System

Abstract

A system is provided.SOLUTION: A system comprising: means for acquiring data of a search result and a question answer collected from a user terminal; means for transmitting the acquired data to a server; means for summarizing the transmitted data in the server; means for generating a weekly report based on the summarized data; and means for providing an interactive function for browsing the weekly report.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In today's business environment, users routinely search for large amounts of information and gain valuable insights by asking generative AI questions. However, this information is often temporary and quickly forgotten. This problem hinders knowledge retention and efficient information sharing. It also reduces efficiency by requiring users to search for the same information again, which takes time. Furthermore, it is difficult to quickly obtain the latest information on topics of interest. Therefore, there is a need to provide a means for users to effectively organize the information they acquire on a daily basis and easily review and share it. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system that includes means for acquiring search result and question-and-answer data collected from a user terminal, means for transmitting the acquired data to a server, means for summarizing the transmitted data at the server, means for generating a weekly report based on the summarized data, and means for providing interactive functions for viewing the weekly report. The system also includes means for customizing the content of the weekly report based on the user's interests, means for notifying the user of the latest information related to specific keywords or topics, and means for archiving past weekly reports and making the archived reports searchable, thereby achieving faster and more efficient access to specific information. These means enable effective organization of information, solidification of knowledge, time savings, and immediate access to the latest information, thereby supporting the user's intellectual activities.

[0006] A "user terminal" is a communication device used by a user, and is a device for collecting search results and question and answer data.

[0007] A "search result" is a collection of information returned in response to a search query submitted by a user on the Internet.

[0008] "Question-answer data" refers to questions asked by users to generative AI and the responses given by the AI.

[0009] The "collection means" is a function or device that allows the user terminal to acquire search results and question and answer data and send them to the server.

[0010] "Transmission means" refers to the function or protocol for sending collected data to a server via the Internet.

[0011] A "server" is a computer system that receives, processes, stores, and manages data sent from user terminals.

[0012] The "summarization means" refers to an algorithm or program that analyzes the data received by the server, simplifies and compresses the content, and generates a summary.

[0013] A "Weekly Report" is a report that consists of a summary of data collected over a period of one week.

[0014] The "generating means" is a function or program for automatically generating a weekly report from the summarized data.

[0015] The "interactive function" is a function that allows the user to obtain detailed information by clicking or otherwise operating the information in the weekly report.

[0016] "Customization" refers to the ability to tailor and change the content of the weekly report based on the user's interests and preferences.

[0017] "Means for notifying the latest information" is a function for notifying users when the latest information related to a specific keyword or topic appears.

[0018] The "archiving means" is a function that stores previously generated weekly reports and allows them to be searched and referenced as needed.

[0019] "Searchability" refers to a function that allows users to search archived data based on specific criteria or keywords. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0022] First, the terms used in the following description will be explained.

[0023] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0024] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0025] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0026] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0031] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0033] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0034] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0038] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0040] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0041] overview

[0042] The present invention is a system for efficiently acquiring, summarizing, storing, and sharing search results and question-and-answer data that users routinely obtain. The system includes a user terminal, a server, and various programs that link these.

[0043] User terminal operation

[0044] It collects search results from users' normal internet use and dialogue data from generative AI (e.g., question-answering systems). The user's device obtains this data through the browser history and an interface to the generative AI. The collected data is then sent to a server.

[0045] Data processing on the server

[0046] The server receives data sent from user devices and stores it in a database. The received data is summarized using a text analysis algorithm. A weekly report is automatically generated based on this summary. The weekly report is generated in HTML format or other formats to make it interactive, allowing users to click to view more detailed information.

[0047] Generate and view weekly reports

[0048] The weekly report contains a week's worth of summary data and is divided into specific topics, such as information on "Python programming" or "artificial intelligence," depending on the user's interests. The generated weekly report is either delivered to the user's device or saved in a format that can be viewed in a web browser.

