device recognizes emotions from the user's facial expressions, voice, and text input. This emotion data is obtained using an emotion recognition engine such as EmotionEngine.

[0746] Input: User facial expressions, voice, and text input

[0747] Output: Emotion data, rating data

[0748] Step 6:

[0749] The terminal transmits the user's evaluation data and emotion data to the server via the evaluation transmitting means.

[0750] Input: Emotion data, rating data

[0751] Output: Send data to the server

[0752] Step 7:

[0753] The server aggregates the evaluation data and emotional data sent by multiple users, thereby generating data that indicates the overall evaluation of the terms and conditions and the emotional state of the users.

[0754] Input: Rating data and sentiment data from multiple users

[0755] Output: Aggregated rating data, sentiment data

[0756] Step 8:

[0757] The server generates reports for companies based on the aggregated evaluation and sentiment data, including recommendations for policy improvements and risk avoidance, which are emailed to company representatives or made available for review on a dedicated company dashboard.

[0758] Input: Aggregated rating data, sentiment data

[0759] Output: Corporate reports

[0760] This allows users to use the service with peace of mind and allows companies to receive more specific feedback.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] [Third embodiment]

[0765] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0766] 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.

[0767] 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).

[0768] 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.

[0769] 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.

[0770] 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).

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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."

[0777] The present invention is implemented as a system including an acquisition means, a change detection means, a generation means, a summarization means, a notification means, an evaluation sending means, a compilation means, and a report generation means. The purpose of this system is to enable users to use services with peace of mind by clearly explaining the contents of the terms and conditions and contracts of services provided on the Web, identifying changes, and clearly indicating risks.

[0778] Obtaining the terms and conditions

[0779] The server periodically accesses the service provider's website to retrieve the latest terms and conditions. This is done by first sending a request and downloading the HTML content, then using an HTML parsing library (e.g. BeautifulSoup) to extract the text portion of the terms and conditions.

[0780] Change Detection

[0781] The server compares the newly acquired terms and conditions with the previously acquired terms and conditions. As a result of this comparison, any changes are identified. A difference detection algorithm is used to extract the specific details of the changes.

[0782] Summary of changes and risk identification

[0783] The server inputs the identified changes into a generation AI, which analyzes them and summarizes them and indicates the risks. For example, by using natural language processing technology such as OpenAI, the changes can be summarized in a form that is easy for users to understand, and risks and points of caution can be highlighted.

[0784] Notification of summary results

[0785] The server then sends the generated summary to the user's device, using web sockets and push notifications to quickly notify the user of changes. Users can receive notifications via a smartphone app or web interface.

[0786] Evaluation of terms and conditions

[0787] The user can use their device to check the notification and evaluate the changes and the entire terms and conditions. This evaluation is done through a dedicated evaluation form, and the evaluation data is sent from the device to the server.

[0788] Aggregation of evaluation data

[0789] The server aggregates the evaluation data sent by users, and calculates the overall evaluation of the terms and conditions and user satisfaction level based on the evaluations collected from multiple users, and displays them as a score.

[0790] Generate reports

[0791] The server generates reports for companies based on the aggregated rating data and user reviews, including suggestions for improvement and risk mitigation, and can be sent to the company via email or made available on a dedicated company dashboard.

[0792] Specific examples

[0793] For example, if the terms and conditions of a particular online shopping site are updated, the system will automatically detect the updates and notify users by summarizing important changes and risks. When users review the summary and provide a rating, the rating is shared with other users, and a report including suggestions for improvement is generated for the company. This allows users to use the shopping site with peace of mind, and companies can receive feedback to improve customer satisfaction.

[0794] The above is an embodiment of the present invention. By using this system, users can easily understand complex terms and conditions and contracts, realizing an environment in which they can use online services with greater peace of mind.

[0795] The processing flow will be explained below.

[0796] Step 1:

[0797] The server accesses the service provider's website to retrieve the latest terms and conditions. This is done by sending an HTTP request and downloading the HTML content returned as a response. The terms and conditions text is extracted from the retrieved HTML content using an HTML parsing library such as BeautifulSoup.

[0798] Step 2:

[0799] The server compares the most recent version of the terms and conditions with the previous version. It uses a difference detection algorithm to identify changes in the text. Specifically, it compares the previous and current terms and conditions line by line and character by character to identify additions and deletions.

[0800] Step 3:

[0801] The server inputs the text of the changes into the generating AI, which then summarizes them and indicates the risks. For example, by using OpenAI's API, the server can summarize the detected changes in an easy-to-understand format, highlighting points that require particular attention and risks.

[0802] Step 4:

[0803] The server notifies the user of the generated summary results via web sockets or push notifications. Users can receive these notifications in real time via a smartphone app or web browser.

[0804] Step 5:

[0805] The user checks the notification displayed on the device and reads the changes to the terms and conditions. The user can check the summary of the changes and the explicit description of the risks, and understand the contents.

[0806] Step 6:

[0807] Users use their devices to evaluate the changes to the terms and conditions. Evaluations are conducted through a dedicated form, and users rate the reliability and comprehension of the changes on a scale of 1 to 5.

[0808] Step 7:

[0809] The terminal transmits the user's evaluation data, including the user's score and additional comments, to the server, where the transmitted evaluation data is stored and aggregated.

[0810] Step 8:

[0811] The server aggregates the evaluation data sent by multiple users and calculates an overall evaluation score. The aggregated results are compiled as data showing the evaluation of the service and the level of understanding of the terms and conditions, and are displayed on a dashboard.

[0812] Step 9:

[0813] The server generates a report for the company based on the aggregated rating data and user reviews. This report includes suggestions for improving the terms and conditions and avoiding risks. The report is either emailed to the company's representative or made available for review on a dedicated company dashboard.

[0814] The above is the specific processing flow in this system.

[0815] Example 1

[0816] 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."

[0817] Currently, terms and conditions and contracts for services provided online are extremely complex, making them difficult for users to understand, and even when changes are made, users often go unnoticed. As a result, users may overlook important risks or changes and fail to take appropriate action. It is also difficult for companies to efficiently collect feedback from users, improve satisfaction, and avoid risks. Therefore, there is a need for a system that allows users to easily understand changes and risks in terms and conditions and provide feedback through evaluations.

[0818] 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.

[0819] In this invention, the server includes an acquisition means, a change detection means, a generation means, a summarization means, a notification means, an evaluation transmission means, a compilation means, and a report generation means. This makes it possible to acquire the latest documents from the information provider's website, analyze their contents, summarize changes and risks, and notify users. Furthermore, users can evaluate the changes, and the evaluation data can be compiled to provide proposals to companies. This system allows users to easily understand important changes and risks, and companies can receive feedback efficiently.

[0820] The "acquisition means" is a function for acquiring the latest documents from the information provider's website.

[0821] The "change detection means" is a function for comparing a newly acquired document with a previously acquired document to identify changes.

[0822] The "generation means" is a function for analyzing the identified changes and generating summaries and risk assessments.

[0823] The "summarization means" is a function for creating a summary in a form that is easy for the user to understand, based on the analysis results of the changes.

[0824] The "notification means" is a function for promptly notifying the user of the generated summary result.

[0825] The "evaluation sending means" is a function that allows a user to input an evaluation and send the evaluation data to the server.

[0826] The "aggregation means" is a function for aggregating evaluation data sent from multiple users and calculating an overall evaluation and user satisfaction level.

[0827] The "report generation means" is a function for generating reports for companies from the collected evaluation data and user reviews.

[0828] The present invention is implemented as a system including an acquisition means, a change detection means, a generation means, a summarization means, a notification means, an evaluation transmission means, a compilation means, and a report generation means. The main purpose of this system is to provide the contents of documents (e.g., terms and conditions and contracts) for services provided over the Internet in a format that is easy for users to understand, and to clearly indicate the risks of changes, so that users can use the services with peace of mind.

[0829] System Configuration

[0830] The system includes the following main means:

[0831] Acquisition means

[0832] The server periodically accesses the information provider's website to retrieve the latest documents, sending an HTTP request to download the HTML content and extracting the text portion of the document using an HTML parsing library such as BeautifulSoup.

[0833] Change detection method

[0834] The server compares the text of the newly retrieved document with that of the previously retrieved document, using a difference detection algorithm (e.g., difflib) to identify the specific changes.

[0835] Generators and Summarizers

[0836] The server inputs the identified changes into the generative AI model using prompt sentences, and generates a summary in a form that is easy for the user to understand based on the summary and risk assessment output by the generative AI model.

[0837] Example prompt: "Please summarize the updates in the latest policy and clarify the risks."

[0838] Example prompt: "Identify and summarize the changes in your service provider's terms and conditions."

[0839] Notification means

[0840] The server notifies the user of the generated summary results using a web socket or push notification service (e.g., Firebase Cloud Messaging) to deliver the information quickly.

[0841] Evaluation sending method

[0842] The user checks the notification on the device and evaluates the changes. Evaluations are made using a dedicated evaluation form, and the evaluation data is sent from the device to the server.

[0843] Aggregation method

[0844] The server aggregates the evaluation data sent by multiple users, and calculates the overall evaluation of the document and the user satisfaction level, which are then displayed as a score.

[0845] Report Generation Method

[0846] The server generates reports for companies based on the aggregated rating data and user reviews, including recommendations for improvement and risk prevention measures, which companies can receive via email or view through a dedicated dashboard.

[0847] Specific examples

[0848] For example, if the terms and conditions of a particular online shopping site are updated, the server automatically detects the updates and notifies users by summarizing important changes and risks. When users review the summary and provide a rating, the rating is shared with other users, and a report including suggestions for improvement is generated for the company. This allows users to use the shopping site with peace of mind, and companies can quickly obtain feedback to improve customer satisfaction.

[0849] As described above, by using the system of the present invention, users can easily understand complex documents, and companies can be provided with an environment in which they can efficiently collect user feedback.

[0850] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0851] Step 1:

[0852] The server periodically accesses the information provider's website to retrieve the latest documents. First, the server sends an HTTP request to a specified URL using a Cron job. It saves the received HTML content and parses the HTML using BeautifulSoup to extract the text portion of the document from specific tags and classes.

