system

Abstract

We provide the system. [Solution] A means for storing digital information acquired by information gathering means in a database, A means for detecting data inconsistencies by comparing new digital data with information in existing databases, A means of notifying the user of detected inconsistencies through an interface, A means for users to immediately correct digital content based on inconsistency information notified in real time, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Information published on the Internet is composed of a large number of pages, and there may be a contradiction with existing information when adding new pages. This contradiction in information gives viewers confusion and distrust, and is a factor that damages trust. Currently, manual checking is mainly carried out, and it is difficult to perform an efficient and comprehensive consistency check. It is required to solve such problems and ensure the reliability of information.

Means for Solving the Problems

[0005] This invention first provides a means for automatically acquiring all page information on a website and storing it in a database. Then, it includes a means for comparing newly created pages with existing information registered in the database and detecting inconsistencies using AI. Furthermore, it includes a means for notifying the user of any detected inconsistencies, along with their specific details, enabling the user to quickly verify and correct them. This system automatically guarantees the integrity of information and makes it possible to resolve issues before publication.

[0006] A "website" is a collection of information published on the internet, consisting of numerous pages.

[0007] A "domain" is an identifier used in a URL to indicate a specific namespace for a website.

[0008] A "database" is a system that organizes and stores large amounts of information, making it possible to quickly search and use it when needed.

[0009] "Information contradiction" refers to a situation where conflicting or contradictory information exists between different pages or within the same page.

[0010] "AI" is an abbreviation for artificial intelligence, a technology that allows computers to learn and make decisions like humans, and to assist in problem-solving.

[0011] A "user" is a person or group that manages and operates a website, and performs tasks such as creating and publishing new pages.

[0012] "Notification" is the act of informing others about a certain fact or piece of information, and is especially used when conveying anomalies or points of caution. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention relates to a website management system for ensuring the integrity of information. Its purpose is to prevent information inconsistencies by having the server, terminal, and user cooperate in collecting, verifying, and notifying information.

[0035] The server first collects information from all pages of the website. This involves accessing each page and extracting necessary elements such as product information and campaign details from the HTML data. The extracted information is stored in a database for efficient searching and comparison.

[0036] Next, the user uploads the newly created page to their device, preparing it for consistency checks before publication. The device converts the new page's format into one that the server's comparison engine can easily process. This conversion enables accurate comparisons even between different formats.

[0037] The server uses AI to meticulously compare new pages with information in existing databases. The AI ​​employs natural language processing and pattern recognition to detect subtle inconsistencies and potential errors. For example, it generates alerts when specific inconsistencies are found, such as differing prices or discrepancies in product descriptions.

[0038] This alert is sent from the server to the user. The notification includes specific figures and explanations of which information is inconsistent, allowing the user to quickly identify the error and make the necessary corrections. It is expected that automating this process in daily operations will significantly reduce the rate of information errors.

[0039] This invention enables a system that can always provide users with the latest and most consistent information, even on websites containing complex information.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server crawls every page of the entire website. This retrieves HTML data, including all product information and campaign details. The retrieved information is then analyzed using appropriate analytics tools to extract and organize specific data.

[0043] Step 2:

[0044] The user creates the content for a new page and prepares to upload the data to their device for consistency checking. The user then checks the page's completeness and visually reviews it for errors.

[0045] Step 3:

[0046] The terminal receives the new page and converts its contents into the server's database format. This prepares the information on the new page in a format that can be directly compared with existing data. Format compatibility is ensured at this stage.

[0047] Step 4:

[0048] The server uses an AI engine to compare new page data with existing database information. The AI ​​quickly calculates text similarity and numerical data matches, identifying different elements. In this process, the server identifies subtle differences between pages and detects potential inconsistencies.

[0049] Step 5:

[0050] If the server detects a discrepancy, it will notify the user of the details as an alert. The notification will include specific numbers and text indicating which part is inconsistent.

[0051] Step 6:

[0052] Users review the alert content and re-evaluate the information on the new page. They can then modify the interface as needed and upload it again to the server via their device, allowing for a rapid consistency check cycle.

[0053] (Example 1)

[0054] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0055] In modern web systems, the accumulation of complex and vast amounts of information increases the likelihood of inconsistencies and errors. This problem can impair the user experience and even reduce reliability. Current systems lack efficient means to detect and correct information inconsistencies, necessitating automated methods to ensure information integrity.

[0056] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0057] In this invention, the server includes means for collecting information using a web resource acquisition device and storing it in an information set, means for comparing new resources with existing information in the information set and identifying information inconsistencies, and means for analyzing the details of the inconsistencies using a generative model and notifying the user. This enables a process for automatically detecting and quickly correcting information inconsistencies.

[0058] A "web resource acquisition device" is a technological device that automatically accesses web content on the internet and collects necessary data.

[0059] An "information collection" is a database or data storage system used to store and manage collected data and information.

[0060] "Inconsistency" refers to contradictions or discrepancies that arise between newly added information and existing information, and is a factor that undermines the reliability of the data.

[0061] A "generative model" is an artificial intelligence technology that analyzes and learns from large amounts of data to generate or analyze new information based on specific patterns or rules.

[0062] "User" refers to an individual or organization that operates this system to collect, verify, or modify information.

[0063] This invention provides a system for ensuring information integrity, and functions effectively through the cooperation of a server, terminal, and user. Specifically, the server automatically accesses web content on the internet using a web resource acquisition device, collects the necessary data, and stores it appropriately in an information collection. In this process, the server efficiently processes the data using the requests library and analysis tools such as BeautifulSoup.

[0064] Next, users can upload newly created pages to the system via their terminals, allowing for verification of information integrity. The terminals then convert these new pages, formatting them into a format that the server's comparison engine can match against existing company information. This may involve using XSLT transformations or regular expressions.

[0065] Furthermore, the server leverages generative models to thoroughly examine inconsistencies between new pages and existing data within the information set. In particular, it utilizes spaCy and NLTK to apply natural language processing and extract data discrepancies. The server uses the generative AI model to send prompts such as: "Analyze the content of the new page and compare it to the information set to identify inconsistencies."

[0066] Finally, based on the identified inconsistencies, the server uses a generative model to analyze the details of the inconsistencies and notify the user appropriately. For example, by reporting specific details of the information inconsistencies via email notifications or pop-ups on the dashboard, users can quickly take corrective action. This method ensures the reliability and consistency of the information.

[0067] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0068] Step 1:

[0069] The server accesses all pages of a website using a web resource acquisition device and collects HTML data. Specifically, it sends HTTP requests using the requests library and parses the resulting HTML using BeautifulSoup. The input is the URL of the web page, and the output is a dataset containing the parsed product information and campaign details. This data is stored in a database as an information set for later comparison.

[0070] Step 2:

[0071] The user creates a new web page. They upload this new page to the system via a terminal. The specific action involves specifying the HTML file for the new page and executing the upload operation. The input is the new page file on the user's local disk, and the output is a notification that the transfer to the server is complete.

[0072] Step 3:

[0073] The terminal converts the format of the uploaded new page into a format that can be easily processed by the server's comparison engine. Specifically, it performs formatting unification processing using XSLT transformations and regular expressions. The input is the original HTML data of the new page, and the output is the formatted data.

[0074] Step 4:

[0075] The server uses a generative AI model to compare the formatted new page data with existing data in the information set in detail. The generative AI model evaluates inconsistencies based on the given prompt sentences. The input is the formatted data and a dataset from the information set, and the output is a list of inconsistencies in that information. SpaCy or NLTK are used for natural language processing.

[0076] Step 5:

[0077] The server analyzes inconsistencies in the comparison results and notifies the user of the specific discrepancies. Specifically, it sends emails or displays the results on a dashboard in the user interface. The input is a list of inconsistencies, and the output is a detailed notification message provided to the user.

[0078] Through this process, servers, terminals, and users cooperate to achieve highly information-consistent website management.

[0079] (Application Example 1)

[0080] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0081] Inconsistencies in promotional information within electronic payment services can lead users to act based on misinformation. Such inconsistencies damage customer trust and reduce service satisfaction. A system that can ensure real-time consistency is needed.

[0082] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0083] In this invention, the server includes means for storing digital information acquired by information gathering means in a data bank, means for comparing new digital data with information in an existing data bank to detect data inconsistencies, and means for notifying the user of the detected inconsistencies through an interface. This enables real-time verification of the consistency of promotional information in electronic payment services, allowing users to make immediate corrections.

[0084] "Information gathering means" refers to technologies or devices for efficiently collecting digital information and storing it in a data bank.

[0085] A "data bank" is a computer system that systematically stores collected digital information, making it easily searchable and retrievable.

[0086] "New digital data" refers to new information not included in existing data banks, and specifically includes promotional information related to electronic payment services.

[0087] "Inconsistency" is a term that describes a state in which the expected consistency is lacking between digital data.

[0088] An "interface" refers to the point of contact or means by which a user and a system exchange information, and its primary role is to notify the user of information.

[0089] "Machine learning technology" is a technique that automatically learns patterns and insights from data to make future predictions and decisions.

[0090] "Real-time" refers to processing or responding immediately, indicating minimal time delay.

[0091] The system implementing this invention consists of a server, a terminal, and a user. The server collects all digital information related to the electronic payment service using information gathering means and stores it in a data bank. This data bank is built using a database system such as PostgreSQL, enabling efficient storage, retrieval, and management of information. The server uses machine learning techniques such as TENSORFLOW® and PyTorch to convert new digital data into a processable format, thereby performing detailed comparisons with existing data.

[0092] The terminal receives instructions from the server and notifies the user in real time of any detected inconsistencies. A smartphone application is used as the interface, allowing the user to quickly correct the digital content based on the information provided. Specifically, if distorted promotional information is discovered, the user receives a notification and is required to immediately correct the information.

[0093] In this way, servers, terminals, and users work together to ensure the integrity of information in real time, thereby improving reliability and user experience in electronic payment services.

[0094] A concrete example is when a cashback campaign is launched for a certain service, and information inconsistencies are discovered. In this case, the server detects the information inconsistencies, and the terminal notifies the user. The user can then quickly correct the product information with incorrect pricing or conditions, enabling the provision of accurate information to customers.

[0095] An example of a prompt for a generative AI model would be: "Verify the consistency of the new promotional information and compare it with the information in the database to identify all inconsistencies." This prompt allows the model to perform accurate comparisons and detections.

[0096] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0097] Step 1:

[0098] The server collects all digital information related to electronic payment services from the web. This information includes campaign details and promotional content. The server uses scraping techniques to retrieve this information and stores it in a PostgreSQL database. The input is HTML data from web pages, and the output is structured data stored in the database.

[0099] Step 2:

[0100] The server converts newly added digital data into a format that can be processed using natural language processing (generative AI model). The server analyzes the underlying patterns in the data using TensorFlow and unifies them into a conventional data format. The input is new digital data, and the output is data in a unified format.

[0101] Step 3:

[0102] The server compares new data in a unified format with information from existing databases to detect data inconsistencies. In this process, the server computationally analyzes the differences between the data to identify which information is inconsistent. The input consists of new and existing data in a unified format, and the output is the detected inconsistencies.

[0103] Step 4:

[0104] The server notifies the user's terminal of the detected inconsistencies. The terminal receives this information and prepares an interface to present it to the user. The input is the inconsistency information, and the output is the notification message presented to the user.