[0049] Customization features

[0050] Users can customize the content of their weekly reports based on their interests. Users can specify topics and keywords of interest, and the server will collect data and generate summaries based on that information. The system also offers a smart alert function that notifies users when new information becomes available, allowing users to stay up to date with the latest information.

[0051] Archive function

[0052] The server archives all generated weekly reports and provides search functionality where necessary, allowing users to search past reports based on specific topics, keywords, or dates, and reuse information.

[0053] Specific examples

[0054] For example, if a user searches for "Python programming" and "artificial intelligence," the user's device collects the search results and sends them to the server. The server summarizes this data and generates a weekly report containing a week's worth of summaries. The user can view the report, check the summaries for each topic, and click on the summary item of interest to access more information. Additionally, the smart alert function allows users to receive instant notifications when new information related to "Python programming" or "artificial intelligence" appears.

[0055] This allows users to efficiently gather information and solidify their knowledge. This system will be a powerful tool to support intellectual activities in today's information overload environment.

[0056] The processing flow will be explained below.

[0057] Step 1:

[0058] A user searches the Internet. The user's device retrieves search results from the browser history. For example, search results related to "Python programming" and "artificial intelligence" are collected from the browser history.

[0059] Step 2:

[0060] The user terminal formats the collected search results and data from the question-answering system and sends them to the server using an HTTP POST request.

[0061] Step 3:

[0062] The server receives the data sent from the user terminal and stores it in a database. The server stores the captured data in text format for later summarization.

[0063] Step 4:

[0064] The server processes the received data and summarizes its contents. It uses text analysis algorithms (e.g., natural language processing techniques) to generate a summary of the data.

[0065] Step 5:

[0066] The server generates weekly reports based on the summarized data, organized chronologically and categorized by major topics, and structured in formats such as HTML and PDF.

[0067] Step 6:

[0068] The server delivers the generated weekly report to the user terminal or stores it in a web interface, and the user terminal displays the received or retrieved weekly report to the user.

[0069] Step 7:

[0070] Users can view weekly reports and click on each summary to view more detailed information. Interactive features allow users to learn more about specific topics.

[0071] Step 8:

[0072] Users can customize the content of reports based on their interests, and the server generates customized reports by filtering the collected data based on user-specified topics and keywords.

[0073] Step 9:

[0074] The server notifies users when there is new information about specific keywords or topics, using the Smart Alert function to instantly deliver the latest information to users' devices.

[0075] Step 10:

[0076] The server archives all generated weekly reports and makes them searchable as needed, allowing users to search past reports based on specific topics, keywords, or dates, and reuse information.

[0077] Example 1

[0078] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0079] In today's world, the amount of information available via the Internet is enormous, making it difficult for users to efficiently obtain, organize, and manage the accurate and useful information they desire. In particular, the amount of data obtained through daily Internet searches and interactions with generative AI is constantly increasing. To effectively utilize this data, appropriate data collection, summarization, storage, and organization are required. Furthermore, there is a demand for customization functions based on user interests and immediate notifications of the latest information.

[0080] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0081] In this invention, the server includes means for acquiring search results and interaction data collected from the user terminal, means for transmitting the acquired data to an information processing device, means for summarizing the transmitted data in the information processing device, means for generating periodic reports based on the summarized data, means for providing an interactive function for viewing the periodic reports, means for customizing the content of the periodic reports based on the user's interests, means for notifying the user of the latest information related to specific topics of interest, and means for saving past periodic reports and making the saved reports searchable. This allows users to efficiently obtain the information they need from a vast amount of information and review it in summarized form. Furthermore, users can instantly obtain the latest content of information of interest and easily access and reuse past information.

[0082] A "user terminal" is an information processing device used by a user, and refers to devices such as a personal computer, smartphone, or tablet.

[0083] "Search Results" refers to a list of information displayed in response to a query entered by a user using an Internet search engine.