[0853] Input: URL of the informant's website, text of the previously obtained document

[0854] Output: Text of the most recent document

[0855] Step 2:

[0856] The server compares the newly retrieved document with the previously retrieved document. First, it reads both texts and uses a difference detection algorithm (e.g., difflib) to identify the changes. It then lists the changes detected as differences and prepares for the next step.

[0857] Input: Text of the latest document, text of the previously retrieved document

[0858] Output: List of changes

[0859] Step 3:

[0860] The server then formats the identified changes into prompts to be input to the generative AI model, including instructions for inserting the text of the changes and generating a summary and risk statement.

[0861] Input: List of changes

[0862] Output: prompt statement

[0863] Step 4:

[0864] The server sends the prompt to the generative AI model and obtains a summary and risk assessment. Specifically, it calls the generative AI model's API, inputs the prompt, and receives the analysis results, which include a summary of the changes and a risk assessment.

[0865] Input: prompt statement

[0866] Output: Summary results and risk assessment

[0867] Step 5:

[0868] The server notifies the user of the generated summary results. Specifically, it uses a web socket or push notification service (e.g., Firebase Cloud Messaging) to send the summary results to each user's device.

[0869] Input: Summary results and risk assessment

[0870] Output: Notification to the user's device

[0871] Step 6:

[0872] Users receive a notification on their device to confirm the changes, and can view the notification content through an application or web interface and fill out an evaluation form.

[0873] Input: Notification received on the user's device

[0874] Output: Data entered into the evaluation form

[0875] Step 7:

[0876] The user inputs evaluation data and transmits it to the server using the evaluation transmission means, and the transmitted evaluation data is stored in the server's database.

[0877] Input: Data entered into the evaluation form

[0878] Output: Rating data sent to the server

[0879] Step 8:

[0880] The server aggregates the evaluation data sent by multiple users and calculates the overall evaluation and user satisfaction. The aggregation script queries the evaluation data and generates statistical information.

[0881] Input: Multiple evaluation data

[0882] Output: Overall rating score and user satisfaction

[0883] Step 9:

[0884] The server generates reports for the company based on the aggregated data. The report generation script uses the statistical information to create reports in PDF and HTML format, including recommendations and risk management measures for the company.

[0885] Input: Overall rating score and user satisfaction

[0886] Output: Corporate reports

[0887] Step 10:

[0888] The server will send the generated report to the designated corporate contact or make it available for viewing in the corporate dashboard. It will use the email sending library to send the report or update the dashboard link.

[0889] Input: Corporate Report

[0890] Output: Send report to company or update dashboard

[0891] (Application example 1)

[0892] 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."

[0893] Documents used in factories, such as safety manuals and work procedures, are frequently updated. This makes it difficult to identify changes and identify risks, requiring workers to always understand the latest information. However, doing this manually is labor-intensive and can lead to misunderstandings and missed communications. To solve this problem, a system is needed that automatically detects changes in documents, summarizes them, identifies risks, and promptly notifies users.

[0894] 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.

[0895] In this invention, the server includes a means for periodically acquiring documents, a means for analyzing and summarizing changes in documents based on a generative AI model, and a means for notifying users in the factory of the summary results. This makes it possible to automatically identify changes in documents used in the factory, summarize risks, and promptly notify them.

[0896] The "acquisition means" is a means for periodically acquiring the latest documents from the document management system within the factory.

[0897] The "change detection means" is a means for comparing the previously acquired document with the latest document and identifying the changes.

[0898] The "generative means" is a means for analyzing the identified changes based on a generative AI model and summarizing the risks.

[0899] The "summarization means" is a means for summarizing the content of the changes in a form that is easy for the user to understand.

[0900] The "notification means" is a means for quickly notifying users in the factory of the generated summary results.

[0901] The "evaluation sending means" is a means for sending the data evaluated by the user to the server.

[0902] The "counting means" is a means for counting evaluation data sent from users.

[0903] The "report generation means" is a means for generating a report for a company based on the collected evaluation data.

[0904] This invention provides a system that manages documents such as safety manuals and work procedures used in factories, identifies changes, summarizes them, and indicates risks. This system periodically retrieves documents from the document management system, analyzes and summarizes the changes based on a generative AI model, and promptly notifies the user.

[0905] Hardware and software used

[0906] Hardware:

[0907] Server: For example, a cloud server such as Amazon EC2

[0908] User device: smartphone, tablet, or factory workstation

[0909] Factory robots: Robots that physically retrieve documents (if necessary)

[0910] software:

[0911] requests: A Python library for HTTP requests

[0912] ftplib: A Python library for FTP access

[0913] difflib: A Python library for diff detection algorithms

[0914] OpenAI GPT-3: Natural Language Processing with Generative AI Models

[0915] Firebase Cloud Messaging: a service for push notifications

[0916] React: A front-end framework for rating forms

[0917] SendGrid: a service for sending emails

[0918] The working mechanism of the invention

[0919] The server periodically accesses the document management system to retrieve the latest documents, using HTTP requests or FTP. For example, it uses the requests library to retrieve data from the API, or the ftplib library to download files from the FTP server.

[0920] The retrieved document is compared with the previous document to identify the changes. The difference detection is done using the Python difflib library, which effectively detects the differences between two pieces of text.

[0921] The identified changes are fed into a generative AI model (e.g., OpenAI's GPT-3) to summarize and identify risks. The generative AI model analyzes the input text based on a prompt and summarizes it in an understandable format. Here is an example prompt:

[0922] "Please summarize the changes in the following documents and highlight any new risk points:

[0923] "Old version: The operation buttons are green. New version: The operation buttons are red. This change is to prevent operation errors."

[0924] The generated summary results are pushed to the user's device using Firebase Cloud Messaging, and the user receives the notification on their smartphone or work device and can check the contents.

[0925] After checking the notification content, the user rates the app. The rating data is sent to the server via a rating form using React. The rating sending means passes this rating data to the aggregation means.

[0926] The server aggregates the submitted evaluation data and generates a report for the company based on the evaluation results. This report includes an overall evaluation and recommendations for the document. The report can be sent to the company via email using SendGrid or made available for viewing on the company's dashboard.

[0927] For example, when a factory's safety manual is updated, new procedures and risk points may be added. The system automatically detects these changes, summarizes them, and clearly indicates the risks. For example, a notification may be issued saying, "Due to the new procedures, the color of the operation buttons has changed, and workers need to be careful to prevent incorrect operation." Users can quickly understand this information and provide evaluation feedback to the server. This improves safety and work efficiency throughout the factory.

[0928] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0929] Step 1:

[0930] The server periodically accesses the document management system to obtain the latest documents. Specifically, it downloads the documents using HTTP requests or FTP. The input is data from the document management system, and the output is the latest documents stored in the server.

[0931] Step 2:

[0932] The server compares the latest retrieved document with the previous one, using Python's difflib library to find the differences between the text. The input is both the previous and latest document, and the output is a list of changes.

[0933] Step 3:

[0934] The server analyzes and summarizes the identified changes based on a generative AI model (e.g., OpenAI GPT-3). It uses prompts to instruct the AI ​​to specify the changes and risks. The input is the text of the changes, and the output is a text containing a summary and a description of the risks. Example prompts:

[0935] "Please summarize the changes in the following documents and highlight any new risk points:

[0936] "Old version: The operation buttons are green. New version: The operation buttons are red. This change is to prevent operation errors."

[0937] Step 4:

[0938] The server sends the generated summary results to the user's device using Firebase Cloud Messaging. The input is the text of the summary results, and the output is a push notification to the user's device. The user receives the notification content on their smartphone or work device.

[0939] Step 5:

[0940] The user checks the notification content and rates it using their workstation. The rating data is sent to the server through a dedicated rating form (e.g., a React-based web form). The input is the user's rating data, and the output is the rating data sent to the server.

[0941] Step 6:

[0942] The server aggregates the submitted rating data. It collects ratings from multiple users and calculates a rating score. The input is the user rating data, and the output is the aggregated rating score.

[0943] Step 7:

[0944] The server generates reports for companies based on the aggregated rating scores. The reports are sent to companies via email using SendGrid or displayed on a company dashboard. The input is the aggregated rating scores and user reviews, and the output is a report for the company.

[0945] Through the above processing steps, the present invention realizes a system that automates document management and risk assessment within a factory, notifies users quickly, and provides appropriate feedback.

[0946] 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.

[0947] The present invention is embodied as a system including an acquisition means, a change detection means, a generation means, a summarization means, a notification means, a rating transmission means, a compilation means, a report generation means, and an emotion engine. The system aims to improve the user experience by enabling users to easily understand terms and conditions and contracts of online services and by recognizing the user's emotions and providing feedback.

[0948] Obtaining the terms and conditions

[0949] The server periodically accesses the service provider's website to retrieve the latest terms and conditions. This is done by sending an HTTP request and parsing the HTML content returned as a response. An HTML parsing library is used to extract the terms and conditions text.

[0950] Change Detection

[0951] The server compares the newly retrieved clause with the previously retrieved clause and uses a difference detection algorithm to identify any changes in the text.

[0952] Summary of changes and risk identification

[0953] The server inputs the text of the changes into the generation AI, which then summarizes them and identifies the risks. Specifically, natural language processing technology is used to summarize the changes in a way that is easy for users to understand, highlighting points that require particular attention and risks.

[0954] Notification of summary results

[0955] The server notifies the user of the generated summary results via web sockets or push notifications. Users can receive these notifications in real time via a smartphone app or web browser.

[0956] Evaluation of terms and conditions

[0957] The user checks the notification displayed on the device and reads the changes to the terms and conditions. The user can check the summary of the changes and the explicit description of the risks, and understand the contents.

[0958] User Emotion Recognition

[0959] When a user uses a device to evaluate the policy changes, the emotion engine recognizes emotions from the user's facial expressions, voice, and text input. This emotion data is recorded along with the user's evaluation.

[0960] Sending emotional data

[0961] The terminal transmits the user's evaluation data and emotion data to the server, which aggregates the emotion data along with the evaluation data.

[0962] Aggregation of rating and sentiment data

[0963] The server aggregates the evaluation data and emotion data sent by multiple users, thereby generating data that indicates the overall evaluation of the terms and conditions and the emotional state of the users.

[0964] Generate reports

[0965] The server generates a report for the company based on the aggregated evaluation and sentiment data, including suggestions for policy improvements and risk avoidance. The report is then emailed to the company's contact person or made available for review on a dedicated company dashboard.