[0105] Step 5:

[0106] The user corrects the digital content based on the inconsistency information received from their device. The user then sends the corrected information back to the server via their device, updating the database. The input is the digital data corrected based on the inconsistency information, and the output is the database updated with the latest accurate data.

[0107] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0108] This invention provides an advanced integrity checking system that takes user sentiment into account in the information management process of a website. This system operates through the cooperation of the server, terminal, user, and sentiment engine.

[0109] The server crawls and collects information from the entire website and stores that data in a database. The collected information includes detailed content for each page, preparing it for later comparison processing. The terminal receives newly created pages from users and converts the data into an existing format. This newly supplied data is intended to facilitate comparison with the existing database.

[0110] The emotion engine is built in to recognize the user's emotions at the notification stage, determining emotions through AI-based analysis of the user's facial expressions, voice, or text. Leveraging this emotion information, the server can deliver inconsistent notifications tailored to the user's emotional state. For example, if the user is stressed, the content and tone of the notification can be softened, providing more supportive messaging.

[0111] The server uses AI to thoroughly verify any inconsistencies between new pages and existing data. Any detected inconsistencies are notified to the user. The sentiment engine considers the user's current emotional state to select the appropriate notification method and content. The user is presented with specific details of the inconsistency, along with suggested corrections tailored to their emotional state.

[0112] For example, if a user mistakenly enters the wrong price for product A on a different page, the server will detect this inconsistency. If the emotion engine determines that the user is anxious, it will add a helpful comment such as, "Please take your time to check," as a notification. In this way, dynamic responses that take user emotions into account enable efficient and human-centered information management and correction.

[0113] The following describes the processing flow.

[0114] Step 1:

[0115] The server crawls all pages on the website and collects information from each page. The collected information is parsed from the HTML and stored in a database with product and campaign details.

[0116] Step 2:

[0117] The user creates a new page and uploads the information to their device for consistency checking. The user then performs a final review of the design and content to ensure there are no problems.

[0118] Step 3:

[0119] The device receives data from the new page and converts it into a format that can be processed by the server. This conversion allows the new data to be smoothly compared with existing information.

[0120] Step 4:

[0121] The server uses an AI engine to compare information from new pages with existing database information. The AI ​​identifies inconsistencies and generates detailed comparison results.

[0122] Step 5:

[0123] The emotion engine analyzes user responses and determines their current emotional state from voice, facial expressions, and entered text. This information is taken into consideration when issuing notifications.

[0124] Step 6:

[0125] The server notifies the user of any detected inconsistencies, but customizes the notification content based on the results of the sentiment engine's analysis. For example, if the server detects that the user is in an unstable state, it will include encouragement and suggestions in the notification message.

[0126] Step 7:

[0127] The user reviews the received notification, checks for inconsistencies, and corrects them. After correction, the user performs another consistency check to confirm that all inconsistencies have been resolved and then prepares for publication.

[0128] (Example 2)

[0129] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0130] In the field of modern information processing, many websites handle vast amounts of information, making it crucial to maintain information consistency. In particular, ensuring that newly added information does not contradict existing information is difficult, which can inconvenience users. Furthermore, the method of information delivery needs to be optimized based on the user's emotions and circumstances. Traditional systems lack sufficient methods to simultaneously consider information consistency and the user's emotional state, highlighting the need for improved user experience.

[0131] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0132] In this invention, the server includes means for acquiring all content information within a web resource identifier and storing it in a data set; means for comparing information of new content with information in an existing data set and detecting inconsistencies in the information; and means for analyzing the emotional state using artificial intelligence and notifying the user of the inconsistencies in a manner that corresponds to the user's emotions. This makes it possible to improve the user experience while maintaining the integrity of the information.

[0133] A "web resource identifier" is a sequence of symbols used to point to specific web content, commonly known as a URL.

[0134] "All content information" refers to all data and metadata contained within a web resource, including text, images, videos, scripts, and more.

[0135] A "data collection" refers to a digital storage location where collected information is systematically stored, and databases typically fulfill this role.

[0136] "New content" refers to newly added information that is not yet included in the existing data set.

[0137] "Inconsistency" refers to a point of contradiction or discrepancy that occurs between two or more datasets, indicating errors or inconsistencies in the information.

[0138] "Artificial intelligence" refers to the technology of using computer systems to imitate human intellectual activity, and typically includes technologies such as machine learning and natural language processing.

[0139] "Emotional state" refers to the emotions a user is experiencing or expressing at a particular moment, and may be determined by facial expressions, voice, and language.

[0140] "Notification" refers to the act of transmitting important information or alerts to a target audience, and is particularly a means of informing recipients of inconsistencies or points that require correction.

[0141] This invention is a system in which a server, terminal, user, and artificial intelligence technology work together in the information management process for web resources. The server collects all content information within the identifier of a web resource and stores this information in a data set. Specifically, it crawls web pages, analyzes various data such as text, images, and videos, and saves them in a database. This process uses general server hardware and software specialized for crawling and data analysis.

[0142] The terminal is responsible for receiving new information added by the user and converting it into a unified format. When a user edits a web page via the terminal, the entered data is converted into a standard format so that the server can easily parse it later. A dedicated data format conversion tool is used for this process.

[0143] Meanwhile, the emotion engine uses a generative AI model to analyze the user's emotional state in real time. This analysis is based on facial expressions, voice, and text data acquired from the device. The AI ​​model is a crucial technology for providing feedback based on emotional state, and if the user is feeling down, for example, it will choose calmer words to respond with.

[0144] For example, if a user mistakenly enters inconsistent product pricing information on different pages, the server will detect this discrepancy. If the sentiment engine determines the user is anxious, the server will notify them with a suggestion to "take your time to check." This dynamic response allows the user to calmly correct their error.

[0145] By utilizing generative AI models, the user experience can be improved, and information consistency can be managed more efficiently. A concrete example of a prompt might be, "When user A receives inconsistent product information while in a state of panic, what follow-up message should be provided?"

[0146] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0147] Step 1:

[0148] The server receives a web resource identifier (URL) as input. The server then initiates an automated crawling process, collecting all content information within the specified scope. This includes HTML documents, images, videos, and other relevant metadata. The server parses this information, converts it into a structured format, and stores it in a data collection.

[0149] Step 2:

[0150] The terminal receives new content created or edited by the user as input. The terminal converts this information into a unified format that is easy for the server to parse. This conversion process includes correcting markup and normalizing data as needed. The output is sent to the server as formatted data.

[0151] Step 3:

[0152] The server compares newly submitted content with existing data sets. Using the new and existing data as input, it checks the consistency of each data field. The server detects differences and inconsistencies and outputs the results as a list.

[0153] Step 4:

[0154] The generative AI model uses facial expressions, voice, or text acquired from the device as input data to analyze the user's emotional state. The model determines the user's emotional state and outputs the analysis results to the server.

[0155] Step 5:

[0156] The server receives a list of inconsistencies and the results of an emotional state analysis from a generating AI model as input. The server generates sentiment-based messaging to optimize notifications to the user. For example, if the server determines that the user is anxious, it will send a notification such as, "Please check again when you are not in a hurry."

[0157] Step 6:

[0158] The user receives a notification from the server, reviews the presented information, and makes corrections. The user then creates new data and sends it back to the server via their device. This feedback loop improves accuracy and efficiency, and maintains the integrity of the information.

[0159] (Application Example 2)

[0160] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0161] In information management, inconsistencies can arise when users handle large amounts of information. These inconsistencies often occur due to unintended errors or input mistakes, and if they persist, they can lead to a deterioration of the user experience and a decrease in the reliability of the information. Furthermore, traditional notification systems send uniform messages without considering the user's perception, which can cause stress to users. Therefore, a system that can flexibly respond according to the user's perception is needed.

[0162] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0163] In this invention, the server includes means for acquiring all data within a collection of information and storing it in a recording device, means for comparing new data with data in an existing recording device and detecting inconsistencies in the information, means for notifying the user of the detected inconsistencies, and means for determining the user's awareness state and adjusting the notification content according to that awareness state. This makes it possible to quickly detect inconsistencies and notify appropriately, and to perform messaging that takes into account the user's awareness state.

[0164] A "collection of information" is the totality of data collected according to certain criteria.

[0165] A "recording device" refers to hardware or software that has the function of accumulating and storing data.

[0166] "New data" refers to data that was not previously available and has been newly generated or acquired.

[0167] "Existing recording device" refers to data storage where information is stored in advance.

[0168] "Inconsistency" refers to a situation where different pieces of information do not match or exhibit contradictions.

[0169] "User" refers to an individual who operates or uses the system.

[0170] "Perceptual state" refers to the state of awareness and emotions that a user exhibits in relation to information.

[0171] "Notification content" refers to the specific information used to communicate detected information or messages to the user.

[0172] "Means of adjustment" refer to processes or functions for changing methods or content according to the situation or conditions.

[0173] The system for realizing this invention consists of a server, a terminal, and a user. The server is responsible for first acquiring all the data within the information collection and storing it in a recording device. This allows all data to be centrally managed and prepared for subsequent comparison processing.

[0174] The terminal is responsible for the process of comparing new data with data from existing recording devices and detecting inconsistencies. This process uses programming languages ​​such as Python and JavaScript® to perform efficient data calculations. Furthermore, it utilizes artificial intelligence and software tools such as Google® Cloud Vision API and Amazon Rekognition to convert the information into a processable format.

[0175] The user's perception state is determined by the emotion engine. The emotion engine uses the camera and microphone of the smartphone or smart glasses to analyze the user's facial expressions and voice. This information is used to adjust the content of notifications. Any detected inconsistencies are appropriately notified to the user from the server by the program and provided in a way that allows the user to take action based on their perception state.

[0176] As a concrete example, consider a scenario where a user accidentally enters airline ticket price information while creating content for a travel blog. The emotion engine would assess the user's state of mind and provide a gentle notification along with advice such as, "Next time, please double-check the price information." An example of a prompt to input into the generative AI model would be, "If there is a contradiction in the user's input, please provide advice that takes the user's emotions into consideration. Choose the emotion closest to the user's from relaxed, stressed, happy, or disappointed, and provide a notification with content appropriate to that emotion."

[0177] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0178] Step 1:

[0179] The server uses a web crawler to collect all the data within a collection of information and stores it in a storage device. The input is web page information, and the output is structured data stored in a database. When stored in the database, duplicate and unnecessary information is filtered out, and the data is processed to enable efficient searching and comparison.

[0180] Step 2:

[0181] The terminal receives new data entered by the user and compares it with data from an existing recording device. Here, the input is the new data provided by the user, and the output is information indicating data inconsistencies. The comparison process uses algorithms to check for data matches and mismatches. This process typically utilizes Python or JavaScript programs.

[0182] Step 3:

[0183] To recognize the user's emotions, the device utilizes the camera and microphone of a smartphone or smart glasses, and an emotion engine analyzes the user's facial expressions and voice. The input is real-time image or audio data of the user, and the output is the user's emotional state. This analysis uses the Google Cloud Vision API and Amazon Rekognition to perform data feature extraction and recognition using machine learning models.

[0184] Step 4:

[0185] The server considers inconsistencies and the user's emotional state, generates an appropriate notification, and sends it to the user via the terminal. The input is the inconsistent content and emotional state data, and the output is a notification message with adjusted content. Using a generation AI model, the message is generated based on the prompt statement: "If there are inconsistencies in the user's input, please provide advice that takes the user's emotions into consideration. Choose the emotion closest to relaxed, tense, happy, or disappointed, and notify the user with content appropriate to that emotion."