[0084] "Dialogue data" refers to data that includes the content of questions and responses exchanged between a user and a generative AI model.

[0085] The term "information processing device" refers to a device such as a server that receives, stores, analyzes, and summarizes data sent from a user terminal.

[0086] "Summarizing" refers to the act of analyzing acquired data, extracting important information, and summarizing it concisely.

[0087] "Periodic Report" refers to a report generated based on a summary of data over a given period of time (e.g., one week).

[0088] "Interactive function" refers to a function that allows users to operate the device interactively and access detailed information by clicking or tapping.

[0089] "Customizing" refers to the act of changing content or functionality based on a user's interests and needs.

[0090] "Notifying the latest information" refers to the act of notifying the user of newly acquired related information in real time.

[0091] "Storing" refers to the act of recording and retaining information in a database or storage device.

[0092] "Searchable" refers to the ability to find stored information based on specific criteria (such as keywords or dates).

[0093] This invention is a system for efficiently acquiring, summarizing, saving, and sharing search results and dialogue data with generative AI models that users regularly obtain. This system mainly includes a user terminal, a server, an information processing device, a browser, and programs for the generative AI model.

[0094] System configuration and operation

[0095] 1. User Device

[0096] User terminals include devices such as personal computers, smartphones, and tablets. Search results and interaction data with the generative AI model that users use during normal Internet usage are collected. The user terminal acquires this data through the browser history and the interface to the generative AI model, and temporarily stores the data in local storage. The acquired data is then sent to an information processing device. Data is sent using HTTP POST requests, etc.

[0097] 2. Servers and Information Processing Devices

[0098] The server and information processing device are responsible for receiving and storing data sent from user terminals. The information processing device manages data using database software such as MySQL or MongoDB. The sent data is analyzed and summarized using a text analysis algorithm (such as NLTK or spaCy). Furthermore, periodic reports containing one week's worth of data are generated based on the summary results. The generated reports are formatted in HTML and can be used interactively by users.

[0099] 3. Report Delivery and Display

[0100] The server periodically delivers the generated reports to the user's device. Delivery methods include email and notification API. Users can check the delivered reports on their device and access detailed information by clicking on each item. For example, if a user clicks on information about "Python programming" or "artificial intelligence," they can view a summary and detailed information about that item.

[0101] 4. Customization features

[0102] Users can specify topics and keywords of interest through a web interface. The server uses the information to customize data collection and summarization, generating reports tailored to the user's needs. The server also notifies users in real time when new information related to a topic of interest emerges, allowing users to stay up to date with the latest information immediately.

[0103] 5. Archive function

[0104] The server archives all generated periodic reports in a database, allowing users to search for and reuse information from past reports based on specific topics, keywords, or dates, allowing them to efficiently find and review the information they need.

[0105] Specific examples

[0106] For example, if a user searches for "Python programming" and "artificial intelligence," the user's device collects the search results and sends them to the server. The server then summarizes this data using a text analysis algorithm and generates periodic reports containing a week's worth of summaries. Users can view the reports, check the summaries for each topic, and click on the summary items they are interested in to access more information. Additionally, when new information related to "Python programming" or "artificial intelligence" appears, they can receive instant notifications using the smart alert function.

[0107] Prompt Sentence Examples

[0108] 1. "What's the latest news about Python programming?"

[0109] 2. "Please summarize recent developments in the field of artificial intelligence."

[0110] This system allows users to efficiently retrieve the information they need from a vast amount of information and check it in a summarized form. It also allows users to instantly obtain the latest information about information they are interested in, and easily access and reuse past information.

[0111] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0112] Step 1:

[0113] The user device monitors the browser history and interaction data with the generated AI model.

[0114] When a user performs a search, the query and results are saved in the browser history, along with any interactions with the generative AI model.

[0115] Input: User search queries and interaction data with the generative AI model.

[0116] Output: Search history and interaction data saved in local storage.