[0966] Specific examples

[0967] For example, if the terms and conditions of a particular online education service are updated, the system automatically detects the changes and notifies the user by summarizing important changes and risks. When the user reviews and evaluates the changes, the emotion engine identifies the user's emotions and sends this data to the server. A report is then generated for the company based on the aggregated results, and the company is provided with improvement suggestions that include the user's emotional state. This allows users to use the education service with peace of mind and allows companies to receive more specific feedback.

[0968] The above is an embodiment of the present invention. By combining an emotion engine, it is possible to recognize the user's emotions and provide more personalized feedback, further improving the user experience.

[0969] The processing flow will be explained below.

[0970] Step 1:

[0971] The server accesses the service provider's website to retrieve the latest terms and conditions. This is done by sending an HTTP request and downloading the HTML content returned as a response. An HTML parsing library (e.g., BeautifulSoup) is used to extract the terms and conditions text from the retrieved HTML content.

[0972] Step 2:

[0973] The server compares the newly retrieved terms and conditions with the previously retrieved terms and conditions. It uses a difference detection algorithm to identify changes in words and sentences. Specifically, it compares the previous and current terms and conditions line by line or character by character to identify additions and deletions.

[0974] Step 3:

[0975] The server inputs the changes into the generation AI, which then summarizes the changes and identifies the risks. For example, it can use OpenAI's API to analyze the detected changes and summarize them in a way that is easy for users to understand, highlighting points to be aware of and risks.

[0976] Step 4:

[0977] The server notifies the generated summary results to the user's device using web socket and push notification technologies, and the user can receive the notification in real time via a smartphone app or web browser.

[0978] Step 5:

[0979] The user checks the notification displayed on their device and reads the changes to the terms and conditions. They then view the generated summary and risk information to understand the content. During this process, the emotion engine monitors the user's facial expressions and voice to recognize their emotions.

[0980] Step 6:

[0981] Users use their devices to evaluate the changes to the terms and conditions. Evaluations are made through an evaluation form, where users enter a score from 1 to 5 and any additional comments. At the same time, an emotion engine analyzes the user's emotions in real time and generates emotion data.

[0982] Step 7:

[0983] The device transmits user evaluation data and emotion data to the server. The transmitted data includes evaluation scores, comments, and emotion recognition data. The server then aggregates the data.

[0984] Step 8:

[0985] The server aggregates the evaluation data and emotional data sent by multiple users. This generates data showing the overall evaluation of the terms and conditions and the emotional state of the users. The aggregated results are displayed on a dashboard.

[0986] Step 9:

[0987] The server generates a report for the company based on the aggregated evaluation and sentiment data, including suggestions for improving the terms and conditions and avoiding risks. The report is then emailed to the company's representative or made available for review on a dedicated company dashboard.

[0988] Example 2

[0989] 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."

[0990] In recent years, with the spread of online services, users are increasingly required to understand complex and lengthy terms and conditions and contracts. However, these documents contain many technical and legal terms, making them difficult for average users to understand. Furthermore, frequently changing terms and conditions place a significant burden on users, who must constantly review them and recognize risks. Furthermore, it is not easy for companies to properly understand user sentiment and feedback and use it to improve terms and conditions. Therefore, there is a need for a system that can automatically detect and summarize changes in terms and conditions, indicate risks, and recognize user sentiment to improve the user experience.

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

[0992] In this invention, the server includes an acquisition means, a change detection means, a generation means, a summarization means, a notification means, an evaluation transmission means, a compilation means, a report generation means, and an emotion recognition means, which enables the following:

[0993] 1. The server periodically retrieves the latest terms and conditions from the service provider's website and compares them with the previous terms and conditions to identify any changes.

[0994] 2. Use a generative AI model to analyze the identified changes and summarize the risks, providing information in a way that is easy for users to understand.

[0995] 3. Notify the user of the summary results in real time and collect user evaluation data and sentiment data.

[0996] 4. Generate reports for companies based on the collected evaluation and sentiment data and provide specific improvement suggestions.

[0997] This allows users to quickly respond to complex changes in terms and conditions and understand the risks, while also allowing companies to obtain useful feedback to improve their terms and conditions based on user sentiment and evaluations, thereby improving the user experience.

[0998] "Acquisition means" refers to a means that has the function of periodically acquiring the latest terms and conditions from the service provider's website.

[0999] The "change detection means" is a means having a function of comparing the previously acquired terms and conditions with the latest terms and conditions and identifying the changed parts.

[1000] "Generative means" means a means having the function of analyzing the changes identified using a generative AI model and summarizing the risks.

[1001] The "summarization means" is a means having a function of recording and storing the generated summary results and risk information.

[1002] The "notification means" is a means having a function of notifying the generated summary result to the user's terminal in real time.

[1003] The "evaluation sending means" is a means having a function of sending data evaluated by the user to the server.

[1004] The "aggregation means" is a means having a function of aggregating evaluation data and emotion data sent from a plurality of users and generating statistical information.

[1005] The "report generation means" is a means having a function of creating a report for a company based on the collected evaluation data and emotion data.

[1006] The "emotion recognition means" is a means having a function of recognizing emotions from the user's facial expressions, voice, and text input, and recording the emotion data.

[1007] A "generative AI model" is a model that uses natural language processing technology to summarize and identify risks based on input text.

[1008] A "prompt" is text used as input to a generative AI model, and is an instruction used to summarize and clarify risks.

[1009] The present invention is embodied as a system including an acquisition means, a change detection means, a generation means, a summarization means, a notification means, a rating transmission means, a compilation means, a report generation means, and an emotion recognition means, which not only makes it easier for users to understand terms and conditions and contracts of online services, but also recognizes the user's emotions and provides appropriate feedback to improve the user experience.

[1010] Obtaining the terms and conditions

[1011] The server periodically accesses the service provider's website to retrieve the latest terms and conditions. This process is performed by sending an HTTP request and analyzing the HTML content received as a response. Specifically, the "requests" library is used to send the HTTP request, and the "BeautifulSoup" library is used to parse the HTML content and retrieve the text of the terms and conditions.

[1012] Change Detection

[1013] The server compares the newly acquired terms and conditions with the previously acquired terms and conditions. The comparison is performed using a difference detection algorithm such as DiffMatchPatch to identify changes. This makes it possible to clearly identify important changes for the user.

[1014] Summary of changes and risk identification

[1015] The server inputs the text of the changes as a prompt to the generative AI model (e.g., OpenAI's GPT-3), summarizing the changes and highlighting the risks. Specific examples of prompts include:

[1016] To help users understand the latest changes, please summarize the following text and highlight any important caveats or risks:

[1017] [Insert text of change to terms here]

[1018] The generative AI model provides a concise summary of the revised terms and conditions, highlighting any risks that require particular attention.

[1019] Notification of summary results

[1020] The server notifies the user of the generated summary results via web sockets or push notifications, allowing the user to receive important change information in real time via a smartphone app or web browser.

[1021] Evaluation of terms and conditions

[1022] The user checks the notification on their device and views the changes to the terms and conditions. They then review a summary of the changes and a statement of the risks, and understand the details. This allows the user to quickly grasp important changes.

[1023] User Emotion Recognition

[1024] When a user uses the device to evaluate the policy changes, the device uses emotion recognition means to capture the user's facial expressions and voice to recognize their emotions, for example, by using the OpenCV library or Microsoft Azure Cognitive Services to recognize and record the user's emotions in real time.

[1025] Sending emotional data

[1026] The device sends the user's rating and emotion data to the server by sending an HTTP POST request using the "requests" library.

[1027] Aggregation of evaluation data and emotion data

[1028] The server collects and aggregates the evaluation and emotion data sent by multiple users into a database. Specifically, it uses the Pandas library to analyze the evaluation and emotion data and generate statistical information.

[1029] Generate reports

[1030] The server generates a report for the company based on the aggregated evaluation data and sentiment data. This report includes suggestions for improving terms and conditions and avoiding risks. The generated report is either sent to the company's personnel via email or provided for viewing on a dedicated corporate dashboard. Specifically, the report is created in HTML format using the "Jinja2" template engine, and emailed using the "smtplib" library. The corporate dashboard is also built using "Flask" as a web framework.

[1031] Specific examples

[1032] For example, if the terms and conditions of a particular online education service are updated, the system automatically detects the changes and notifies the user by summarizing important changes and risks. When the user reviews and evaluates the changes, the emotion engine identifies the user's emotions and sends this data to the server. The server generates a report for the company based on the collected data, and provides the company with improvement suggestions that include the user's emotional state. This allows users to use the education service with peace of mind and allows companies to receive specific feedback.

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

[1034] Step 1:

[1035] The server accesses the service provider's website at 2:00 AM every day to retrieve the latest terms and conditions. The input for this process is the website URL, and an HTTP request is sent to retrieve the HTML content. The HTTP request is sent using the "requests" library, and the HTML content received as a response is analyzed using the "BeautifulSoup" library to extract the terms and conditions text. As an output, the extracted terms and conditions text is saved in a database.

[1036] Step 2:

[1037] The server retrieves the newly acquired clause text and the previously acquired clause text from the database. The input to this process is the two clause texts retrieved from the database. Next, a difference detection algorithm such as DiffMatchPatch is used to identify the changes in the text. As an output, the server generates the text of the identified changes and stores it in the database.

[1038] Step 3:

[1039] The server inputs the text of the change to the generative AI model as a prompt. The input for this process is the text of the change. Here is an example of a prompt sentence to input to the generative AI model:

[1040] To help users understand the latest changes, please summarize the following text and highlight any important caveats or risks:

[1041] [Insert text of change to terms here]

[1042] The generative AI model analyzes this input, produces a summary and identifies risks, and stores the resulting summary and risk information in a database as output.

[1043] Step 4:

[1044] The server notifies the user's device of the generated summary results. The input for this process is the summary results and risk information. Web sockets and push notifications are used as notification methods. The server uses the "WebSocket" library and "Firebase Cloud Messaging" to send notifications to the user's device in real time. As an output, notifications are displayed on the user's device.

[1045] Step 5:

[1046] The user checks the notification displayed on their device and views the changes to the terms and conditions. The input to this process is a summary of the terms and conditions received as a notification. The user then views this information using a smartphone app or web browser to understand the content. The output is a deeper understanding of the user.