[0186] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0187] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0188] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0189] [Second Embodiment]

[0190] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0191] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0192] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0193] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0194] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0195] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0196] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0197] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0198] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0199] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0200] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0201] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0202] This invention relates to a website management system for ensuring the integrity of information. Its purpose is to prevent information inconsistencies by having the server, terminal, and user cooperate in collecting, verifying, and notifying information.

[0203] The server first collects information from all pages of the website. This involves accessing each page and extracting necessary elements such as product information and campaign details from the HTML data. The extracted information is stored in a database for efficient searching and comparison.

[0204] Next, the user uploads the newly created page to their device, preparing it for consistency checks before publication. The device converts the new page's format into one that the server's comparison engine can easily process. This conversion enables accurate comparisons even between different formats.

[0205] The server uses AI to meticulously compare new pages with information in existing databases. The AI ​​employs natural language processing and pattern recognition to detect subtle inconsistencies and potential errors. For example, it generates alerts when specific inconsistencies are found, such as differing prices or discrepancies in product descriptions.

[0206] This alert is sent from the server to the user. The notification includes specific figures and explanations of which information is inconsistent, allowing the user to quickly identify the error and make the necessary corrections. It is expected that automating this process in daily operations will significantly reduce the rate of information errors.

[0207] This invention enables a system that can always provide users with the latest and most consistent information, even on websites containing complex information.

[0208] The following describes the processing flow.

[0209] Step 1:

[0210] The server crawls every page of the entire website. This retrieves HTML data, including all product information and campaign details. The retrieved information is then analyzed using appropriate analytics tools to extract and organize specific data.

[0211] Step 2:

[0212] The user creates the content for a new page and prepares to upload the data to their device for consistency checking. The user then checks the page's completeness and visually reviews it for errors.

[0213] Step 3:

[0214] The terminal receives the new page and converts its contents into the server's database format. This prepares the information on the new page in a format that can be directly compared with existing data. Format compatibility is ensured at this stage.

[0215] Step 4:

[0216] The server uses an AI engine to compare new page data with existing database information. The AI ​​quickly calculates text similarity and numerical data matches, identifying different elements. In this process, the server identifies subtle differences between pages and detects potential inconsistencies.

[0217] Step 5:

[0218] If the server detects a discrepancy, it will notify the user of the details as an alert. The notification will include specific numbers and text indicating which part is inconsistent.

[0219] Step 6:

[0220] Users review the alert content and re-evaluate the information on the new page. They can then modify the interface as needed and upload it again to the server via their device, allowing for a rapid consistency check cycle.

[0221] (Example 1)

[0222] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0223] In modern web systems, the accumulation of complex and vast amounts of information increases the likelihood of inconsistencies and errors. This problem can impair the user experience and even reduce reliability. Current systems lack efficient means to detect and correct information inconsistencies, necessitating automated methods to ensure information integrity.

[0224] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0225] In this invention, the server includes means for collecting information using a web resource acquisition device and storing it in an information set, means for comparing new resources with existing information in the information set and identifying information inconsistencies, and means for analyzing the details of the inconsistencies using a generative model and notifying the user. This enables a process for automatically detecting and quickly correcting information inconsistencies.

[0226] A "web resource acquisition device" is a technological device that automatically accesses web content on the internet and collects necessary data.

[0227] An "information collection" is a database or data storage system used to store and manage collected data and information.

[0228] "Inconsistency" refers to contradictions or discrepancies that arise between newly added information and existing information, and is a factor that undermines the reliability of the data.

[0229] A "generative model" is an artificial intelligence technology that analyzes and learns from large amounts of data to generate or analyze new information based on specific patterns or rules.

[0230] "User" refers to an individual or organization that operates this system to collect, verify, or modify information.

[0231] This invention provides a system for ensuring information integrity, and functions effectively through the cooperation of a server, terminal, and user. Specifically, the server automatically accesses web content on the internet using a web resource acquisition device, collects the necessary data, and stores it appropriately in an information collection. In this process, the server efficiently processes the data using the requests library and analysis tools such as BeautifulSoup.

[0232] Next, users can upload newly created pages to the system via their terminals, allowing for verification of information integrity. The terminals then convert these new pages, formatting them into a format that the server's comparison engine can match against existing company information. This may involve using XSLT transformations or regular expressions.

[0233] Furthermore, the server leverages generative models to thoroughly examine inconsistencies between new pages and existing data within the information set. In particular, it utilizes spaCy and NLTK to apply natural language processing and extract data discrepancies. The server uses the generative AI model to send prompts such as: "Analyze the content of the new page and compare it to the information set to identify inconsistencies."

[0234] Finally, based on the identified inconsistencies, the server uses a generative model to analyze the details of the inconsistencies and notify the user appropriately. For example, by reporting specific details of the information inconsistencies via email notifications or pop-ups on the dashboard, users can quickly take corrective action. This method ensures the reliability and consistency of the information.

[0235] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0236] Step 1:

[0237] The server accesses all pages of a website using a web resource acquisition device and collects HTML data. Specifically, it sends HTTP requests using the requests library and parses the resulting HTML using BeautifulSoup. The input is the URL of the web page, and the output is a dataset containing the parsed product information and campaign details. This data is stored in a database as an information set for later comparison.

[0238] Step 2:

[0239] The user creates a new web page. They upload this new page to the system via a terminal. The specific action involves specifying the HTML file for the new page and executing the upload operation. The input is the new page file on the user's local disk, and the output is a notification that the transfer to the server is complete.

[0240] Step 3:

[0241] The terminal converts the format of the uploaded new page into a format that can be easily processed by the server's comparison engine. Specifically, it performs formatting unification processing using XSLT transformations and regular expressions. The input is the original HTML data of the new page, and the output is the formatted data.

[0242] Step 4:

[0243] The server uses a generative AI model to compare the formatted new page data with existing data in the information set in detail. The generative AI model evaluates inconsistencies based on the given prompt sentences. The input is the formatted data and a dataset from the information set, and the output is a list of inconsistencies in that information. SpaCy or NLTK are used for natural language processing.

[0244] Step 5:

[0245] The server analyzes inconsistencies in the comparison results and notifies the user of the specific discrepancies. Specifically, it sends emails or displays the results on a dashboard in the user interface. The input is a list of inconsistencies, and the output is a detailed notification message provided to the user.

[0246] Through this process, servers, terminals, and users cooperate to achieve highly information-consistent website management.

[0247] (Application Example 1)

[0248] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0249] Inconsistencies in promotional information within electronic payment services can lead users to act based on misinformation. Such inconsistencies damage customer trust and reduce service satisfaction. A system that can ensure real-time consistency is needed.

[0250] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0251] In this invention, the server includes means for storing digital information acquired by information gathering means in a data bank, means for comparing new digital data with information in an existing data bank to detect data inconsistencies, and means for notifying the user of the detected inconsistencies through an interface. This enables real-time verification of the consistency of promotional information in electronic payment services, allowing users to make immediate corrections.

[0252] "Information gathering means" refers to technologies or devices for efficiently collecting digital information and storing it in a data bank.

[0253] A "data bank" is a computer system that systematically stores collected digital information, making it easily searchable and retrievable.

[0254] "New digital data" refers to new information not included in existing data banks, and specifically includes promotional information related to electronic payment services.

[0255] "Inconsistency" is a term that describes a state in which the expected consistency is lacking between digital data.

[0256] An "interface" refers to the point of contact or means by which a user and a system exchange information, and its primary role is to notify the user of information.

[0257] "Machine learning technology" is a technique that automatically learns patterns and insights from data to make future predictions and decisions.

[0258] "Real-time" refers to processing or responding immediately, indicating minimal time delay.

[0259] The system implementing this invention consists of a server, a terminal, and a user. The server collects all digital information related to the electronic payment service using information gathering means and stores it in a data bank. This data bank is built using a database system such as PostgreSQL, enabling efficient storage, retrieval, and management of information. The server uses machine learning techniques such as TensorFlow and PyTorch to convert new digital data into a processable format, thereby performing detailed comparisons with existing data.

[0260] The terminal receives instructions from the server and notifies the user in real time of any detected inconsistencies. A smartphone application is used as the interface, allowing the user to quickly correct the digital content based on the information provided. Specifically, if distorted promotional information is discovered, the user receives a notification and is required to immediately correct the information.

[0261] In this way, servers, terminals, and users work together to ensure the integrity of information in real time, thereby improving reliability and user experience in electronic payment services.

[0262] A concrete example is when a cashback campaign is launched for a certain service, and information inconsistencies are discovered. In this case, the server detects the information inconsistencies, and the terminal notifies the user. The user can then quickly correct the product information with incorrect pricing or conditions, enabling the provision of accurate information to customers.

[0263] An example of a prompt for a generative AI model would be: "Verify the consistency of the new promotional information and compare it with the information in the database to identify all inconsistencies." This prompt allows the model to perform accurate comparisons and detections.

[0264] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0265] Step 1:

[0266] The server collects all digital information related to electronic payment services from the web. This information includes campaign details and promotional content. The server uses scraping techniques to retrieve this information and stores it in a PostgreSQL database. The input is HTML data from web pages, and the output is structured data stored in the database.

[0267] Step 2:

[0268] The server converts newly added digital data into a format that can be processed using natural language processing (generative AI model). The server analyzes the underlying patterns in the data using TensorFlow and unifies them into a conventional data format. The input is new digital data, and the output is data in a unified format.

[0269] Step 3:

[0270] The server compares new data in a unified format with information from existing databases to detect data inconsistencies. In this process, the server computationally analyzes the differences between the data to identify which information is inconsistent. The input consists of new and existing data in a unified format, and the output is the detected inconsistencies.

[0271] Step 4:

[0272] The server notifies the user's terminal of the detected inconsistencies. The terminal receives this information and prepares an interface to present it to the user. The input is the inconsistency information, and the output is the notification message presented to the user.

[0273] Step 5:

[0274] The user corrects the digital content based on the inconsistency information received from their device. The user then sends the corrected information back to the server via their device, updating the database. The input is the digital data corrected based on the inconsistency information, and the output is the database updated with the latest accurate data.

[0275] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0276] This invention provides an advanced integrity checking system that takes user sentiment into account in the information management process of a website. This system operates through the cooperation of the server, terminal, user, and sentiment engine.

[0277] The server crawls and collects information from the entire website and stores that data in a database. The collected information includes detailed content for each page, preparing it for later comparison processing. The terminal receives newly created pages from users and converts the data into an existing format. This newly supplied data is intended to facilitate comparison with the existing database.

[0278] The emotion engine is built in to recognize the user's emotions at the notification stage, determining emotions through AI-based analysis of the user's facial expressions, voice, or text. Leveraging this emotion information, the server can deliver inconsistent notifications tailored to the user's emotional state. For example, if the user is stressed, the content and tone of the notification can be softened, providing more supportive messaging.

[0279] The server uses AI to thoroughly verify any inconsistencies between new pages and existing data. Any detected inconsistencies are notified to the user. The sentiment engine considers the user's current emotional state to select the appropriate notification method and content. The user is presented with specific details of the inconsistency, along with suggested corrections tailored to their emotional state.

[0280] For example, if a user mistakenly enters the wrong price for product A on a different page, the server will detect this inconsistency. If the emotion engine determines that the user is anxious, it will add a helpful comment such as, "Please take your time to check," as a notification. In this way, dynamic responses that take user emotions into account enable efficient and human-centered information management and correction.