[0117] What it does: When a user searches for "Python programming," the system retrieves the search query and results. It also collects conversational data when the AI ​​model is asked questions about recent advances in artificial intelligence.

[0118] Step 2:

[0119] The user terminal periodically transmits data stored in the local storage to the information processing device.

[0120] Submissions are made using HTTP POST requests, sending data to a specific endpoint.

[0121] Input: Search history and interaction data stored in local storage.

[0122] Output: Data sent over a broadband network to a server.

[0123] Specific behavior: Search history and interaction data are periodically sent in batches to the http: / / example.com / upload endpoint.

[0124] Step 3:

[0125] The server analyzes the received data and stores it in a database.

[0126] The server receives the HTTP request, parses it, and stores it in a database.

[0127] Input: Browser history and interaction data sent to the server.

[0128] Output: Search history and interaction data stored in a database.

[0129] Specific operation: Executes an INSERT statement to the database and saves the submitted search query and conversation content.

[0130] Step 4:

[0131] The server summarizes the data stored in the database.

[0132] Use text analysis algorithms (e.g., NLTK or spaCy) to extract and summarize important information.

[0133] Input: Search history and interaction data stored in a database.

[0134] Output: Summarized text data.

[0135] What it does: It extracts search history from the database and extracts key points about "Python programming." It also generates an overview of "Recent Advances in Artificial Intelligence" from AI interaction data.

[0136] Step 5:

[0137] The server generates periodic reports based on the summarized data.

[0138] The generated summary data is embedded in an HTML template and formatted as a report.

[0139] Input: Abstracted text data.

[0140] Output: Scheduled reports in HTML format.

[0141] Specific operation: Using Python scripts etc., generate a weekly report by properly arranging the summary data in HTML format.

[0142] Step 6:

[0143] The server generates periodic reports and delivers them to the user terminal.

[0144] Delivery is done via email or notification API.

[0145] Input: Scheduled report in HTML format.

[0146] Output: Report delivered to user terminal.

[0147] Specific operation: Uses the email sending API to send a report to the specified email address, and also uses the notification API to send a notification to the user's device.

[0148] Step 7:

[0149] Users review periodic reports and click on items of interest to access more information.

[0150] Users open the delivered report in a browser or email client.

[0151] Input: HTML format report delivered to user terminal.

[0152] Output: Detailed information displayed in a browser.

[0153] What it does: When a user clicks on a summary item about "Python programming," more information about it opens in a new tab or window.

[0154] Step 8:

[0155] Users specify topics or keywords that interest them.

[0156] Users register topics of interest through a web interface.

[0157] Input: User-specified topics or keywords.

[0158] Output: User interest data stored on the server.

[0159] Specific operation: The user specifies a topic such as "machine learning" or "data science," and it is saved on the server.

[0160] Step 9:

[0161] The server customizes the data collection and summarization process based on the user's interests.

[0162] The server adjusts the collection and summarization algorithms based on the specified data of interest.

[0163] Input: User interest data.

[0164] Output: Customized summary data and reports.

[0165] What it does: Adjust database queries to prioritize search results and interaction data related to "machine learning" and "data science."

[0166] Step 10:

[0167] If the server detects new relevant information, it notifies the user in real time.

[0168] A notification system will be in place when new information is added to the database.

[0169] Input: New information added to the database.

[0170] Output: Notification sent to the user's device.

[0171] What it does: When new information about "Python programming" or "artificial intelligence" is added, users will receive an email or app notification instantly.

[0172] Step 11:

[0173] The server stores past periodic reports and makes them available for user retrieval.

[0174] Saved reports are added to the search index.

[0175] Enter: Periodic Reports.

[0176] Output: Searchable archive data.

[0177] What it does: Periodically generated reports are archived in a database, and when a user searches for "Python programming" or a specific date, relevant past reports are displayed.