[1047] Step 6:

[1048] When a user uses a device to evaluate changes to the terms and conditions, the device captures the user's facial expressions and voice using a camera and microphone, and analyzes them using emotion recognition. The input for this process is the text to be evaluated and the captured user's facial expressions and voice data. The device recognizes emotions and records the emotion data using OpenCV and Microsoft Azure Cognitive Services. As an output, emotion data is generated and recorded.

[1049] Step 7:

[1050] The device sends the user's rating data and emotion data to the server. The input of this process is the rating data and emotion data. The device uses the "requests" library to send an HTTP POST request to the server's API endpoint, and the data is sent to the server as output.

[1051] Step 8:

[1052] The server imports rating data and emotion data submitted by multiple users into a database and aggregates them. The input for this process is the rating data and emotion data stored in the database. The "Pandas" library is used to analyze the data and generate statistical information. The aggregated results are generated as the output.

[1053] Step 9:

[1054] The server generates a report for the company based on the aggregated evaluation data and sentiment data. The input for this process is the aggregated data. The report is created in HTML format using the "Jinja2" template engine and sent by email using the "smtplib" library. In addition, a dashboard for the company is provided using "Flask" as a web framework. The generated report is provided to the company's personnel as an output.

[1055] (Application example 2)

[1056] 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."

[1057] Changes to terms of service are frequent in modern online services, making it difficult for users to keep up with them in a timely and accurate manner. Understanding user sentiment regarding changes to terms of service provides important feedback for service providers, but a system for efficiently collecting and analyzing this information has yet to be established. Furthermore, there is a lack of a system for clearly communicating the risks and importance of changes to users, which often results in a poor user experience.

[1058] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a generating means, an acquiring means, a change detecting means, a summarizing means, a notifying means, an emotion recognizing means, an evaluation sending means, a tabulating means, and a report generating means. This makes it possible to quickly detect changes in the terms of use, summarize the risks, and notify the user, and further collect user emotion data, aggregate this data, and provide feedback to the company.

[1059] The "generation means" is a means for analyzing the identified changes and summarizing them in a form that is easy for the user to understand.

[1060] "Means for Acquisition" means a means for periodically obtaining the latest terms and conditions from the website of the online service provider.

[1061] The "change detection means" is a means for comparing the previously acquired terms and conditions with the latest terms and conditions to identify any changes.

[1062] The "summarization means" is a means for summarizing important points and risks of the changes identified by the change detection means.

[1063] The "notification means" is a means for notifying the user of the generated summary result.

[1064] An "emotion recognition means" is a means for recognizing emotions from a user's facial expressions, voice, text input, etc., and acquiring that data.

[1065] The "evaluation sending means" is a means for sending the data rated by the user and the emotion data acquired by the emotion recognition means to the server.

[1066] The "aggregation means" is a means for aggregating the evaluation data and emotion data sent from a plurality of users.

[1067] The "report generation means" is a means for generating a report for a company based on the collected evaluation data and emotion data.

[1068] A system for carrying out the present invention includes the following means:

[1069] 1. Acquisition method:

[1070] The server periodically retrieves the latest terms and conditions from the online service provider's website by sending an HTTP request and parsing the HTML content returned as a response. Specifically, it uses an HTML parsing library such as BeautifulSoup to extract the terms and conditions text.

[1071] 2. Change detection methods:

[1072] The server compares the previously retrieved contract with the newly retrieved contract, using a difference detection algorithm such as difflib to detect text differences and identify changes.

[1073] 3. Summary tools:

[1074] The server uses a generative AI model to summarize the risks of the changes. It generates prompt sentences, inputs them into the generative AI model, and summarizes the changes using natural language processing technology.

[1075] 4. Means of notification:

[1076] The generated summary results are sent to the user's device (smartphone app or web browser) via web socket or push notification, allowing the user to receive real-time updates.

[1077] 5. Emotion recognition means:

[1078] As users evaluate the policy changes, the device recognizes their emotions from their facial expressions, voice, and text input. This emotional data is obtained using an emotion recognition engine such as EmotionEngine.

[1079] 6. Evaluation submission method:

[1080] The device transmits user rating and emotion data to the server, which is then aggregated.

[1081] 7. Aggregation method:

[1082] The server aggregates the evaluation data and emotional data sent by multiple users, thereby generating data that indicates the overall evaluation of the terms and conditions and the emotional state of the users.

[1083] 8. Report Generation Methods:

[1084] The server generates reports for companies based on the aggregated evaluation and sentiment data, including recommendations for policy improvements and risk avoidance, which are emailed to company representatives or made available for review on a dedicated company dashboard.

[1085] Specific examples

[1086] For example, if the terms and conditions of a particular online education service are updated, the system automatically detects the changes and notifies the user by summarizing important changes and risks. When the user reviews and evaluates the changes, the emotion engine identifies the user's emotions and sends this data to the server. A report is then generated for the company based on the aggregated results, and the company is provided with improvement suggestions that take into account the user's emotional state. This allows users to use the service with peace of mind and allows companies to receive more specific feedback.

[1087] Prompt Sentence Examples

[1088] Changes:

[1089] The usage fee will increase from 3% to 5%.

[1090] Please summarize."

[1091] Notification summary and risk statement:

[1092] "

[1093] notification:

[1094] Latest Terms and Conditions Changes:

[1095] The service fee has increased from 3% to 5%, a change that may directly affect the payment costs of users.

[1096] "

[1097] In this way, by using the system of the present invention, it is possible to quickly grasp changes to the terms of use and the risks involved, and also to provide feedback that reflects the user's feelings.

[1098] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1099] Step 1:

[1100] The server retrieves the latest terms and conditions from the online service provider's website by sending an HTTP request and parsing the HTML content returned as a response. A library such as BeautifulSoup is used for HTML parsing, and the latest terms and conditions text is extracted.

[1101] Input: Service provider website URL

[1102] Output: Latest extracted clause text

[1103] Step 2:

[1104] The server compares the previously retrieved clause with the latest clause to identify any changes. This comparison uses a difference detection algorithm such as difflib, and a change detection means detects the differences in the text.

[1105] Input: Latest clause text, previously retrieved clause text

[1106] Output: List of changes

[1107] Step 3:

[1108] The server uses a generative AI model to summarize the risks of the changes. A prompt is input into the generative AI model, which then uses natural language processing technology to summarize the changes. Based on the input prompt, the generative AI outputs text summarizing the important points and risks of the changes.

[1109] Input: list of changes, prompt

[1110] Output: Summarized risk text

[1111] Step 4:

[1112] The server then notifies the user of the generated summary results via web sockets or push notifications, which are sent in real time to the user's smartphone app or web browser.

[1113] Input: Abstracted risk text

[1114] Output: Notification to user's device

[1115] Step 5:

[1116] The user reviews and evaluates the changes to the terms and conditions. In addition, the user's device recognizes emotions from the user's facial expressions, voice, and text input. This emotion data is obtained using an emotion recognition engine such as EmotionEngine.

[1117] Input: User facial expressions, voice, and text input

[1118] Output: Emotion data, rating data

[1119] Step 6:

[1120] The terminal transmits the user's evaluation data and emotion data to the server via the evaluation transmitting means.

[1121] Input: Emotion data, rating data

[1122] Output: Send data to the server

[1123] Step 7:

[1124] The server aggregates the evaluation data and emotional data sent by multiple users, thereby generating data that indicates the overall evaluation of the terms and conditions and the emotional state of the users.

[1125] Input: Rating data and sentiment data from multiple users

[1126] Output: Aggregated rating data, sentiment data

[1127] Step 8:

[1128] The server generates reports for companies based on the aggregated evaluation and sentiment data, including recommendations for policy improvements and risk avoidance, which are emailed to company representatives or made available for review on a dedicated company dashboard.

[1129] Input: Aggregated rating data, sentiment data

[1130] Output: Corporate reports

[1131] This allows users to use the service with peace of mind and allows companies to receive more specific feedback.

[1132] 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.

[1133] 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.

[1134] 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.

[1135] [Fourth embodiment]

[1136] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1137] 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.

[1138] 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).

[1139] 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.

[1140] 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.

[1141] 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).

[1142] 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.

[1143] 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.

[1144] 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.

[1145] 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.

[1146] 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.

[1147] 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.

[1148] 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."

[1149] The present invention is implemented as a system including an acquisition means, a change detection means, a generation means, a summarization means, a notification means, an evaluation sending means, a compilation means, and a report generation means. The purpose of this system is to enable users to use services with peace of mind by clearly explaining the contents of the terms and conditions and contracts of services provided on the Web, identifying changes, and clearly indicating risks.

[1150] Obtaining the terms and conditions

[1151] The server periodically accesses the service provider's website to retrieve the latest terms and conditions. This is done by first sending a request and downloading the HTML content, then using an HTML parsing library (e.g. BeautifulSoup) to extract the text portion of the terms and conditions.

[1152] Change Detection

[1153] The server compares the newly acquired terms and conditions with the previously acquired terms and conditions. As a result of this comparison, any changes are identified. A difference detection algorithm is used to extract the specific details of the changes.

[1154] Summary of changes and risk identification

[1155] The server inputs the identified changes into a generation AI, which analyzes them and summarizes them and indicates the risks. For example, by using natural language processing technology such as OpenAI, the changes can be summarized in a form that is easy for users to understand, and risks and points of caution can be highlighted.

[1156] Notification of summary results

[1157] The server then sends the generated summary to the user's device, using web sockets and push notifications to quickly notify the user of changes. Users can receive notifications via a smartphone app or web interface.

[1158] Evaluation of terms and conditions

[1159] The user can use their device to check the notification and evaluate the changes and the entire terms and conditions. This evaluation is done through a dedicated evaluation form, and the evaluation data is sent from the device to the server.

[1160] Aggregation of evaluation data

[1161] The server aggregates the evaluation data sent by users, and calculates the overall evaluation of the terms and conditions and user satisfaction level based on the evaluations collected from multiple users, and displays them as a score.

[1162] Generate reports

[1163] The server generates reports for companies based on the aggregated rating data and user reviews, including suggestions for improvement and risk mitigation, and can be sent to the company via email or made available on a dedicated company dashboard.