[0281] The following describes the processing flow.

[0282] Step 1:

[0283] The server crawls all pages within the website and collects information for each page. The collected information is parsed from HTML and details of products and campaigns are stored in the database.

[0284] Step 2:

[0285] The user creates a new page and uploads its information to the terminal for consistency checking. The user performs a final check on the design and content and makes it problem-free.

[0286] Step 3:

[0287] The terminal receives the data of the new page and converts it into a format that can be processed by the server. This conversion enables the new data to be smoothly compared with the existing information.

[0288] Step 4:

[0289] The server utilizes the AI engine to compare the information of the new page with the existing database information. The AI identifies areas where contradictions exist and generates detailed comparison results.

[0290] Step 5:

[0291] The emotion engine analyzes the user's reaction and determines the current emotional state from voice, expressions, and the input text. This information is taken into account during notification.

[0292] Step 6:

[0293] The server notifies the user of the detected contradictions, but customizes the notification content based on the analysis results of the emotion engine. For example, if the user is detected to be in an unstable state, the notification message includes encouragement and suggestions.

[0294] Step 7:

[0295] The user reviews the received notification, checks for inconsistencies, and corrects them. After correction, the user performs another consistency check to confirm that all inconsistencies have been resolved and then prepares for publication.

[0296] (Example 2)

[0297] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0298] In the field of modern information processing, many websites handle vast amounts of information, making it crucial to maintain information consistency. In particular, ensuring that newly added information does not contradict existing information is difficult, which can inconvenience users. Furthermore, the method of information delivery needs to be optimized based on the user's emotions and circumstances. Traditional systems lack sufficient methods to simultaneously consider information consistency and the user's emotional state, highlighting the need for improved user experience.

[0299] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0300] In this invention, the server includes means for acquiring all content information within a web resource identifier and storing it in a data set; means for comparing information of new content with information in an existing data set and detecting inconsistencies in the information; and means for analyzing the emotional state using artificial intelligence and notifying the user of the inconsistencies in a manner that corresponds to the user's emotions. This makes it possible to improve the user experience while maintaining the integrity of the information.

[0301] A "web resource identifier" is a sequence of symbols used to point to specific web content, commonly known as a URL.

[0302] "All content information" refers to all data and metadata contained in a web resource, and its content includes text, images, videos, scripts, etc.

[0303] "Data aggregate" refers to a digital storage location where the collected information is stored organizationally, and usually a database plays this role.

[0304] "New content" refers to newly added information that is not yet included in the existing data aggregate.

[0305] "Inconsistency" means a contradiction or non - matching point that occurs between two or more data sets, indicating an error or contradiction in information.

[0306] "Artificial intelligence" refers to a technology that imitates human intellectual activities by a computer system, and usually includes technologies such as machine learning and natural language processing.

[0307] "Emotional state" refers to the emotion that a user experiences or expresses at a specific moment, and may be judged by expressions, voices, or languages.

[0308] "Notification" refers to the act of transmitting important information or alerts to a target, and is especially a means of informing the recipient of information about points that require contradictions or corrections.

[0309] This invention is a system in which a server, a terminal, a user, and artificial intelligence technology cooperate in the information management process of web resources. The server collects all content information within the identifier of the web resource and stores this information in a data aggregate. Specifically, it crawls web pages, analyzes various data such as text, images, videos, etc., and stores them in a database. General server hardware and software specialized for crawling and data analysis are used for this process.

[0310] The terminal is responsible for receiving new information added by the user and converting it into a unified format. When a user edits a web page via the terminal, the entered data is converted into a standard format so that the server can easily parse it later. A dedicated data format conversion tool is used for this process.

[0311] Meanwhile, the emotion engine uses a generative AI model to analyze the user's emotional state in real time. This analysis is based on facial expressions, voice, and text data acquired from the device. The AI ​​model is a crucial technology for providing feedback based on emotional state, and if the user is feeling down, for example, it will choose calmer words to respond with.

[0312] For example, if a user mistakenly enters inconsistent product pricing information on different pages, the server will detect this discrepancy. If the sentiment engine determines the user is anxious, the server will notify them with a suggestion to "take your time to check." This dynamic response allows the user to calmly correct their error.

[0313] By utilizing generative AI models, the user experience can be improved, and information consistency can be managed more efficiently. A concrete example of a prompt might be, "When user A receives inconsistent product information while in a state of panic, what follow-up message should be provided?"

[0314] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0315] Step 1:

[0316] The server receives a web resource identifier (URL) as input. The server then initiates an automated crawling process, collecting all content information within the specified scope. This includes HTML documents, images, videos, and other relevant metadata. The server parses this information, converts it into a structured format, and stores it in a data collection.

[0317] Step 2:

[0318] The terminal receives new content created or edited by the user as input. The terminal converts this information into a unified format that is easy for the server to parse. This conversion process includes correcting markup and normalizing data as needed. The output is sent to the server as formatted data.

[0319] Step 3:

[0320] The server compares newly submitted content with existing data sets. Using the new and existing data as input, it checks the consistency of each data field. The server detects differences and inconsistencies and outputs the results as a list.

[0321] Step 4:

[0322] The generative AI model uses facial expressions, voice, or text acquired from the device as input data to analyze the user's emotional state. The model determines the user's emotional state and outputs the analysis results to the server.

[0323] Step 5:

[0324] The server receives a list of inconsistencies and the results of an emotional state analysis from a generating AI model as input. The server generates sentiment-based messaging to optimize notifications to the user. For example, if the server determines that the user is anxious, it will send a notification such as, "Please check again when you are not in a hurry."

[0325] Step 6:

[0326] The user receives a notification from the server, reviews the presented information, and makes corrections. The user then creates new data and sends it back to the server via their device. This feedback loop improves accuracy and efficiency, and maintains the integrity of the information.

[0327] (Application Example 2)

[0328] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0329] In information management, inconsistencies can arise when users handle large amounts of information. These inconsistencies often occur due to unintended errors or input mistakes, and if they persist, they can lead to a deterioration of the user experience and a decrease in the reliability of the information. Furthermore, traditional notification systems send uniform messages without considering the user's perception, which can cause stress to users. Therefore, a system that can flexibly respond according to the user's perception is needed.

[0330] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0331] In this invention, the server includes means for acquiring all data within a collection of information and storing it in a recording device, means for comparing new data with data in an existing recording device and detecting inconsistencies in the information, means for notifying the user of the detected inconsistencies, and means for determining the user's awareness state and adjusting the notification content according to that awareness state. This makes it possible to quickly detect inconsistencies and notify appropriately, and to perform messaging that takes into account the user's awareness state.

[0332] A "collection of information" is the totality of data collected according to certain criteria.

[0333] A "recording device" refers to hardware or software that has the function of accumulating and storing data.

[0334] "New data" refers to data that was not previously available and has been newly generated or acquired.

[0335] "Existing recording device" refers to data storage where information is stored in advance.

[0336] "Inconsistency" refers to a situation where different pieces of information do not match or exhibit contradictions.

[0337] "User" refers to an individual who operates or uses the system.

[0338] "Perceptual state" refers to the state of awareness and emotions that a user exhibits in relation to information.

[0339] "Notification content" refers to the specific information used to communicate detected information or messages to the user.

[0340] "Means of adjustment" refer to processes or functions for changing methods or content according to the situation or conditions.

[0341] The system for realizing this invention consists of a server, a terminal, and a user. The server is responsible for first acquiring all the data within the information collection and storing it in a recording device. This allows all data to be centrally managed and prepared for subsequent comparison processing.

[0342] The terminal is responsible for the process of comparing new data with data from existing recording devices and detecting inconsistencies. This process uses programming languages ​​such as Python and JavaScript to perform efficient data calculations. Furthermore, it utilizes artificial intelligence and software tools such as Google Cloud Vision API and Amazon Rekognition to convert the information into a processable format.

[0343] The user's perception state is determined by the emotion engine. The emotion engine uses the camera and microphone of the smartphone or smart glasses to analyze the user's facial expressions and voice. This information is used to adjust the content of notifications. Any detected inconsistencies are appropriately notified to the user from the server by the program and provided in a way that allows the user to take action based on their perception state.

[0344] As a concrete example, consider a scenario where a user accidentally enters airline ticket price information while creating content for a travel blog. The emotion engine would assess the user's state of mind and provide a gentle notification along with advice such as, "Next time, please double-check the price information." An example of a prompt to input into the generative AI model would be, "If there is a contradiction in the user's input, please provide advice that takes the user's emotions into consideration. Choose the emotion closest to the user's from relaxed, stressed, happy, or disappointed, and provide a notification with content appropriate to that emotion."

[0345] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0346] Step 1:

[0347] The server uses a web crawler to collect all the data within a collection of information and stores it in a storage device. The input is web page information, and the output is structured data stored in a database. When stored in the database, duplicate and unnecessary information is filtered out, and the data is processed to enable efficient searching and comparison.

[0348] Step 2:

[0349] The terminal receives new data entered by the user and compares it with data from an existing recording device. Here, the input is the new data provided by the user, and the output is information indicating data inconsistencies. The comparison process uses algorithms to check for data matches and mismatches. This process typically utilizes Python or JavaScript programs.

[0350] Step 3:

[0351] To recognize the user's emotions, the device utilizes the camera and microphone of a smartphone or smart glasses, and an emotion engine analyzes the user's facial expressions and voice. The input is real-time image or audio data of the user, and the output is the user's emotional state. This analysis uses the Google Cloud Vision API and Amazon Rekognition to perform data feature extraction and recognition using machine learning models.

[0352] Step 4:

[0353] The server considers inconsistencies and the user's emotional state, generates an appropriate notification, and sends it to the user via the terminal. The input is the inconsistent content and emotional state data, and the output is a notification message with adjusted content. Using a generation AI model, the message is generated based on the prompt statement: "If there are inconsistencies in the user's input, please provide advice that takes the user's emotions into consideration. Choose the emotion closest to relaxed, tense, happy, or disappointed, and notify the user with content appropriate to that emotion."

[0354] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0355] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0356] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0357] [Third Embodiment]

[0358] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0359] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0360] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0361] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0362] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0363] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0364] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0365] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0366] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0367] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0368] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0369] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0370] This invention relates to a website management system for ensuring the integrity of information. Its purpose is to prevent information inconsistencies by having the server, terminal, and user cooperate in collecting, verifying, and notifying information.

[0371] The server first collects information from all pages of the website. This involves accessing each page and extracting necessary elements such as product information and campaign details from the HTML data. The extracted information is stored in a database for efficient searching and comparison.

[0372] Next, the user uploads the newly created page to their device, preparing it for consistency checks before publication. The device converts the new page's format into one that the server's comparison engine can easily process. This conversion enables accurate comparisons even between different formats.

[0373] The server uses AI to meticulously compare new pages with information in existing databases. The AI ​​employs natural language processing and pattern recognition to detect subtle inconsistencies and potential errors. For example, it generates alerts when specific inconsistencies are found, such as differing prices or discrepancies in product descriptions.

[0374] This alert is sent from the server to the user. The notification includes specific figures and explanations of which information is inconsistent, allowing the user to quickly identify the error and make the necessary corrections. It is expected that automating this process in daily operations will significantly reduce the rate of information errors.

[0375] This invention enables a system that can always provide users with the latest and most consistent information, even on websites containing complex information.