[0178] (Application example 1)

[0179] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0180] Conventional factory robot management and maintenance systems lack a means to efficiently collect and organize information about errors and troubleshooting. This prevents factory managers and maintenance teams from quickly grasping the necessary information, resulting in delayed responses and reduced efficiency. There are also insufficient methods for searching and utilizing past error and repair history.

[0181] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0182] In this invention, the server includes means for acquiring search result and question and answer data collected from user terminals, means for transmitting the acquired data to the server, means for summarizing the transmitted data in the server, means for generating a weekly report based on the summarized data, means for providing an interactive function for viewing the weekly report, means for collecting error and log data generated by the robot during operation, means for transmitting the collected error and log data to the server, means for summarizing the received error and log data in the server to generate a weekly report, and means for providing the generated weekly report in a format viewable by factory managers and maintenance teams. This allows for efficient collection and organization of information on errors and troubleshooting that occur in the factory, enabling factory managers and maintenance teams to quickly grasp the necessary information, thereby enabling faster responses and improved work efficiency.

[0183] "User terminal" refers to an electronic device that a user operates to search for information or answer questions.

[0184] "Server" refers to a central processing system that receives, stores, and processes data sent from user terminals.

[0185] "Search results" refers to a collection of information obtained as a result of a search performed by a user to find specific information on the Internet.

[0186] "Question-answering data" refers to information on questions and answers posed through dialogue systems such as generative AI.

[0187] "Summarization" refers to the process of simplifying a large amount of collected data and extracting only the important information.

[0188] A "Weekly Report" is a report that summarizes data collected over the past week and organizes it by specific topic.

[0189] "Interactive function" refers to a function that allows the user to display detailed information or perform specific operations through user operations.

[0190] An "error" refers to an abnormal condition recorded when the robot does not operate normally during a task.

[0191] "Log data" refers to data that records detailed operational history, such as the robot's work content and error occurrence status.

[0192] "Factory manager" refers to an official in charge of the operation and management of a factory.

[0193] "Maintenance team" refers to the group of workers responsible for maintaining and repairing equipment and systems within a factory.

[0194] Overall system overview

[0195] This invention is a system that includes a user terminal, a server, and various programs that link them together to efficiently support the management and maintenance of robots in factories. In particular, it aims to efficiently collect, summarize, store, and share information on errors and troubleshooting that occur during robot operation.

[0196] User device and robot behavior

[0197] The user's device and the robot receive real-time information about errors and troubleshooting that occur during work. This information is sent to a server via the Internet. The robot must have an Internet connection and transmits data using HTTP requests.

[0198] Data processing on the server

[0199] The server receives, stores, and processes a wide variety of data. It houses a database, where the received error and log data is stored. The stored data is summarized using text analysis algorithms. Based on the results of these summaries, regular weekly reports are automatically generated.

[0200] Generate and view weekly reports

[0201] The generated weekly report summarizes the data collected over the past week and is organized by specific topics. The report is generated in HTML format and is presented to the user in an interactive format, allowing the user to click for more information, such as error codes and how to troubleshoot them.

[0202] Customization features

[0203] Users can customize the content of their weekly reports based on their interests, and a smart alert feature is also provided, allowing users to receive instant notifications when new information emerges on specific keywords or topics, helping them stay up to date with the latest information.

[0204] Archive function

[0205] The server archives all generated weekly reports and provides search functionality for them as needed. Users can search past reports based on specific topics, keywords, or dates, and reuse information. This functionality allows for quick access to past error and troubleshooting information.

[0206] Specific examples

[0207] For example, if Robot A in a factory encounters a connection failure with error code 404 while in operation, the robot sends the error information to the server. The server summarizes the received error information and generates a weekly report stating, "An error has occurred regarding a connection failure for Part A. Please refer to the link below for detailed troubleshooting." Factory managers and maintenance teams can view summaries of each topic in this report and click on the summary items of interest to access detailed information. Additionally, when new information occurs, they can receive instant notifications via the smart alert function.