[1164] Specific examples

[1165] For example, if the terms and conditions of a particular online shopping site are updated, the system will automatically detect the updates and notify users by summarizing important changes and risks. When users review the summary and provide a rating, the rating is shared with other users, and a report including suggestions for improvement is generated for the company. This allows users to use the shopping site with peace of mind, and companies can receive feedback to improve customer satisfaction.

[1166] The above is an embodiment of the present invention. By using this system, users can easily understand complex terms and conditions and contracts, realizing an environment in which they can use online services with greater peace of mind.

[1167] The processing flow will be explained below.

[1168] Step 1:

[1169] The server accesses the service provider's website to retrieve the latest terms and conditions. This is done by sending an HTTP request and downloading the HTML content returned as a response. The terms and conditions text is extracted from the retrieved HTML content using an HTML parsing library such as BeautifulSoup.

[1170] Step 2:

[1171] The server compares the most recent version of the terms and conditions with the previous version. It uses a difference detection algorithm to identify changes in the text. Specifically, it compares the previous and current terms and conditions line by line and character by character to identify additions and deletions.

[1172] Step 3:

[1173] The server inputs the text of the changes into the generating AI, which then summarizes them and indicates the risks. For example, by using OpenAI's API, the server can summarize the detected changes in an easy-to-understand format, highlighting points that require particular attention and risks.

[1174] Step 4:

[1175] The server notifies the user of the generated summary results via web sockets or push notifications. Users can receive these notifications in real time via a smartphone app or web browser.

[1176] Step 5:

[1177] The user checks the notification displayed on the device and reads the changes to the terms and conditions. The user can check the summary of the changes and the explicit description of the risks, and understand the contents.

[1178] Step 6:

[1179] Users use their devices to evaluate the changes to the terms and conditions. Evaluations are conducted through a dedicated form, and users rate the reliability and comprehension of the changes on a scale of 1 to 5.

[1180] Step 7:

[1181] The terminal transmits the user's evaluation data, including the user's score and additional comments, to the server, where the transmitted evaluation data is stored and aggregated.

[1182] Step 8:

[1183] The server aggregates the evaluation data sent by multiple users and calculates an overall evaluation score. The aggregated results are compiled as data showing the evaluation of the service and the level of understanding of the terms and conditions, and are displayed on a dashboard.

[1184] Step 9:

[1185] The server generates a report for the company based on the aggregated rating data and user reviews. This report includes suggestions for improving the terms and conditions and avoiding risks. The report is either emailed to the company's representative or made available for review on a dedicated company dashboard.

[1186] The above is the specific processing flow in this system.

[1187] Example 1

[1188] 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."

[1189] Currently, terms and conditions and contracts for services provided online are extremely complex, making them difficult for users to understand, and even when changes are made, users often go unnoticed. As a result, users may overlook important risks or changes and fail to take appropriate action. It is also difficult for companies to efficiently collect feedback from users, improve satisfaction, and avoid risks. Therefore, there is a need for a system that allows users to easily understand changes and risks in terms and conditions and provide feedback through evaluations.

[1190] 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.

[1191] In this invention, the server includes an acquisition means, a change detection means, a generation means, a summarization means, a notification means, an evaluation transmission means, a compilation means, and a report generation means. This makes it possible to acquire the latest documents from the information provider's website, analyze their contents, summarize changes and risks, and notify users. Furthermore, users can evaluate the changes, and the evaluation data can be compiled to provide proposals to companies. This system allows users to easily understand important changes and risks, and companies can receive feedback efficiently.

[1192] The "acquisition means" is a function for acquiring the latest documents from the information provider's website.

[1193] The "change detection means" is a function for comparing a newly acquired document with a previously acquired document to identify changes.

[1194] The "generation means" is a function for analyzing the identified changes and generating summaries and risk assessments.

[1195] The "summarization means" is a function for creating a summary in a form that is easy for the user to understand, based on the analysis results of the changes.

[1196] The "notification means" is a function for promptly notifying the user of the generated summary result.

[1197] The "evaluation sending means" is a function that allows a user to input an evaluation and send the evaluation data to the server.

[1198] The "aggregation means" is a function for aggregating evaluation data sent from multiple users and calculating an overall evaluation and user satisfaction level.

[1199] The "report generation means" is a function for generating reports for companies from the collected evaluation data and user reviews.

[1200] The present invention is implemented as a system including an acquisition means, a change detection means, a generation means, a summarization means, a notification means, an evaluation transmission means, a compilation means, and a report generation means. The main purpose of this system is to provide the contents of documents (e.g., terms and conditions and contracts) for services provided over the Internet in a format that is easy for users to understand, and to clearly indicate the risks of changes, so that users can use the services with peace of mind.

[1201] System Configuration

[1202] The system includes the following main means:

[1203] Acquisition means

[1204] The server periodically accesses the information provider's website to retrieve the latest documents, sending an HTTP request to download the HTML content and extracting the text portion of the document using an HTML parsing library such as BeautifulSoup.

[1205] Change detection method

[1206] The server compares the text of the newly retrieved document with that of the previously retrieved document, using a difference detection algorithm (e.g., difflib) to identify the specific changes.

[1207] Generators and Summarizers

[1208] The server inputs the identified changes into the generative AI model using prompt sentences, and generates a summary in a form that is easy for the user to understand based on the summary and risk assessment output by the generative AI model.

[1209] Example prompt: "Please summarize the updates in the latest policy and clarify the risks."

[1210] Example prompt: "Identify and summarize the changes in your service provider's terms and conditions."

[1211] Notification means

[1212] The server notifies the user of the generated summary results using a web socket or push notification service (e.g., Firebase Cloud Messaging) to deliver the information quickly.

[1213] Evaluation sending method

[1214] The user checks the notification on the device and evaluates the changes. Evaluations are made using a dedicated evaluation form, and the evaluation data is sent from the device to the server.

[1215] Aggregation method

[1216] The server aggregates the evaluation data sent by multiple users, and calculates the overall evaluation of the document and the user satisfaction level, which are then displayed as a score.

[1217] Report Generation Method

[1218] The server generates reports for companies based on the aggregated rating data and user reviews, including recommendations for improvement and risk prevention measures, which companies can receive via email or view through a dedicated dashboard.

[1219] Specific examples

[1220] For example, if the terms and conditions of a particular online shopping site are updated, the server automatically detects the updates and notifies users by summarizing important changes and risks. When users review the summary and provide a rating, the rating is shared with other users, and a report including suggestions for improvement is generated for the company. This allows users to use the shopping site with peace of mind, and companies can quickly obtain feedback to improve customer satisfaction.

[1221] As described above, by using the system of the present invention, users can easily understand complex documents, and companies can be provided with an environment in which they can efficiently collect user feedback.

[1222] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1223] Step 1:

[1224] The server periodically accesses the information provider's website to retrieve the latest documents. First, the server sends an HTTP request to a specified URL using a Cron job. It saves the received HTML content and parses the HTML using BeautifulSoup to extract the text portion of the document from specific tags and classes.

[1225] Input: URL of the informant's website, text of the previously obtained document

[1226] Output: Text of the most recent document

[1227] Step 2:

[1228] The server compares the newly retrieved document with the previously retrieved document. First, it reads both texts and uses a difference detection algorithm (e.g., difflib) to identify the changes. It then lists the changes detected as differences and prepares for the next step.

[1229] Input: Text of the latest document, text of the previously retrieved document

[1230] Output: List of changes

[1231] Step 3:

[1232] The server then formats the identified changes into prompts to be input to the generative AI model, including instructions for inserting the text of the changes and generating a summary and risk statement.

[1233] Input: List of changes

[1234] Output: prompt statement

[1235] Step 4:

[1236] The server sends the prompt to the generative AI model and obtains a summary and risk assessment. Specifically, it calls the generative AI model's API, inputs the prompt, and receives the analysis results, which include a summary of the changes and a risk assessment.

[1237] Input: prompt statement

[1238] Output: Summary results and risk assessment

[1239] Step 5:

[1240] The server notifies the user of the generated summary results. Specifically, it uses a web socket or push notification service (e.g., Firebase Cloud Messaging) to send the summary results to each user's device.

[1241] Input: Summary results and risk assessment

[1242] Output: Notification to the user's device

[1243] Step 6:

[1244] Users receive a notification on their device to confirm the changes, and can view the notification content through an application or web interface and fill out an evaluation form.

[1245] Input: Notification received on the user's device

[1246] Output: Data entered into the evaluation form

[1247] Step 7:

[1248] The user inputs evaluation data and transmits it to the server using the evaluation transmission means, and the transmitted evaluation data is stored in the server's database.

[1249] Input: Data entered into the evaluation form

[1250] Output: Rating data sent to the server

[1251] Step 8:

[1252] The server aggregates the evaluation data sent by multiple users and calculates the overall evaluation and user satisfaction. The aggregation script queries the evaluation data and generates statistical information.

[1253] Input: Multiple evaluation data

[1254] Output: Overall rating score and user satisfaction

[1255] Step 9:

[1256] The server generates reports for the company based on the aggregated data. The report generation script uses the statistical information to create reports in PDF and HTML format, including recommendations and risk management measures for the company.

[1257] Input: Overall rating score and user satisfaction

[1258] Output: Corporate reports

[1259] Step 10:

[1260] The server will send the generated report to the designated corporate contact or make it available for viewing in the corporate dashboard. It will use the email sending library to send the report or update the dashboard link.

[1261] Input: Corporate Report

[1262] Output: Send report to company or update dashboard

[1263] (Application example 1)

[1264] 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."

[1265] Documents used in factories, such as safety manuals and work procedures, are frequently updated. This makes it difficult to identify changes and identify risks, requiring workers to always understand the latest information. However, doing this manually is labor-intensive and can lead to misunderstandings and missed communications. To solve this problem, a system is needed that automatically detects changes in documents, summarizes them, identifies risks, and promptly notifies users.

[1266] 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.

[1267] In this invention, the server includes a means for periodically acquiring documents, a means for analyzing and summarizing changes in documents based on a generative AI model, and a means for notifying users in the factory of the summary results. This makes it possible to automatically identify changes in documents used in the factory, summarize risks, and promptly notify them.

[1268] The "acquisition means" is a means for periodically acquiring the latest documents from the document management system within the factory.

[1269] The "change detection means" is a means for comparing the previously acquired document with the latest document and identifying the changes.