[0376] The following describes the processing flow.

[0377] Step 1:

[0378] The server crawls every page of the entire website. This retrieves HTML data, including all product information and campaign details. The retrieved information is then analyzed using appropriate analytics tools to extract and organize specific data.

[0379] Step 2:

[0380] The user creates the content for a new page and prepares to upload the data to their device for consistency checking. The user then checks the page's completeness and visually reviews it for errors.

[0381] Step 3:

[0382] The terminal receives the new page and converts its contents into the server's database format. This prepares the information on the new page in a format that can be directly compared with existing data. Format compatibility is ensured at this stage.

[0383] Step 4:

[0384] The server uses an AI engine to compare new page data with existing database information. The AI ​​quickly calculates text similarity and numerical data matches, identifying different elements. In this process, the server identifies subtle differences between pages and detects potential inconsistencies.

[0385] Step 5:

[0386] If the server detects a discrepancy, it will notify the user of the details as an alert. The notification will include specific numbers and text indicating which part is inconsistent.

[0387] Step 6:

[0388] Users review the alert content and re-evaluate the information on the new page. They can then modify the interface as needed and upload it again to the server via their device, allowing for a rapid consistency check cycle.

[0389] (Example 1)

[0390] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0391] In modern web systems, the accumulation of complex and vast amounts of information increases the likelihood of inconsistencies and errors. This problem can impair the user experience and even reduce reliability. Current systems lack efficient means to detect and correct information inconsistencies, necessitating automated methods to ensure information integrity.

[0392] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0393] In this invention, the server includes means for collecting information using a web resource acquisition device and storing it in an information set, means for comparing new resources with existing information in the information set and identifying information inconsistencies, and means for analyzing the details of the inconsistencies using a generative model and notifying the user. This enables a process for automatically detecting and quickly correcting information inconsistencies.

[0394] A "web resource acquisition device" is a technological device that automatically accesses web content on the internet and collects necessary data.

[0395] An "information collection" is a database or data storage system used to store and manage collected data and information.

[0396] "Inconsistency" refers to contradictions or discrepancies that arise between newly added information and existing information, and is a factor that undermines the reliability of the data.

[0397] A "generative model" is an artificial intelligence technology that analyzes and learns from large amounts of data to generate or analyze new information based on specific patterns or rules.

[0398] "User" refers to an individual or organization that operates this system to collect, verify, or modify information.

[0399] This invention provides a system for ensuring information integrity, and functions effectively through the cooperation of a server, terminal, and user. Specifically, the server automatically accesses web content on the internet using a web resource acquisition device, collects the necessary data, and stores it appropriately in an information collection. In this process, the server efficiently processes the data using the requests library and analysis tools such as BeautifulSoup.

[0400] Next, users can upload newly created pages to the system via their terminals, allowing for verification of information integrity. The terminals then convert these new pages, formatting them into a format that the server's comparison engine can match against existing company information. This may involve using XSLT transformations or regular expressions.

[0401] Furthermore, the server leverages generative models to thoroughly examine inconsistencies between new pages and existing data within the information set. In particular, it utilizes spaCy and NLTK to apply natural language processing and extract data discrepancies. The server uses the generative AI model to send prompts such as: "Analyze the content of the new page and compare it to the information set to identify inconsistencies."

[0402] Finally, based on the identified inconsistencies, the server uses a generative model to analyze the details of the inconsistencies and notify the user appropriately. For example, by reporting specific details of the information inconsistencies via email notifications or pop-ups on the dashboard, users can quickly take corrective action. This method ensures the reliability and consistency of the information.

[0403] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0404] Step 1:

[0405] The server accesses all pages of a website using a web resource acquisition device and collects HTML data. Specifically, it sends HTTP requests using the requests library and parses the resulting HTML using BeautifulSoup. The input is the URL of the web page, and the output is a dataset containing the parsed product information and campaign details. This data is stored in a database as an information set for later comparison.

[0406] Step 2:

[0407] The user creates a new web page. They upload this new page to the system via a terminal. The specific action involves specifying the HTML file for the new page and executing the upload operation. The input is the new page file on the user's local disk, and the output is a notification that the transfer to the server is complete.

[0408] Step 3:

[0409] The terminal converts the format of the uploaded new page into a format that can be easily processed by the server's comparison engine. Specifically, it performs formatting unification processing using XSLT transformations and regular expressions. The input is the original HTML data of the new page, and the output is the formatted data.

[0410] Step 4:

[0411] The server uses a generative AI model to compare the formatted new page data with existing data in the information set in detail. The generative AI model evaluates inconsistencies based on the given prompt sentences. The input is the formatted data and a dataset from the information set, and the output is a list of inconsistencies in that information. SpaCy or NLTK are used for natural language processing.

[0412] Step 5:

[0413] The server analyzes inconsistencies in the comparison results and notifies the user of the specific discrepancies. Specifically, it sends emails or displays the results on a dashboard in the user interface. The input is a list of inconsistencies, and the output is a detailed notification message provided to the user.

[0414] Through this process, servers, terminals, and users cooperate to achieve highly information-consistent website management.

[0415] (Application Example 1)

[0416] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0417] Inconsistencies in promotional information within electronic payment services can lead users to act based on misinformation. Such inconsistencies damage customer trust and reduce service satisfaction. A system that can ensure real-time consistency is needed.

[0418] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0419] In this invention, the server includes means for storing digital information acquired by information gathering means in a data bank, means for comparing new digital data with information in an existing data bank to detect data inconsistencies, and means for notifying the user of the detected inconsistencies through an interface. This enables real-time verification of the consistency of promotional information in electronic payment services, allowing users to make immediate corrections.

[0420] "Information gathering means" refers to technologies or devices for efficiently collecting digital information and storing it in a data bank.

[0421] A "data bank" is a computer system that systematically stores collected digital information, making it easily searchable and retrievable.

[0422] "New digital data" refers to new information not included in existing data banks, and specifically includes promotional information related to electronic payment services.

[0423] "Inconsistency" is a term that describes a state in which the expected consistency is lacking between digital data.

[0424] An "interface" refers to the point of contact or means by which a user and a system exchange information, and its primary role is to notify the user of information.

[0425] "Machine learning technology" is a technique that automatically learns patterns and insights from data to make future predictions and decisions.

[0426] "Real-time" refers to processing or responding immediately, indicating minimal time delay.

[0427] The system implementing this invention consists of a server, a terminal, and a user. The server collects all digital information related to the electronic payment service using information gathering means and stores it in a data bank. This data bank is built using a database system such as PostgreSQL, enabling efficient storage, retrieval, and management of information. The server uses machine learning techniques such as TensorFlow and PyTorch to convert new digital data into a processable format, thereby performing detailed comparisons with existing data.

[0428] The terminal receives instructions from the server and notifies the user in real time of any detected inconsistencies. A smartphone application is used as the interface, allowing the user to quickly correct the digital content based on the information provided. Specifically, if distorted promotional information is discovered, the user receives a notification and is required to immediately correct the information.

[0429] In this way, servers, terminals, and users work together to ensure the integrity of information in real time, thereby improving reliability and user experience in electronic payment services.

[0430] A concrete example is when a cashback campaign is launched for a certain service, and information inconsistencies are discovered. In this case, the server detects the information inconsistencies, and the terminal notifies the user. The user can then quickly correct the product information with incorrect pricing or conditions, enabling the provision of accurate information to customers.

[0431] An example of a prompt for a generative AI model would be: "Verify the consistency of the new promotional information and compare it with the information in the database to identify all inconsistencies." This prompt allows the model to perform accurate comparisons and detections.

[0432] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0433] Step 1:

[0434] The server collects all digital information related to electronic payment services from the web. This information includes campaign details and promotional content. The server uses scraping techniques to retrieve this information and stores it in a PostgreSQL database. The input is HTML data from web pages, and the output is structured data stored in the database.

[0435] Step 2:

[0436] The server converts newly added digital data into a format that can be processed using natural language processing (generative AI model). The server analyzes the underlying patterns in the data using TensorFlow and unifies them into a conventional data format. The input is new digital data, and the output is data in a unified format.

[0437] Step 3:

[0438] The server compares new data in a unified format with information from existing databases to detect data inconsistencies. In this process, the server computationally analyzes the differences between the data to identify which information is inconsistent. The input consists of new and existing data in a unified format, and the output is the detected inconsistencies.

[0439] Step 4:

[0440] The server notifies the user's terminal of the detected inconsistencies. The terminal receives this information and prepares an interface to present it to the user. The input is the inconsistency information, and the output is the notification message presented to the user.

[0441] Step 5:

[0442] The user corrects the digital content based on the inconsistency information received from their device. The user then sends the corrected information back to the server via their device, updating the database. The input is the digital data corrected based on the inconsistency information, and the output is the database updated with the latest accurate data.

[0443] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0444] This invention provides an advanced integrity checking system that takes user sentiment into account in the information management process of a website. This system operates through the cooperation of the server, terminal, user, and sentiment engine.

[0445] The server crawls and collects information from the entire website and stores that data in a database. The collected information includes detailed content for each page, preparing it for later comparison processing. The terminal receives newly created pages from users and converts the data into an existing format. This newly supplied data is intended to facilitate comparison with the existing database.

[0446] The emotion engine is built in to recognize the user's emotions at the notification stage, determining emotions through AI-based analysis of the user's facial expressions, voice, or text. Leveraging this emotion information, the server can deliver inconsistent notifications tailored to the user's emotional state. For example, if the user is stressed, the content and tone of the notification can be softened, providing more supportive messaging.

[0447] The server uses AI to thoroughly verify any inconsistencies between new pages and existing data. Any detected inconsistencies are notified to the user. The sentiment engine considers the user's current emotional state to select the appropriate notification method and content. The user is presented with specific details of the inconsistency, along with suggested corrections tailored to their emotional state.

[0448] For example, if a user mistakenly enters the wrong price for product A on a different page, the server will detect this inconsistency. If the emotion engine determines that the user is anxious, it will add a helpful comment such as, "Please take your time to check," as a notification. In this way, dynamic responses that take user emotions into account enable efficient and human-centered information management and correction.

[0449] The following describes the processing flow.

[0450] Step 1:

[0451] The server crawls all pages on the website and collects information from each page. The collected information is parsed from the HTML and stored in a database with product and campaign details.

[0452] Step 2:

[0453] The user creates a new page and uploads the information to their device for consistency checking. The user then performs a final review of the design and content to ensure there are no problems.

[0454] Step 3:

[0455] The device receives data from the new page and converts it into a format that can be processed by the server. This conversion allows the new data to be smoothly compared with existing information.

[0456] Step 4:

[0457] The server uses an AI engine to compare information from new pages with existing database information. The AI ​​identifies inconsistencies and generates detailed comparison results.

[0458] Step 5:

[0459] The emotion engine analyzes user responses and determines their current emotional state from voice, facial expressions, and entered text. This information is taken into consideration when issuing notifications.

[0460] Step 6:

[0461] The server notifies the user of any detected inconsistencies, but customizes the notification content based on the results of the sentiment engine's analysis. For example, if the server detects that the user is in an unstable state, it will include encouragement and suggestions in the notification message.

[0462] Step 7:

[0463] The user reviews the received notification, checks for inconsistencies, and corrects them. After correction, the user performs another consistency check to confirm that all inconsistencies have been resolved and then prepares for publication.