[0208] Prompt Sentence Examples

[0209] "Can you tell me more about the robot error code 404? What troubleshooting steps would work?"

[0210] This system will be a powerful tool for efficient robot management and maintenance within factories, allowing factory managers and maintenance teams to respond quickly when errors occur and significantly improving overall work efficiency.

[0211] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0212] Step 1:

[0213] Data collection

[0214] Subject: Device

[0215] Input: Errors and log data that occur while the robot is working

[0216] Processing: The terminal receives real-time error and log data from the robot, including error codes and troubleshooting information.

[0217] Output: Captured error and log data

[0218] Step 2:

[0219] Data transmission

[0220] Subject: Device

[0221] Input: Captured error and log data

[0222] Processing: The device sends the collected error and log data to the server using an HTTP request.

[0223] Output: Error and log data sent to the server

[0224] Step 3:

[0225] Data reception

[0226] Subject: Server

[0227] Input: Error and log data sent from the device

[0228] Processing: The server receives HTTP requests and stores error and log data in a database, using either an SQL or NoSQL database.

[0229] Output: Error and log data stored in the database

[0230] Step 4:

[0231] Data Summary

[0232] Subject: Server

[0233] Input: Stored error and log data

[0234] Processing: The server summarizes the stored error and log data using text analysis algorithms (e.g., natural language processing libraries).

[0235] Output: Summarized error and log data

[0236] Step 5:

[0237] Weekly Report Generation

[0238] Subject: Server

[0239] Input: Digest error and log data

[0240] Processing: The server generates weekly reports in HTML format based on the summary results, organized by specific topics.

[0241] Output: Generated weekly report

[0242] Step 6:

[0243] Report provision

[0244] Subject: Server

[0245] Input: Generated weekly report

[0246] Processing: The server provides weekly reports to the plant manager or maintenance team either in a format viewable in a web browser or via email.

[0247] Output: Weekly reports provided to plant managers and maintenance teams

[0248] Step 7:

[0249] Customization and Notifications

[0250] Subject: Server

[0251] Input: User interest data, keywords and topic specifications

[0252] Processing: The server customizes weekly reports based on user interests and notifies users when updates occur on specific keywords or topics using the Smart Alerts feature.

[0253] Output: User-customized weekly reports and update notifications

[0254] Step 8:

[0255] Archive and Search

[0256] Subject: Server

[0257] Input: Generated weekly report

[0258] Processing: The server archives all generated weekly reports and allows users to search historical data. For this purpose, it uses a text search engine (e.g. Elasticsearch).

[0259] Output: Searchable archive data

[0260] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0261] overview

[0262] This invention is a system for efficiently acquiring, summarizing, saving, and sharing search results and question-and-answer data that users obtain on a daily basis, and also combines an emotion engine that recognizes the user's emotions. This system includes a user terminal, a server, and various programs that link these.

[0263] User terminal operation

[0264] It collects search results and dialogue data from generative AI (e.g., question-answering systems) during a user's normal internet use. The user's device acquires this data through the browser history and an interface to the generative AI. The acquired data is sent to an emotion engine, which evaluates the user's emotional state. The user's device then sends the collected data and the evaluation of the emotional state to the server.

[0265] Data processing on the server

[0266] The server receives data sent from the user's device and stores it in a database. The received data is summarized using a text analysis algorithm and prioritized for processing based on the evaluation results of the emotion engine. A weekly report is automatically generated based on the summary results. The weekly report also includes an analysis of the user's emotional state.

[0267] Generate and view weekly reports

[0268] The weekly report contains a week's worth of summary data and is divided into specific topics, such as information on "Python programming" or "artificial intelligence," depending on the user's interests. The generated weekly report is delivered to the user's device or saved in a format that can be viewed in a web browser. The report is interactive, allowing users to click to view more detailed information.