[1270] The "generative means" is a means for analyzing the identified changes based on a generative AI model and summarizing the risks.

[1271] The "summarization means" is a means for summarizing the content of the changes in a form that is easy for the user to understand.

[1272] The "notification means" is a means for quickly notifying users in the factory of the generated summary results.

[1273] The "evaluation sending means" is a means for sending the data evaluated by the user to the server.

[1274] The "counting means" is a means for counting evaluation data sent from users.

[1275] The "report generation means" is a means for generating a report for a company based on the collected evaluation data.

[1276] This invention provides a system that manages documents such as safety manuals and work procedures used in factories, identifies changes, summarizes them, and indicates risks. This system periodically retrieves documents from the document management system, analyzes and summarizes the changes based on a generative AI model, and promptly notifies the user.

[1277] Hardware and software used

[1278] Hardware:

[1279] Server: For example, a cloud server such as Amazon EC2

[1280] User device: smartphone, tablet, or factory workstation

[1281] Factory robots: Robots that physically retrieve documents (if necessary)

[1282] software:

[1283] requests: A Python library for HTTP requests

[1284] ftplib: A Python library for FTP access

[1285] difflib: A Python library for diff detection algorithms

[1286] OpenAI GPT-3: Natural Language Processing with Generative AI Models

[1287] Firebase Cloud Messaging: a service for push notifications

[1288] React: A front-end framework for rating forms

[1289] SendGrid: a service for sending emails

[1290] The working mechanism of the invention

[1291] The server periodically accesses the document management system to retrieve the latest documents, using HTTP requests or FTP. For example, it uses the requests library to retrieve data from the API, or the ftplib library to download files from the FTP server.

[1292] The retrieved document is compared with the previous document to identify the changes. The difference detection is done using the Python difflib library, which effectively detects the differences between two pieces of text.

[1293] The identified changes are fed into a generative AI model (e.g., OpenAI's GPT-3) to summarize and identify risks. The generative AI model analyzes the input text based on a prompt and summarizes it in an understandable format. Here is an example prompt:

[1294] "Please summarize the changes in the following documents and highlight any new risk points:

[1295] "Old version: The operation buttons are green. New version: The operation buttons are red. This change is to prevent operation errors."

[1296] The generated summary results are pushed to the user's device using Firebase Cloud Messaging, and the user receives the notification on their smartphone or work device and can check the contents.

[1297] After checking the notification content, the user rates the app. The rating data is sent to the server via a rating form using React. The rating sending means passes this rating data to the aggregation means.

[1298] The server aggregates the submitted evaluation data and generates a report for the company based on the evaluation results. This report includes an overall evaluation and recommendations for the document. The report can be sent to the company via email using SendGrid or made available for viewing on the company's dashboard.

[1299] For example, when a factory's safety manual is updated, new procedures and risk points may be added. The system automatically detects these changes, summarizes them, and clearly indicates the risks. For example, a notification may be issued saying, "Due to the new procedures, the color of the operation buttons has changed, and workers need to be careful to prevent incorrect operation." Users can quickly understand this information and provide evaluation feedback to the server. This improves safety and work efficiency throughout the factory.

[1300] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1301] Step 1:

[1302] The server periodically accesses the document management system to obtain the latest documents. Specifically, it downloads the documents using HTTP requests or FTP. The input is data from the document management system, and the output is the latest documents stored in the server.

[1303] Step 2:

[1304] The server compares the latest retrieved document with the previous one, using Python's difflib library to find the differences between the text. The input is both the previous and latest document, and the output is a list of changes.

[1305] Step 3:

[1306] The server analyzes and summarizes the identified changes based on a generative AI model (e.g., OpenAI GPT-3). It uses prompts to instruct the AI ​​to specify the changes and risks. The input is the text of the changes, and the output is a text containing a summary and a description of the risks. Example prompts:

[1307] "Please summarize the changes in the following documents and highlight any new risk points:

[1308] "Old version: The operation buttons are green. New version: The operation buttons are red. This change is to prevent operation errors."

[1309] Step 4:

[1310] The server sends the generated summary results to the user's device using Firebase Cloud Messaging. The input is the text of the summary results, and the output is a push notification to the user's device. The user receives the notification content on their smartphone or work device.

[1311] Step 5:

[1312] The user checks the notification content and rates it using their workstation. The rating data is sent to the server through a dedicated rating form (e.g., a React-based web form). The input is the user's rating data, and the output is the rating data sent to the server.

[1313] Step 6:

[1314] The server aggregates the submitted rating data. It collects ratings from multiple users and calculates a rating score. The input is the user rating data, and the output is the aggregated rating score.

[1315] Step 7:

[1316] The server generates reports for companies based on the aggregated rating scores. The reports are sent to companies via email using SendGrid or displayed on a company dashboard. The input is the aggregated rating scores and user reviews, and the output is a report for the company.

[1317] Through the above processing steps, the present invention realizes a system that automates document management and risk assessment within a factory, notifies users quickly, and provides appropriate feedback.

[1318] 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.

[1319] The present invention is embodied as a system including an acquisition means, a change detection means, a generation means, a summarization means, a notification means, a rating transmission means, a compilation means, a report generation means, and an emotion engine. The system aims to improve the user experience by enabling users to easily understand terms and conditions and contracts of online services and by recognizing the user's emotions and providing feedback.

[1320] Obtaining the terms and conditions

[1321] The server periodically accesses the service provider's website to retrieve the latest terms and conditions. This is done by sending an HTTP request and parsing the HTML content returned as a response. An HTML parsing library is used to extract the terms and conditions text.

[1322] Change Detection

[1323] The server compares the newly retrieved clause with the previously retrieved clause and uses a difference detection algorithm to identify any changes in the text.

[1324] Summary of changes and risk identification

[1325] The server inputs the text of the changes into the generation AI, which then summarizes them and identifies the risks. Specifically, natural language processing technology is used to summarize the changes in a way that is easy for users to understand, highlighting points that require particular attention and risks.

[1326] Notification of summary results

[1327] The server notifies the user of the generated summary results via web sockets or push notifications. Users can receive these notifications in real time via a smartphone app or web browser.

[1328] Evaluation of terms and conditions

[1329] The user checks the notification displayed on the device and reads the changes to the terms and conditions. The user can check the summary of the changes and the explicit description of the risks, and understand the contents.

[1330] User Emotion Recognition

[1331] When a user uses a device to evaluate the policy changes, the emotion engine recognizes emotions from the user's facial expressions, voice, and text input. This emotion data is recorded along with the user's evaluation.

[1332] Sending emotional data

[1333] The terminal transmits the user's evaluation data and emotion data to the server, which aggregates the emotion data along with the evaluation data.

[1334] Aggregation of rating and sentiment data

[1335] The server aggregates the evaluation data and emotion data sent by multiple users, thereby generating data that indicates the overall evaluation of the terms and conditions and the emotional state of the users.

[1336] Generate reports

[1337] The server generates a report for the company based on the aggregated evaluation and sentiment data, including suggestions for policy improvements and risk avoidance. The report is then emailed to the company's contact person or made available for review on a dedicated company dashboard.

[1338] Specific examples

[1339] For example, if the terms and conditions of a particular online education service are updated, the system automatically detects the changes and notifies the user by summarizing important changes and risks. When the user reviews and evaluates the changes, the emotion engine identifies the user's emotions and sends this data to the server. A report is then generated for the company based on the aggregated results, and the company is provided with improvement suggestions that include the user's emotional state. This allows users to use the education service with peace of mind and allows companies to receive more specific feedback.

[1340] The above is an embodiment of the present invention. By combining an emotion engine, it is possible to recognize the user's emotions and provide more personalized feedback, further improving the user experience.

[1341] The processing flow will be explained below.

[1342] Step 1:

[1343] The server accesses the service provider's website to retrieve the latest terms and conditions. This is done by sending an HTTP request and downloading the HTML content returned as a response. An HTML parsing library (e.g., BeautifulSoup) is used to extract the terms and conditions text from the retrieved HTML content.

[1344] Step 2:

[1345] The server compares the newly retrieved terms and conditions with the previously retrieved terms and conditions. It uses a difference detection algorithm to identify changes in words and sentences. Specifically, it compares the previous and current terms and conditions line by line or character by character to identify additions and deletions.

[1346] Step 3:

[1347] The server inputs the changes into the generation AI, which then summarizes the changes and identifies the risks. For example, it can use OpenAI's API to analyze the detected changes and summarize them in a way that is easy for users to understand, highlighting points to be aware of and risks.

[1348] Step 4:

[1349] The server notifies the generated summary results to the user's device using web socket and push notification technologies, and the user can receive the notification in real time via a smartphone app or web browser.

[1350] Step 5:

[1351] The user checks the notification displayed on their device and reads the changes to the terms and conditions. They then view the generated summary and risk information to understand the content. During this process, the emotion engine monitors the user's facial expressions and voice to recognize their emotions.

[1352] Step 6:

[1353] Users use their devices to evaluate the changes to the terms and conditions. Evaluations are made through an evaluation form, where users enter a score from 1 to 5 and any additional comments. At the same time, an emotion engine analyzes the user's emotions in real time and generates emotion data.

[1354] Step 7:

[1355] The device transmits user evaluation data and emotion data to the server. The transmitted data includes evaluation scores, comments, and emotion recognition data. The server then aggregates the data.

[1356] Step 8:

[1357] The server aggregates the evaluation data and emotional data sent by multiple users. This generates data showing the overall evaluation of the terms and conditions and the emotional state of the users. The aggregated results are displayed on a dashboard.

[1358] Step 9:

[1359] The server generates a report for the company based on the aggregated evaluation and sentiment data, including suggestions for improving the terms and conditions and avoiding risks. The report is then emailed to the company's representative or made available for review on a dedicated company dashboard.

[1360] Example 2

[1361] 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."

[1362] In recent years, with the spread of online services, users are increasingly required to understand complex and lengthy terms and conditions and contracts. However, these documents contain many technical and legal terms, making them difficult for average users to understand. Furthermore, frequently changing terms and conditions place a significant burden on users, who must constantly review them and recognize risks. Furthermore, it is not easy for companies to properly understand user sentiment and feedback and use it to improve terms and conditions. Therefore, there is a need for a system that can automatically detect and summarize changes in terms and conditions, indicate risks, and recognize user sentiment to improve the user experience.