[0464] (Example 2)

[0465] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0466] In the field of modern information processing, many websites handle vast amounts of information, making it crucial to maintain information consistency. In particular, ensuring that newly added information does not contradict existing information is difficult, which can inconvenience users. Furthermore, the method of information delivery needs to be optimized based on the user's emotions and circumstances. Traditional systems lack sufficient methods to simultaneously consider information consistency and the user's emotional state, highlighting the need for improved user experience.

[0467] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0468] In this invention, the server includes means for acquiring all content information within a web resource identifier and storing it in a data set; means for comparing information of new content with information in an existing data set and detecting inconsistencies in the information; and means for analyzing the emotional state using artificial intelligence and notifying the user of the inconsistencies in a manner that corresponds to the user's emotions. This makes it possible to improve the user experience while maintaining the integrity of the information.

[0469] A "web resource identifier" is a sequence of symbols used to point to specific web content, commonly known as a URL.

[0470] "All content information" refers to all data and metadata contained within a web resource, including text, images, videos, scripts, and more.

[0471] A "data collection" refers to a digital storage location where collected information is systematically stored, and databases typically fulfill this role.

[0472] "New content" refers to newly added information that is not yet included in the existing data set.

[0473] "Inconsistency" refers to a point of contradiction or discrepancy that occurs between two or more datasets, indicating errors or inconsistencies in the information.

[0474] "Artificial intelligence" refers to the technology of using computer systems to imitate human intellectual activity, and typically includes technologies such as machine learning and natural language processing.

[0475] "Emotional state" refers to the emotions a user is experiencing or expressing at a particular moment, and may be determined by facial expressions, voice, and language.

[0476] "Notification" refers to the act of transmitting important information or alerts to a target audience, and is particularly a means of informing recipients of inconsistencies or points that require correction.

[0477] This invention is a system in which a server, terminal, user, and artificial intelligence technology work together in the information management process for web resources. The server collects all content information within the identifier of a web resource and stores this information in a data set. Specifically, it crawls web pages, analyzes various data such as text, images, and videos, and saves them in a database. This process uses general server hardware and software specialized for crawling and data analysis.

[0478] The terminal is responsible for receiving new information added by the user and converting it into a unified format. When a user edits a web page via the terminal, the entered data is converted into a standard format so that the server can easily parse it later. A dedicated data format conversion tool is used for this process.

[0479] Meanwhile, the emotion engine uses a generative AI model to analyze the user's emotional state in real time. This analysis is based on facial expressions, voice, and text data acquired from the device. The AI ​​model is a crucial technology for providing feedback based on emotional state, and if the user is feeling down, for example, it will choose calmer words to respond with.

[0480] For example, if a user mistakenly enters inconsistent product pricing information on different pages, the server will detect this discrepancy. If the sentiment engine determines the user is anxious, the server will notify them with a suggestion to "take your time to check." This dynamic response allows the user to calmly correct their error.

[0481] By utilizing generative AI models, the user experience can be improved, and information consistency can be managed more efficiently. A concrete example of a prompt might be, "When user A receives inconsistent product information while in a state of panic, what follow-up message should be provided?"

[0482] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0483] Step 1:

[0484] The server receives a web resource identifier (URL) as input. The server then initiates an automated crawling process, collecting all content information within the specified scope. This includes HTML documents, images, videos, and other relevant metadata. The server parses this information, converts it into a structured format, and stores it in a data collection.

[0485] Step 2:

[0486] The terminal receives new content created or edited by the user as input. The terminal converts this information into a unified format that is easy for the server to parse. This conversion process includes correcting markup and normalizing data as needed. The output is sent to the server as formatted data.

[0487] Step 3:

[0488] The server compares newly submitted content with existing data sets. Using the new and existing data as input, it checks the consistency of each data field. The server detects differences and inconsistencies and outputs the results as a list.

[0489] Step 4:

[0490] The generative AI model uses facial expressions, voice, or text acquired from the device as input data to analyze the user's emotional state. The model determines the user's emotional state and outputs the analysis results to the server.

[0491] Step 5:

[0492] The server receives a list of inconsistencies and the results of an emotional state analysis from a generating AI model as input. The server generates sentiment-based messaging to optimize notifications to the user. For example, if the server determines that the user is anxious, it will send a notification such as, "Please check again when you are not in a hurry."

[0493] Step 6:

[0494] The user receives a notification from the server, reviews the presented information, and makes corrections. The user then creates new data and sends it back to the server via their device. This feedback loop improves accuracy and efficiency, and maintains the integrity of the information.

[0495] (Application Example 2)

[0496] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0497] In information management, inconsistencies can arise when users handle large amounts of information. These inconsistencies often occur due to unintended errors or input mistakes, and if they persist, they can lead to a deterioration of the user experience and a decrease in the reliability of the information. Furthermore, traditional notification systems send uniform messages without considering the user's perception, which can cause stress to users. Therefore, a system that can flexibly respond according to the user's perception is needed.

[0498] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0499] In this invention, the server includes means for acquiring all data within a collection of information and storing it in a recording device, means for comparing new data with data in an existing recording device and detecting inconsistencies in the information, means for notifying the user of the detected inconsistencies, and means for determining the user's awareness state and adjusting the notification content according to that awareness state. This makes it possible to quickly detect inconsistencies and notify appropriately, and to perform messaging that takes into account the user's awareness state.

[0500] A "collection of information" is the totality of data collected according to certain criteria.

[0501] A "recording device" refers to hardware or software that has the function of accumulating and storing data.

[0502] "New data" refers to data that was not previously available and has been newly generated or acquired.

[0503] "Existing recording device" refers to data storage where information is stored in advance.

[0504] "Inconsistency" refers to a situation where different pieces of information do not match or exhibit contradictions.

[0505] "User" refers to an individual who operates or uses the system.

[0506] "Perceptual state" refers to the state of awareness and emotions that a user exhibits in relation to information.

[0507] "Notification content" refers to the specific information used to communicate detected information or messages to the user.

[0508] "Means of adjustment" refer to processes or functions for changing methods or content according to the situation or conditions.

[0509] The system for realizing this invention consists of a server, a terminal, and a user. The server is responsible for first acquiring all the data within the information collection and storing it in a recording device. This allows all data to be centrally managed and prepared for subsequent comparison processing.

[0510] The terminal is responsible for the process of comparing new data with data from existing recording devices and detecting inconsistencies. This process uses programming languages ​​such as Python and JavaScript to perform efficient data calculations. Furthermore, it utilizes artificial intelligence and software tools such as Google Cloud Vision API and Amazon Rekognition to convert the information into a processable format.

[0511] The user's perception state is determined by the emotion engine. The emotion engine uses the camera and microphone of the smartphone or smart glasses to analyze the user's facial expressions and voice. This information is used to adjust the content of notifications. Any detected inconsistencies are appropriately notified to the user from the server by the program and provided in a way that allows the user to take action based on their perception state.

[0512] As a concrete example, consider a scenario where a user accidentally enters airline ticket price information while creating content for a travel blog. The emotion engine would assess the user's state of mind and provide a gentle notification along with advice such as, "Next time, please double-check the price information." An example of a prompt to input into the generative AI model would be, "If there is a contradiction in the user's input, please provide advice that takes the user's emotions into consideration. Choose the emotion closest to the user's from relaxed, stressed, happy, or disappointed, and provide a notification with content appropriate to that emotion."

[0513] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0514] Step 1:

[0515] The server uses a web crawler to collect all the data within a collection of information and stores it in a storage device. The input is web page information, and the output is structured data stored in a database. When stored in the database, duplicate and unnecessary information is filtered out, and the data is processed to enable efficient searching and comparison.

[0516] Step 2:

[0517] The terminal receives new data entered by the user and compares it with data from an existing recording device. Here, the input is the new data provided by the user, and the output is information indicating data inconsistencies. The comparison process uses algorithms to check for data matches and mismatches. This process typically utilizes Python or JavaScript programs.

[0518] Step 3:

[0519] To recognize the user's emotions, the device utilizes the camera and microphone of a smartphone or smart glasses, and an emotion engine analyzes the user's facial expressions and voice. The input is real-time image or audio data of the user, and the output is the user's emotional state. This analysis uses the Google Cloud Vision API and Amazon Rekognition to perform data feature extraction and recognition using machine learning models.

[0520] Step 4:

[0521] The server considers inconsistencies and the user's emotional state, generates an appropriate notification, and sends it to the user via the terminal. The input is the inconsistent content and emotional state data, and the output is a notification message with adjusted content. Using a generation AI model, the message is generated based on the prompt statement: "If there are inconsistencies in the user's input, please provide advice that takes the user's emotions into consideration. Choose the emotion closest to relaxed, tense, happy, or disappointed, and notify the user with content appropriate to that emotion."

[0522] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0523] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0524] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0525] [Fourth Embodiment]

[0526] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0527] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0528] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0529] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0530] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0531] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0532] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0533] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0534] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0535] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0536] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0537] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0538] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0539] This invention relates to a website management system for ensuring the integrity of information. Its purpose is to prevent information inconsistencies by having the server, terminal, and user cooperate in collecting, verifying, and notifying information.

[0540] The server first collects information from all pages of the website. This involves accessing each page and extracting necessary elements such as product information and campaign details from the HTML data. The extracted information is stored in a database for efficient searching and comparison.

[0541] Next, the user uploads the newly created page to their device, preparing it for consistency checks before publication. The device converts the new page's format into one that the server's comparison engine can easily process. This conversion enables accurate comparisons even between different formats.

[0542] The server uses AI to meticulously compare new pages with information in existing databases. The AI ​​employs natural language processing and pattern recognition to detect subtle inconsistencies and potential errors. For example, it generates alerts when specific inconsistencies are found, such as differing prices or discrepancies in product descriptions.

[0543] This alert is sent from the server to the user. The notification includes specific figures and explanations of which information is inconsistent, allowing the user to quickly identify the error and make the necessary corrections. It is expected that automating this process in daily operations will significantly reduce the rate of information errors.

[0544] This invention enables a system that can always provide users with the latest and most consistent information, even on websites containing complex information.

[0545] The following describes the processing flow.

[0546] Step 1:

[0547] The server crawls every page of the entire website. This retrieves HTML data, including all product information and campaign details. The retrieved information is then analyzed using appropriate analytics tools to extract and organize specific data.

[0548] Step 2:

[0549] The user creates the content for a new page and prepares to upload the data to their device for consistency checking. The user then checks the page's completeness and visually reviews it for errors.

[0550] Step 3:

[0551] The terminal receives the new page and converts its contents into the server's database format. This prepares the information on the new page in a format that can be directly compared with existing data. Format compatibility is ensured at this stage.

[0552] Step 4:

[0553] The server uses an AI engine to compare new page data with existing database information. The AI ​​quickly calculates text similarity and numerical data matches, identifying different elements. In this process, the server identifies subtle differences between pages and detects potential inconsistencies.

[0554] Step 5:

[0555] If the server detects a discrepancy, it will notify the user of the details as an alert. The notification will include specific numbers and text indicating which part is inconsistent.

[0556] Step 6:

[0557] Users review the alert content and re-evaluate the information on the new page. They can then modify the interface as needed and upload it again to the server via their device, allowing for a rapid consistency check cycle.

[0558] (Example 1)

[0559] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0560] In modern web systems, the accumulation of complex and vast amounts of information increases the likelihood of inconsistencies and errors. This problem can impair the user experience and even reduce reliability. Current systems lack efficient means to detect and correct information inconsistencies, necessitating automated methods to ensure information integrity.