[0269] Use of emotion engine

[0270] The user device is equipped with an emotion engine that recognizes emotions from the user's tone of voice, facial expressions, and text input. For example, if a user asks a question about "Python programming" and expresses dissatisfaction or confusion, this information is transmitted to the server, and the relevant content is highlighted in the weekly report.

[0271] Customization features

[0272] Users can customize the content of their reports based on their interests. The server filters the collected data based on user-specified topics and keywords to generate customized reports. The server also provides a smart alert feature that notifies users when new information becomes available.

[0273] Archive function

[0274] The server archives all generated weekly reports and provides search functionality for them as needed, allowing users to search past reports based on specific topics, keywords, or dates and reuse information.

[0275] Specific examples

[0276] For example, if a user searches for "Python programming" and the emotion engine detects feelings of dissatisfaction or confusion, the user's device will send this information to the server. The server will prioritize and summarize this data and include it as a special section in the weekly report. After a week, the user will receive a report with a detailed explanation of the topic that confused them. The user can then browse the report and use the interactive features to find out more details. Smart alerts will also instantly notify users when new useful information about "Python programming" becomes available.

[0277] This allows users to efficiently gather information and solidify their knowledge. In today's information overload environment, this system will be a powerful tool for supporting intellectual activities while taking into account the user's subjective feelings.

[0278] The processing flow will be explained below.

[0279] Step 1:

[0280] A user searches the Internet. The user's device retrieves search results from the browser history. For example, search results related to "Python programming" and "artificial intelligence" are collected from the browser history.

[0281] Step 2:

[0282] The user device uses an emotion engine to analyze the user's emotional state from the search results and question-and-answer data it obtains, for example, by analyzing the content of text input, voice tone, and facial expressions to evaluate the user's emotional state.

[0283] Step 3:

[0284] The user device formats the collected search results and emotional state ratings and sends them to the server. This data is sent to the server using an HTTP POST request.

[0285] Step 4:

[0286] The server receives data sent from the user terminal and stores it in a database, including search results, question-and-answer data, and the user's emotional state.

[0287] Step 5:

[0288] The server then summarises the stored data using a text analysis algorithm, taking into account the user's emotional state and prioritising particularly important information or content closely related to the user's emotions.

[0289] Step 6:

[0290] The server then uses the summarized data to generate a weekly report customized for each user, with analysis and relevant information based on their emotional state, including detailed explanations and additional information on topics that confuse the user.

[0291] Step 7:

[0292] The generated weekly report is delivered to the user terminal or stored in a web interface, and the user terminal displays the received or retrieved weekly report to the user.

[0293] Step 8:

[0294] Users can view weekly reports and click on each summary to view more detailed information. Interactive features allow users to learn more about specific topics.

[0295] Step 9:

[0296] Users can customize the content of reports based on their interests, and the server generates customized reports by filtering the collected data based on user-specified topics and keywords.

[0297] Step 10:

[0298] The server notifies users when there is new information about specific keywords or topics, using the Smart Alert function to instantly deliver the latest information to users' devices.

[0299] Step 11:

[0300] The server archives all generated weekly reports and makes them searchable as needed, allowing users to search past reports based on specific topics, keywords, or dates, and reuse information.

[0301] Example 2

[0302] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0303] In today's information-overloaded world, it is important to efficiently retrieve, consolidate, and effectively utilize user search results and question-answer data. However, existing systems lack the functionality to prioritize information based on the user's emotional state and customize reports accordingly. Furthermore, archiving and searching for old information is also inadequate, making it difficult for users to reuse past information.

[0304] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0305] In this invention, the server includes means for acquiring search result and question-and-answer data collected from the user terminal, means for transmitting the acquired data to an emotion engine to evaluate the user's emotional state, means for transmitting data including the evaluation results to the server, means for summarizing the transmitted data in the server, means for generating a weekly report based on the summarized data and the emotion evaluation results, and means for providing an interactive function for viewing the weekly report. This enables prioritization of information and generation of customized reports taking into account the user's emotional state, and also enables effective retrieval and reuse of past information.