[1363] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1364] In this invention, the server includes an acquisition means, a change detection means, a generation means, a summarization means, a notification means, an evaluation transmission means, a compilation means, a report generation means, and an emotion recognition means, which enables the following:

[1365] 1. The server periodically retrieves the latest terms and conditions from the service provider's website and compares them with the previous terms and conditions to identify any changes.

[1366] 2. Use a generative AI model to analyze the identified changes and summarize the risks, providing information in a way that is easy for users to understand.

[1367] 3. Notify the user of the summary results in real time and collect user evaluation data and sentiment data.

[1368] 4. Generate reports for companies based on the collected evaluation and sentiment data and provide specific improvement suggestions.

[1369] This allows users to quickly respond to complex changes in terms and conditions and understand the risks, while also allowing companies to obtain useful feedback to improve their terms and conditions based on user sentiment and evaluations, thereby improving the user experience.

[1370] "Acquisition means" refers to a means that has the function of periodically acquiring the latest terms and conditions from the service provider's website.

[1371] The "change detection means" is a means having a function of comparing the previously acquired terms and conditions with the latest terms and conditions and identifying the changed parts.

[1372] "Generative means" means a means having the function of analyzing the changes identified using a generative AI model and summarizing the risks.

[1373] The "summarization means" is a means having a function of recording and storing the generated summary results and risk information.

[1374] The "notification means" is a means having a function of notifying the generated summary result to the user's terminal in real time.

[1375] The "evaluation sending means" is a means having a function of sending data evaluated by the user to the server.

[1376] The "aggregation means" is a means having a function of aggregating evaluation data and emotion data sent from a plurality of users and generating statistical information.

[1377] The "report generation means" is a means having a function of creating a report for a company based on the collected evaluation data and emotion data.

[1378] The "emotion recognition means" is a means having a function of recognizing emotions from the user's facial expressions, voice, and text input, and recording the emotion data.

[1379] A "generative AI model" is a model that uses natural language processing technology to summarize and identify risks based on input text.

[1380] A "prompt" is text used as input to a generative AI model, and is an instruction used to summarize and clarify risks.

[1381] The present invention is embodied as a system including an acquisition means, a change detection means, a generation means, a summarization means, a notification means, a rating transmission means, a compilation means, a report generation means, and an emotion recognition means, which not only makes it easier for users to understand terms and conditions and contracts of online services, but also recognizes the user's emotions and provides appropriate feedback to improve the user experience.

[1382] Obtaining the terms and conditions

[1383] The server periodically accesses the service provider's website to retrieve the latest terms and conditions. This process is performed by sending an HTTP request and analyzing the HTML content received as a response. Specifically, the "requests" library is used to send the HTTP request, and the "BeautifulSoup" library is used to parse the HTML content and retrieve the text of the terms and conditions.

[1384] Change Detection

[1385] The server compares the newly acquired terms and conditions with the previously acquired terms and conditions. The comparison is performed using a difference detection algorithm such as DiffMatchPatch to identify changes. This makes it possible to clearly identify important changes for the user.

[1386] Summary of changes and risk identification

[1387] The server inputs the text of the changes as a prompt to the generative AI model (e.g., OpenAI's GPT-3), summarizing the changes and highlighting the risks. Specific examples of prompts include:

[1388] To help users understand the latest changes, please summarize the following text and highlight any important caveats or risks:

[1389] [Insert text of change to terms here]

[1390] The generative AI model provides a concise summary of the revised terms and conditions, highlighting any risks that require particular attention.

[1391] Notification of summary results

[1392] The server notifies the user of the generated summary results via web sockets or push notifications, allowing the user to receive important change information in real time via a smartphone app or web browser.

[1393] Evaluation of terms and conditions

[1394] The user checks the notification on their device and views the changes to the terms and conditions. They then review a summary of the changes and a statement of the risks, and understand the details. This allows the user to quickly grasp important changes.

[1395] User Emotion Recognition

[1396] When a user uses the device to evaluate the policy changes, the device uses emotion recognition means to capture the user's facial expressions and voice to recognize their emotions, for example, by using the OpenCV library or Microsoft Azure Cognitive Services to recognize and record the user's emotions in real time.

[1397] Sending emotional data

[1398] The device sends the user's rating and emotion data to the server by sending an HTTP POST request using the "requests" library.

[1399] Aggregation of evaluation data and emotion data

[1400] The server collects and aggregates the evaluation and emotion data sent by multiple users into a database. Specifically, it uses the Pandas library to analyze the evaluation and emotion data and generate statistical information.

[1401] Generate reports

[1402] The server generates a report for the company based on the aggregated evaluation data and sentiment data. This report includes suggestions for improving terms and conditions and avoiding risks. The generated report is either sent to the company's personnel via email or provided for viewing on a dedicated corporate dashboard. Specifically, the report is created in HTML format using the "Jinja2" template engine, and emailed using the "smtplib" library. The corporate dashboard is also built using "Flask" as a web framework.

[1403] Specific examples

[1404] For example, if the terms and conditions of a particular online education service are updated, the system automatically detects the changes and notifies the user by summarizing important changes and risks. When the user reviews and evaluates the changes, the emotion engine identifies the user's emotions and sends this data to the server. The server generates a report for the company based on the collected data, and provides the company with improvement suggestions that include the user's emotional state. This allows users to use the education service with peace of mind and allows companies to receive specific feedback.

[1405] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1406] Step 1:

[1407] The server accesses the service provider's website at 2:00 AM every day to retrieve the latest terms and conditions. The input for this process is the website URL, and an HTTP request is sent to retrieve the HTML content. The HTTP request is sent using the "requests" library, and the HTML content received as a response is analyzed using the "BeautifulSoup" library to extract the terms and conditions text. As an output, the extracted terms and conditions text is saved in a database.

[1408] Step 2:

[1409] The server retrieves the newly acquired clause text and the previously acquired clause text from the database. The input to this process is the two clause texts retrieved from the database. Next, a difference detection algorithm such as DiffMatchPatch is used to identify the changes in the text. As an output, the server generates the text of the identified changes and stores it in the database.

[1410] Step 3:

[1411] The server inputs the text of the change to the generative AI model as a prompt. The input for this process is the text of the change. Here is an example of a prompt sentence to input to the generative AI model:

[1412] To help users understand the latest changes, please summarize the following text and highlight any important caveats or risks:

[1413] [Insert text of change to terms here]

[1414] The generative AI model analyzes this input, produces a summary and identifies risks, and stores the resulting summary and risk information in a database as output.

[1415] Step 4:

[1416] The server notifies the user's device of the generated summary results. The input for this process is the summary results and risk information. Web sockets and push notifications are used as notification methods. The server uses the "WebSocket" library and "Firebase Cloud Messaging" to send notifications to the user's device in real time. As an output, notifications are displayed on the user's device.

[1417] Step 5:

[1418] The user checks the notification displayed on their device and views the changes to the terms and conditions. The input to this process is a summary of the terms and conditions received as a notification. The user then views this information using a smartphone app or web browser to understand the content. The output is a deeper understanding of the user.

[1419] Step 6:

[1420] When a user uses a device to evaluate changes to the terms and conditions, the device captures the user's facial expressions and voice using a camera and microphone, and analyzes them using emotion recognition. The input for this process is the text to be evaluated and the captured user's facial expressions and voice data. The device recognizes emotions and records the emotion data using OpenCV and Microsoft Azure Cognitive Services. As an output, emotion data is generated and recorded.

[1421] Step 7:

[1422] The device sends the user's rating data and emotion data to the server. The input of this process is the rating data and emotion data. The device uses the "requests" library to send an HTTP POST request to the server's API endpoint, and the data is sent to the server as output.

[1423] Step 8:

[1424] The server imports rating data and emotion data submitted by multiple users into a database and aggregates them. The input for this process is the rating data and emotion data stored in the database. The "Pandas" library is used to analyze the data and generate statistical information. The aggregated results are generated as the output.

[1425] Step 9:

[1426] The server generates a report for the company based on the aggregated evaluation data and sentiment data. The input for this process is the aggregated data. The report is created in HTML format using the "Jinja2" template engine and sent by email using the "smtplib" library. In addition, a dashboard for the company is provided using "Flask" as a web framework. The generated report is provided to the company's personnel as an output.

[1427] (Application example 2)

[1428] 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 robot 414 will be referred to as a "terminal."

[1429] Changes to terms of service are frequent in modern online services, making it difficult for users to keep up with them in a timely and accurate manner. Understanding user sentiment regarding changes to terms of service provides important feedback for service providers, but a system for efficiently collecting and analyzing this information has yet to be established. Furthermore, there is a lack of a system for clearly communicating the risks and importance of changes to users, which often results in a poor user experience.

[1430] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a generating means, an acquiring means, a change detecting means, a summarizing means, a notifying means, an emotion recognizing means, an evaluation sending means, a tabulating means, and a report generating means. This makes it possible to quickly detect changes in the terms of use, summarize the risks, and notify the user, and further collect user emotion data, aggregate this data, and provide feedback to the company.

[1431] The "generation means" is a means for analyzing the identified changes and summarizing them in a form that is easy for the user to understand.

[1432] "Means for Acquisition" means a means for periodically obtaining the latest terms and conditions from the website of the online service provider.

[1433] The "change detection means" is a means for comparing the previously acquired terms and conditions with the latest terms and conditions to identify any changes.

[1434] The "summarization means" is a means for summarizing important points and risks of the changes identified by the change detection means.

[1435] The "notification means" is a means for notifying the user of the generated summary result.

[1436] An "emotion recognition means" is a means for recognizing emotions from a user's facial expressions, voice, text input, etc., and acquiring that data.

[1437] The "evaluation sending means" is a means for sending the data rated by the user and the emotion data acquired by the emotion recognition means to the server.

[1438] The "aggregation means" is a means for aggregating the evaluation data and emotion data sent from a plurality of users.

[1439] The "report generation means" is a means for generating a report for a company based on the collected evaluation data and emotion data.

[1440] A system for carrying out the present invention includes the following means:

[1441] 1. Acquisition method:

[1442] The server periodically retrieves the latest terms and conditions from the online service provider's website by sending an HTTP request and parsing the HTML content returned as a response. Specifically, it uses an HTML parsing library such as BeautifulSoup to extract the terms and conditions text.