[0561] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0562] In this invention, the server includes means for collecting information using a web resource acquisition device and storing it in an information set, means for comparing new resources with existing information in the information set and identifying information inconsistencies, and means for analyzing the details of the inconsistencies using a generative model and notifying the user. This enables a process for automatically detecting and quickly correcting information inconsistencies.

[0563] A "web resource acquisition device" is a technological device that automatically accesses web content on the internet and collects necessary data.

[0564] An "information collection" is a database or data storage system used to store and manage collected data and information.

[0565] "Inconsistency" refers to contradictions or discrepancies that arise between newly added information and existing information, and is a factor that undermines the reliability of the data.

[0566] A "generative model" is an artificial intelligence technology that analyzes and learns from large amounts of data to generate or analyze new information based on specific patterns or rules.

[0567] "User" refers to an individual or organization that operates this system to collect, verify, or modify information.

[0568] This invention provides a system for ensuring information integrity, and functions effectively through the cooperation of a server, terminal, and user. Specifically, the server automatically accesses web content on the internet using a web resource acquisition device, collects the necessary data, and stores it appropriately in an information collection. In this process, the server efficiently processes the data using the requests library and analysis tools such as BeautifulSoup.

[0569] Next, users can upload newly created pages to the system via their terminals, allowing for verification of information integrity. The terminals then convert these new pages, formatting them into a format that the server's comparison engine can match against existing company information. This may involve using XSLT transformations or regular expressions.

[0570] Furthermore, the server leverages generative models to thoroughly examine inconsistencies between new pages and existing data within the information set. In particular, it utilizes spaCy and NLTK to apply natural language processing and extract data discrepancies. The server uses the generative AI model to send prompts such as: "Analyze the content of the new page and compare it to the information set to identify inconsistencies."

[0571] Finally, based on the identified inconsistencies, the server uses a generative model to analyze the details of the inconsistencies and notify the user appropriately. For example, by reporting specific details of the information inconsistencies via email notifications or pop-ups on the dashboard, users can quickly take corrective action. This method ensures the reliability and consistency of the information.

[0572] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0573] Step 1:

[0574] The server accesses all pages of a website using a web resource acquisition device and collects HTML data. Specifically, it sends HTTP requests using the requests library and parses the resulting HTML using BeautifulSoup. The input is the URL of the web page, and the output is a dataset containing the parsed product information and campaign details. This data is stored in a database as an information set for later comparison.

[0575] Step 2:

[0576] The user creates a new web page. They upload this new page to the system via a terminal. The specific action involves specifying the HTML file for the new page and executing the upload operation. The input is the new page file on the user's local disk, and the output is a notification that the transfer to the server is complete.

[0577] Step 3:

[0578] The terminal converts the format of the uploaded new page into a format that can be easily processed by the server's comparison engine. Specifically, it performs formatting unification processing using XSLT transformations and regular expressions. The input is the original HTML data of the new page, and the output is the formatted data.

[0579] Step 4:

[0580] The server uses a generative AI model to compare the formatted new page data with existing data in the information set in detail. The generative AI model evaluates inconsistencies based on the given prompt sentences. The input is the formatted data and a dataset from the information set, and the output is a list of inconsistencies in that information. SpaCy or NLTK are used for natural language processing.

[0581] Step 5:

[0582] The server analyzes inconsistencies in the comparison results and notifies the user of the specific discrepancies. Specifically, it sends emails or displays the results on a dashboard in the user interface. The input is a list of inconsistencies, and the output is a detailed notification message provided to the user.

[0583] Through this process, servers, terminals, and users cooperate to achieve highly information-consistent website management.

[0584] (Application Example 1)

[0585] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0586] Inconsistencies in promotional information within electronic payment services can lead users to act based on misinformation. Such inconsistencies damage customer trust and reduce service satisfaction. A system that can ensure real-time consistency is needed.

[0587] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0588] In this invention, the server includes means for storing digital information acquired by information gathering means in a data bank, means for comparing new digital data with information in an existing data bank to detect data inconsistencies, and means for notifying the user of the detected inconsistencies through an interface. This enables real-time verification of the consistency of promotional information in electronic payment services, allowing users to make immediate corrections.

[0589] "Information gathering means" refers to technologies or devices for efficiently collecting digital information and storing it in a data bank.

[0590] A "data bank" is a computer system that systematically stores collected digital information, making it easily searchable and retrievable.

[0591] "New digital data" refers to new information not included in existing data banks, and specifically includes promotional information related to electronic payment services.

[0592] "Inconsistency" is a term that describes a state in which the expected consistency is lacking between digital data.

[0593] An "interface" refers to the point of contact or means by which a user and a system exchange information, and its primary role is to notify the user of information.

[0594] "Machine learning technology" is a technique that automatically learns patterns and insights from data to make future predictions and decisions.

[0595] "Real-time" refers to processing or responding immediately, indicating minimal time delay.

[0596] The system implementing this invention consists of a server, a terminal, and a user. The server collects all digital information related to the electronic payment service using information gathering means and stores it in a data bank. This data bank is built using a database system such as PostgreSQL, enabling efficient storage, retrieval, and management of information. The server uses machine learning techniques such as TensorFlow and PyTorch to convert new digital data into a processable format, thereby performing detailed comparisons with existing data.

[0597] The terminal receives instructions from the server and notifies the user in real time of any detected inconsistencies. A smartphone application is used as the interface, allowing the user to quickly correct the digital content based on the information provided. Specifically, if distorted promotional information is discovered, the user receives a notification and is required to immediately correct the information.

[0598] In this way, servers, terminals, and users work together to ensure the integrity of information in real time, thereby improving reliability and user experience in electronic payment services.

[0599] A concrete example is when a cashback campaign is launched for a certain service, and information inconsistencies are discovered. In this case, the server detects the information inconsistencies, and the terminal notifies the user. The user can then quickly correct the product information with incorrect pricing or conditions, enabling the provision of accurate information to customers.

[0600] An example of a prompt for a generative AI model would be: "Verify the consistency of the new promotional information and compare it with the information in the database to identify all inconsistencies." This prompt allows the model to perform accurate comparisons and detections.

[0601] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0602] Step 1:

[0603] The server collects all digital information related to electronic payment services from the web. This information includes campaign details and promotional content. The server uses scraping techniques to retrieve this information and stores it in a PostgreSQL database. The input is HTML data from web pages, and the output is structured data stored in the database.

[0604] Step 2:

[0605] The server converts newly added digital data into a format that can be processed using natural language processing (generative AI model). The server analyzes the underlying patterns in the data using TensorFlow and unifies them into a conventional data format. The input is new digital data, and the output is data in a unified format.

[0606] Step 3:

[0607] The server compares new data in a unified format with information from existing databases to detect data inconsistencies. In this process, the server computationally analyzes the differences between the data to identify which information is inconsistent. The input consists of new and existing data in a unified format, and the output is the detected inconsistencies.

[0608] Step 4:

[0609] The server notifies the user's terminal of the detected inconsistencies. The terminal receives this information and prepares an interface to present it to the user. The input is the inconsistency information, and the output is the notification message presented to the user.

[0610] Step 5:

[0611] The user corrects the digital content based on the inconsistency information received from their device. The user then sends the corrected information back to the server via their device, updating the database. The input is the digital data corrected based on the inconsistency information, and the output is the database updated with the latest accurate data.

[0612] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0613] This invention provides an advanced integrity checking system that takes user sentiment into account in the information management process of a website. This system operates through the cooperation of the server, terminal, user, and sentiment engine.

[0614] The server crawls and collects information from the entire website and stores that data in a database. The collected information includes detailed content for each page, preparing it for later comparison processing. The terminal receives newly created pages from users and converts the data into an existing format. This newly supplied data is intended to facilitate comparison with the existing database.

[0615] The emotion engine is built in to recognize the user's emotions at the notification stage, determining emotions through AI-based analysis of the user's facial expressions, voice, or text. Leveraging this emotion information, the server can deliver inconsistent notifications tailored to the user's emotional state. For example, if the user is stressed, the content and tone of the notification can be softened, providing more supportive messaging.

[0616] The server uses AI to thoroughly verify any inconsistencies between new pages and existing data. Any detected inconsistencies are notified to the user. The sentiment engine considers the user's current emotional state to select the appropriate notification method and content. The user is presented with specific details of the inconsistency, along with suggested corrections tailored to their emotional state.

[0617] For example, if a user mistakenly enters the wrong price for product A on a different page, the server will detect this inconsistency. If the emotion engine determines that the user is anxious, it will add a helpful comment such as, "Please take your time to check," as a notification. In this way, dynamic responses that take user emotions into account enable efficient and human-centered information management and correction.

[0618] The following describes the processing flow.

[0619] Step 1:

[0620] The server crawls all pages on the website and collects information from each page. The collected information is parsed from the HTML and stored in a database with product and campaign details.

[0621] Step 2:

[0622] The user creates a new page and uploads the information to their device for consistency checking. The user then performs a final review of the design and content to ensure there are no problems.

[0623] Step 3:

[0624] The device receives data from the new page and converts it into a format that can be processed by the server. This conversion allows the new data to be smoothly compared with existing information.

[0625] Step 4:

[0626] The server uses an AI engine to compare information from new pages with existing database information. The AI ​​identifies inconsistencies and generates detailed comparison results.

[0627] Step 5:

[0628] The emotion engine analyzes user responses and determines their current emotional state from voice, facial expressions, and entered text. This information is taken into consideration when issuing notifications.

[0629] Step 6:

[0630] The server notifies the user of any detected inconsistencies, but customizes the notification content based on the results of the sentiment engine's analysis. For example, if the server detects that the user is in an unstable state, it will include encouragement and suggestions in the notification message.

[0631] Step 7:

[0632] The user reviews the received notification, checks for inconsistencies, and corrects them. After correction, the user performs another consistency check to confirm that all inconsistencies have been resolved and then prepares for publication.

[0633] (Example 2)

[0634] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0635] In the field of modern information processing, many websites handle vast amounts of information, making it crucial to maintain information consistency. In particular, ensuring that newly added information does not contradict existing information is difficult, which can inconvenience users. Furthermore, the method of information delivery needs to be optimized based on the user's emotions and circumstances. Traditional systems lack sufficient methods to simultaneously consider information consistency and the user's emotional state, highlighting the need for improved user experience.

[0636] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0637] In this invention, the server includes means for acquiring all content information within a web resource identifier and storing it in a data set; means for comparing information of new content with information in an existing data set and detecting inconsistencies in the information; and means for analyzing the emotional state using artificial intelligence and notifying the user of the inconsistencies in a manner that corresponds to the user's emotions. This makes it possible to improve the user experience while maintaining the integrity of the information.

[0638] A "web resource identifier" is a sequence of symbols used to point to specific web content, commonly known as a URL.

[0639] "All content information" refers to all data and metadata contained within a web resource, including text, images, videos, scripts, and more.

[0640] A "data collection" refers to a digital storage location where collected information is systematically stored, and databases typically fulfill this role.

[0641] "New content" refers to newly added information that is not yet included in the existing data set.

[0642] "Inconsistency" refers to a point of contradiction or discrepancy that occurs between two or more datasets, indicating errors or inconsistencies in the information.

[0643] "Artificial intelligence" refers to the technology of using computer systems to imitate human intellectual activity, and typically includes technologies such as machine learning and natural language processing.