[1443] 2. Change detection methods:

[1444] The server compares the previously retrieved contract with the newly retrieved contract, using a difference detection algorithm such as difflib to detect text differences and identify changes.

[1445] 3. Summary tools:

[1446] The server uses a generative AI model to summarize the risks of the changes. It generates prompt sentences, inputs them into the generative AI model, and summarizes the changes using natural language processing technology.

[1447] 4. Means of notification:

[1448] The generated summary results are sent to the user's device (smartphone app or web browser) via web socket or push notification, allowing the user to receive real-time updates.

[1449] 5. Emotion recognition means:

[1450] As users evaluate the policy changes, the device recognizes their emotions from their facial expressions, voice, and text input. This emotional data is obtained using an emotion recognition engine such as EmotionEngine.

[1451] 6. Evaluation submission method:

[1452] The device transmits user rating and emotion data to the server, which is then aggregated.

[1453] 7. Aggregation method:

[1454] The server aggregates the evaluation data and emotional data sent by multiple users, thereby generating data that indicates the overall evaluation of the terms and conditions and the emotional state of the users.

[1455] 8. Report Generation Methods:

[1456] The server generates reports for companies based on the aggregated evaluation and sentiment data, including recommendations for policy improvements and risk avoidance, which are emailed to company representatives or made available for review on a dedicated company dashboard.

[1457] Specific examples

[1458] For example, if the terms and conditions of a particular online education service are updated, the system automatically detects the changes and notifies the user by summarizing important changes and risks. When the user reviews and evaluates the changes, the emotion engine identifies the user's emotions and sends this data to the server. A report is then generated for the company based on the aggregated results, and the company is provided with improvement suggestions that take into account the user's emotional state. This allows users to use the service with peace of mind and allows companies to receive more specific feedback.

[1459] Prompt Sentence Examples

[1460] Changes:

[1461] The usage fee will increase from 3% to 5%.

[1462] Please summarize."

[1463] Notification summary and risk statement:

[1464] "

[1465] notification:

[1466] Latest Terms and Conditions Changes:

[1467] The service fee has increased from 3% to 5%, a change that may directly affect the payment costs of users.

[1468] "

[1469] In this way, by using the system of the present invention, it is possible to quickly grasp changes to the terms of use and the risks involved, and also to provide feedback that reflects the user's feelings.

[1470] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1471] Step 1:

[1472] The server retrieves the latest terms and conditions from the online service provider's website by sending an HTTP request and parsing the HTML content returned as a response. A library such as BeautifulSoup is used for HTML parsing, and the latest terms and conditions text is extracted.

[1473] Input: Service provider website URL

[1474] Output: Latest extracted clause text

[1475] Step 2:

[1476] The server compares the previously retrieved clause with the latest clause to identify any changes. This comparison uses a difference detection algorithm such as difflib, and a change detection means detects the differences in the text.

[1477] Input: Latest clause text, previously retrieved clause text

[1478] Output: List of changes

[1479] Step 3:

[1480] The server uses a generative AI model to summarize the risks of the changes. A prompt is input into the generative AI model, which then uses natural language processing technology to summarize the changes. Based on the input prompt, the generative AI outputs text summarizing the important points and risks of the changes.

[1481] Input: list of changes, prompt

[1482] Output: Summarized risk text

[1483] Step 4:

[1484] The server then notifies the user of the generated summary results via web sockets or push notifications, which are sent in real time to the user's smartphone app or web browser.

[1485] Input: Abstracted risk text

[1486] Output: Notification to user's device

[1487] Step 5:

[1488] The user reviews and evaluates the changes to the terms and conditions. In addition, the user's device recognizes emotions from the user's facial expressions, voice, and text input. This emotion data is obtained using an emotion recognition engine such as EmotionEngine.

[1489] Input: User facial expressions, voice, and text input

[1490] Output: Emotion data, rating data

[1491] Step 6:

[1492] The terminal transmits the user's evaluation data and emotion data to the server via the evaluation transmitting means.

[1493] Input: Emotion data, rating data

[1494] Output: Send data to the server

[1495] Step 7:

[1496] The server aggregates the evaluation data and emotional data sent by multiple users, thereby generating data that indicates the overall evaluation of the terms and conditions and the emotional state of the users.

[1497] Input: Rating data and sentiment data from multiple users

[1498] Output: Aggregated rating data, sentiment data

[1499] Step 8:

[1500] The server generates reports for companies based on the aggregated evaluation and sentiment data, including recommendations for policy improvements and risk avoidance, which are emailed to company representatives or made available for review on a dedicated company dashboard.

[1501] Input: Aggregated rating data, sentiment data

[1502] Output: Corporate reports

[1503] This allows users to use the service with peace of mind and allows companies to receive more specific feedback.

[1504] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice 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 voice data.

[1505] 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.

[1506] 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 robot 414.

[1507] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1508] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1509] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1510] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1511] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1512] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1513] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1514] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1515] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1516] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1517] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1518] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1519] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1520] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1521] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1522] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1523] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1524] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1525] The following is further disclosed regarding the above embodiment.

[1526] (Claim 1)

[1527] generating means;

[1528] Acquisition means;

[1529] A change detection means;

[1530] a summarizing means;

[1531] Notification means;

[1532] evaluation sending means;

[1533] A means of aggregation;

[1534] A system including a report generation means.

[1535] (Claim 2)

[1536] The system of claim 1, wherein the acquisition means acquires the terms and conditions from the service provider's website, the change detection means compares the previously acquired terms and conditions with the latest terms and conditions to identify changes, and the generation means analyzes the identified changes and summarizes the risks.

[1537] (Claim 3)

[1538] The system of claim 1, wherein the generating means notifies the user of the generated summary result to the user's terminal, and transmits the data evaluated by the user to the aggregating means via the evaluation sending means, and the aggregating means aggregates the user's evaluations to generate a report and provide proposals for companies.

[1539] "Example 1"

[1540] (Claim 1)

[1541] Acquisition means;

[1542] A change detection means;

[1543] generating means;

[1544] a summarizing means;

[1545] Notification means;

[1546] evaluation sending means;

[1547] A means of aggregation;

[1548] A system including a report generation means.

[1549] (Claim 2)

[1550] The system of claim 1, wherein the acquisition means acquires documents from the information provider's website, the change detection means compares the previously acquired document with the latest document to identify changes, and the generation means analyzes the identified changes and summarizes the risks.

[1551] (Claim 3)

[1552] The system of claim 1, wherein the generating means notifies the user of the generated summary result and transmits the data evaluated by the user to the aggregating means via the evaluation sending means, and the aggregating means aggregates the user evaluations to generate a report and provide suggestions for the organization.

[1553] "Application Example 1"

[1554] (Claim 1)

[1555] generating means;

[1556] Acquisition means;

[1557] A change detection means;

[1558] a summarizing means;

[1559] Notification means;

[1560] evaluation sending means;

[1561] A means of aggregation;

[1562] report generation means;

[1563] A means of regularly obtaining documentation within the factory;

[1564] A means for analyzing and summarizing document changes based on a generative AI model;

[1565] The system includes a means for notifying users within the plant of the summary results.

[1566] (Claim 2)

[1567] The system of claim 1, wherein the acquisition means acquires documents from a factory document management system, the change detection means compares the previously acquired document with the latest document to identify changes, and the generation means analyzes the identified changes and summarizes the risks.

[1568] (Claim 3)

[1569] The system of claim 1, wherein the generating means notifies the user of the generated summary result to the user's terminal, and transmits the data evaluated by the user to the aggregating means via the evaluation sending means, and the aggregating means aggregates the user's evaluations to generate a report and provide proposals for companies.

[1570] "Example 2: Combining Emotion Engines"

[1571] (Claim 1)

[1572] Acquisition means;

[1573] A change detection means;

[1574] generating means;

[1575] a summarizing means;

[1576] Notification means;

[1577] evaluation sending means;

[1578] A means of aggregation;

[1579] report generation means;

[1580] A system including an emotion recognition means.

[1581] (Claim 2)

[1582] The system of claim 1, wherein the acquisition means acquires terms and conditions from the service provider's website, the change detection means compares the previously acquired terms and conditions with the latest terms and conditions to identify changes, the generation means analyzes the identified changes using a generative AI model and summarizes risks, and the summarization means stores the generated summary and risk information.

[1583] (Claim 3)

[1584] The system of claim 1, wherein the generating means notifies the user of the generated summary result and transmits the user's evaluation data to the aggregating means via the evaluation sending means, the aggregating means aggregates the emotion data recognized by the emotion recognition means together with the user's evaluation, and the report generating means provides proposals to companies based on the aggregated evaluation data and emotion data.

[1585] "Application example 2 when combining emotion engines"

[1586] (Claim 1)

[1587] generating means;

[1588] Acquisition means;

[1589] A change detection means;

[1590] a summarizing means;

[1591] Notification means;

[1592] An emotion recognition means;

[1593] evaluation sending means;

[1594] A means of aggregation;

[1595] A system including a report generation means.

[1596] (Claim 2)

[1597] The system of claim 1, wherein the acquisition means acquires terms and conditions from the website of the online service provider, the change detection means compares the previously acquired terms and conditions with the latest terms and conditions to identify changes, and the generation means analyzes the identified changes and summarizes the risks.

[1598] (Claim 3)

[1599] The system of claim 1, wherein the generating means notifies the user's terminal of the generated summary result, transmits the data evaluated by the user and the emotion data acquired by the emotion recognizing means to the aggregating means via the evaluation transmitting means, and the aggregating means aggregates the user's evaluation data and emotion data to generate a report and provide proposals for companies. [Explanation of symbols]

[1600] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. generating means; Acquisition means; A change detection means; a summarizing means; Notification means; evaluation sending means; A means of aggregation; A system including a report generation means.

2. The system described in claim 1, wherein the acquisition means acquires terms and conditions from the service provider's website, the change detection means compares the previously acquired terms and conditions with the latest terms and conditions to identify changes, and the generation means analyzes the identified changes and summarizes the risks.

3. The system of claim 1, wherein the generation means notifies the user of the generated summary result and transmits the user's evaluation data to the aggregation means via the evaluation transmission means, and the aggregation means aggregates the user's evaluations to generate a report and provide proposals for businesses.

Images