[0644] "Emotional state" refers to the emotions a user is experiencing or expressing at a particular moment, and may be determined by facial expressions, voice, and language.

[0645] "Notification" refers to the act of transmitting important information or alerts to a target audience, and is particularly a means of informing recipients of inconsistencies or points that require correction.

[0646] This invention is a system in which a server, terminal, user, and artificial intelligence technology work together in the information management process for web resources. The server collects all content information within the identifier of a web resource and stores this information in a data set. Specifically, it crawls web pages, analyzes various data such as text, images, and videos, and saves them in a database. This process uses general server hardware and software specialized for crawling and data analysis.

[0647] The terminal is responsible for receiving new information added by the user and converting it into a unified format. When a user edits a web page via the terminal, the entered data is converted into a standard format so that the server can easily parse it later. A dedicated data format conversion tool is used for this process.

[0648] Meanwhile, the emotion engine uses a generative AI model to analyze the user's emotional state in real time. This analysis is based on facial expressions, voice, and text data acquired from the device. The AI ​​model is a crucial technology for providing feedback based on emotional state, and if the user is feeling down, for example, it will choose calmer words to respond with.

[0649] For example, if a user mistakenly enters inconsistent product pricing information on different pages, the server will detect this discrepancy. If the sentiment engine determines the user is anxious, the server will notify them with a suggestion to "take your time to check." This dynamic response allows the user to calmly correct their error.

[0650] By utilizing generative AI models, the user experience can be improved, and information consistency can be managed more efficiently. A concrete example of a prompt might be, "When user A receives inconsistent product information while in a state of panic, what follow-up message should be provided?"

[0651] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0652] Step 1:

[0653] The server receives a web resource identifier (URL) as input. The server then initiates an automated crawling process, collecting all content information within the specified scope. This includes HTML documents, images, videos, and other relevant metadata. The server parses this information, converts it into a structured format, and stores it in a data collection.

[0654] Step 2:

[0655] The terminal receives new content created or edited by the user as input. The terminal converts this information into a unified format that is easy for the server to parse. This conversion process includes correcting markup and normalizing data as needed. The output is sent to the server as formatted data.

[0656] Step 3:

[0657] The server compares newly submitted content with existing data sets. Using the new and existing data as input, it checks the consistency of each data field. The server detects differences and inconsistencies and outputs the results as a list.

[0658] Step 4:

[0659] The generative AI model uses facial expressions, voice, or text acquired from the device as input data to analyze the user's emotional state. The model determines the user's emotional state and outputs the analysis results to the server.

[0660] Step 5:

[0661] The server receives a list of inconsistencies and the results of an emotional state analysis from a generating AI model as input. The server generates sentiment-based messaging to optimize notifications to the user. For example, if the server determines that the user is anxious, it will send a notification such as, "Please check again when you are not in a hurry."

[0662] Step 6:

[0663] The user receives a notification from the server, reviews the presented information, and makes corrections. The user then creates new data and sends it back to the server via their device. This feedback loop improves accuracy and efficiency, and maintains the integrity of the information.

[0664] (Application Example 2)

[0665] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0666] In information management, inconsistencies can arise when users handle large amounts of information. These inconsistencies often occur due to unintended errors or input mistakes, and if they persist, they can lead to a deterioration of the user experience and a decrease in the reliability of the information. Furthermore, traditional notification systems send uniform messages without considering the user's perception, which can cause stress to users. Therefore, a system that can flexibly respond according to the user's perception is needed.

[0667] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0668] In this invention, the server includes means for acquiring all data within a collection of information and storing it in a recording device, means for comparing new data with data in an existing recording device and detecting inconsistencies in the information, means for notifying the user of the detected inconsistencies, and means for determining the user's awareness state and adjusting the notification content according to that awareness state. This makes it possible to quickly detect inconsistencies and notify appropriately, and to perform messaging that takes into account the user's awareness state.

[0669] A "collection of information" is the totality of data collected according to certain criteria.

[0670] A "recording device" refers to hardware or software that has the function of accumulating and storing data.

[0671] "New data" refers to data that was not previously available and has been newly generated or acquired.

[0672] "Existing recording device" refers to data storage where information is stored in advance.

[0673] "Inconsistency" refers to a situation where different pieces of information do not match or exhibit contradictions.

[0674] "User" refers to an individual who operates or uses the system.

[0675] "Perceptual state" refers to the state of awareness and emotions that a user exhibits in relation to information.

[0676] "Notification content" refers to the specific information used to communicate detected information or messages to the user.

[0677] "Means of adjustment" refer to processes or functions for changing methods or content according to the situation or conditions.

[0678] The system for realizing this invention consists of a server, a terminal, and a user. The server is responsible for first acquiring all the data within the information collection and storing it in a recording device. This allows all data to be centrally managed and prepared for subsequent comparison processing.

[0679] The terminal is responsible for the process of comparing new data with data from existing recording devices and detecting inconsistencies. This process uses programming languages ​​such as Python and JavaScript to perform efficient data calculations. Furthermore, it utilizes artificial intelligence and software tools such as Google Cloud Vision API and Amazon Rekognition to convert the information into a processable format.

[0680] The user's perception state is determined by the emotion engine. The emotion engine uses the camera and microphone of the smartphone or smart glasses to analyze the user's facial expressions and voice. This information is used to adjust the content of notifications. Any detected inconsistencies are appropriately notified to the user from the server by the program and provided in a way that allows the user to take action based on their perception state.

[0681] As a concrete example, consider a scenario where a user accidentally enters airline ticket price information while creating content for a travel blog. The emotion engine would assess the user's state of mind and provide a gentle notification along with advice such as, "Next time, please double-check the price information." An example of a prompt to input into the generative AI model would be, "If there is a contradiction in the user's input, please provide advice that takes the user's emotions into consideration. Choose the emotion closest to the user's from relaxed, stressed, happy, or disappointed, and provide a notification with content appropriate to that emotion."

[0682] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0683] Step 1:

[0684] The server uses a web crawler to collect all the data within a collection of information and stores it in a storage device. The input is web page information, and the output is structured data stored in a database. When stored in the database, duplicate and unnecessary information is filtered out, and the data is processed to enable efficient searching and comparison.

[0685] Step 2:

[0686] The terminal receives new data entered by the user and compares it with data from an existing recording device. Here, the input is the new data provided by the user, and the output is information indicating data inconsistencies. The comparison process uses algorithms to check for data matches and mismatches. This process typically utilizes Python or JavaScript programs.

[0687] Step 3:

[0688] To recognize the user's emotions, the device utilizes the camera and microphone of a smartphone or smart glasses, and an emotion engine analyzes the user's facial expressions and voice. The input is real-time image or audio data of the user, and the output is the user's emotional state. This analysis uses the Google Cloud Vision API and Amazon Rekognition to perform data feature extraction and recognition using machine learning models.

[0689] Step 4:

[0690] The server considers inconsistencies and the user's emotional state, generates an appropriate notification, and sends it to the user via the terminal. The input is the inconsistent content and emotional state data, and the output is a notification message with adjusted content. Using a generation AI model, the message is generated based on the prompt statement: "If there are inconsistencies in the user's input, please provide advice that takes the user's emotions into consideration. Choose the emotion closest to relaxed, tense, happy, or disappointed, and notify the user with content appropriate to that emotion."

[0691] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0692] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0693] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0694] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0695] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0696] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0697] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0698] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0699] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0700] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0701] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0702] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0703] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0704] 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.

[0705] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0706] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0707] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0708] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0709] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0710] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0711] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0712] The following is further disclosed regarding the embodiments described above.

[0713] (Claim 1)

[0714] A means of obtaining information on all pages within a website's domain and storing it in a database,

[0715] A means to compare the information on a new page with the information in an existing database and detect inconsistencies in the information,

[0716] A means of notifying the user of detected inconsistencies,

[0717] A system that includes this.

[0718] (Claim 2)

[0719] The system according to claim 1, comprising means for converting information on a new page into a format that can be processed using AI.

[0720] (Claim 3)

[0721] The system according to claim 1, further comprising means for outputting the specific details of the inconsistency as a message when an inconsistency is detected.

[0722] "Example 1"

[0723] (Claim 1)

[0724] A means for collecting information using a web resource acquisition device and storing it in an information collection,

[0725] A means for comparing new resources with existing information within an information set and identifying information inconsistencies,

[0726] A means of analyzing the details of the inconsistency using a generative model and notifying the user,

[0727] Output means to support automated information correction,

[0728] A system that includes this.

[0729] (Claim 2)

[0730] The system according to claim 1, comprising a device for converting new resources into a machine-processable format.

[0731] (Claim 3)

[0732] The system according to claim 1, comprising means for providing specific details of the information inconsistency as a report.

[0733] "Application Example 1"

[0734] (Claim 1)

[0735] A means for storing digital information acquired by information gathering means in a database,

[0736] A means for detecting data inconsistencies by comparing new digital data with information in existing databases,

[0737] A means of notifying the user of detected inconsistencies through an interface,

[0738] A means for users to immediately correct digital content based on inconsistency information notified in real time,

[0739] A system that includes this.

[0740] (Claim 2)

[0741] The system according to claim 1, comprising means for converting new digital data into a format that can be processed using machine learning technology.

[0742] (Claim 3)

[0743] The system according to claim 1, further comprising means for presenting the detected inconsistencies to the user as specific digital data and prompting them to make corrections.

[0744] "Example 2 of combining an emotion engine"

[0745] (Claim 1)

[0746] A means for obtaining all content information within a web resource identifier and storing it in a data set,

[0747] A means for comparing information from new content with information from existing data sets and detecting inconsistencies in the information,

[0748] A means of analyzing emotional states using artificial intelligence and notifying users of inconsistencies in a manner that corresponds to their emotions,

[0749] A system that includes this.

[0750] (Claim 2)

[0751] The system according to claim 1, comprising means for converting information on new content into a format that can be processed using artificial intelligence.

[0752] (Claim 3)

[0753] The system according to claim 1, further comprising means for outputting the specific details of the inconsistency as a message when an inconsistency is detected, and providing correction suggestions that correspond to the user's feelings.

[0754] "Application example 2 when combining with an emotional engine"

[0755] (Claim 1)

[0756] A means of acquiring all data within a collection of information and storing it in a recording device,

[0757] A means for comparing new data with data from existing recording devices and detecting inconsistencies in the information,

[0758] A means of notifying the user of detected inconsistencies,

[0759] A means for determining the user's awareness level and adjusting the notification content according to that awareness level,

[0760] A system that includes this.

[0761] (Claim 2)

[0762] The system according to claim 1, comprising means for converting new data into a format that can be processed using artificial intelligence.

[0763] (Claim 3)

[0764] The system according to claim 1, further comprising means for outputting the specific details of the inconsistency as a message when an inconsistency is detected, and for presenting a suggested correction based on the user's awareness. [Explanation of symbols]

[0765] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for storing digital information acquired by information gathering means in a database, A means for detecting data inconsistencies by comparing new digital data with information in existing databases, A means of notifying the user of detected inconsistencies through an interface, A means for users to immediately correct digital content based on inconsistency information notified in real time, A system that includes this.

2. The system according to claim 1, comprising means for converting new digital data into a format that can be processed using machine learning technology.

3. The system according to claim 1, further comprising means for presenting the detected inconsistencies to the user as specific digital data and prompting them to make corrections.

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