[0306] A "user terminal" is a device that a user uses to access the Internet, and includes a PC, smartphone, tablet, etc.

[0307] "Search results" refers to the list of information that a user can obtain by using an Internet search engine.

[0308] "Question-answer data" refers to data that includes questions posed to generative AI by users and the AI's answers to those questions.

[0309] An "emotion engine" refers to a system that recognizes and evaluates emotions from a user's tone of voice, facial expressions, text input, etc.

[0310] "Server" refers to a computer that operates on a network and processes and stores data sent from user devices.

[0311] A "summarization method" is a method that uses a text analysis algorithm to extract the main parts of the transmitted data and present them in a concise form.

[0312] A "Weekly Report" is a report that compiles a week's worth of summary data and categorizes it by specific topic.

[0313] "Interactive function" refers to a function that allows a user to view detailed information by clicking on a link in a report.

[0314] "Customization methods" are ways to tailor and change the content of a report based on user interests.

[0315] "Means for notifying" refers to a method for notifying the user when new information is generated.

[0316] "Archiving means" refers to a method for storing generated weekly reports as historical records and making them available for later retrieval.

[0317] MODE FOR CARRYING OUT THE INVENTION

[0318] overview

[0319] This invention is a system for efficiently acquiring, summarizing, saving, and sharing search results and question-and-answer data that users obtain on a daily basis, and also combines an emotion engine that recognizes the user's emotions. This system includes a user terminal, a server, and various programs that link these.

[0320] User terminal operation

[0321] The user device collects the search results the user has obtained online and data on their interactions with the generative AI. Specifically, it obtains search keywords, questions, and conversation history through browser history data and the generative AI's API. It also uses an emotion engine to evaluate the user's emotional state based on their tone of voice, facial expression, and text input. The results of this emotional state evaluation are sent to the server along with the collected data.

[0322] Data processing on the server

[0323] The server receives data sent from the user's device and stores it in a relational database (e.g., MySQL, PostgreSQL). The received data is summarized using a text analysis algorithm (e.g., NLTK, SpaCy). Based on this summary data and the evaluation results of the emotion engine, the server automatically generates a weekly report. A template engine for report generation (e.g., Jinja2) is used to create a report that highlights the most important content based on the user's emotion evaluation results.

[0324] Generate and view weekly reports

[0325] The generated weekly reports are generated in a visually friendly format using HTML and CSS. The reports are delivered to the user's device or saved in a format that can be viewed in a web browser. The reports include interactive features, allowing users to click to view more detailed information.

[0326] Customize reports

[0327] Users can customize the content of their reports based on their interests. The server generates a customized report by filtering relevant information in the database based on topics and keywords specified by the user. For example, a report can be tailored to include only information about "AI." The server also notifies users when new information becomes available.

[0328] Archive function

[0329] The server stores an archive of all generated weekly reports and provides users with the ability to search past reports. Users can search past reports based on specific topics, keywords, or dates, allowing them to reuse information. This search functionality can utilize search engine technology (e.g., Elasticsearch).

[0330] Specific examples

[0331] For example, if a user searches for "Python programming" and the emotion engine detects feelings of dissatisfaction or confusion, the user's device will send this information to the server. The server will prioritize and summarize this data and include it as a special section in the weekly report. After a week, the user will receive a report with a detailed explanation of the topic that confused them. The user can then view the report and use the interactive features to find out more details. Additionally, the smart alert feature will instantly notify the user when new useful information about "Python programming" becomes available.

[0332] Example prompt sentence:

[0333] "Explain how you can measure how users learn about Python programming, summarize that data, and generate a report based on the sentiment engine's assessment."

[0334] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0335] Step 1:

[0336] Data collection from user devices

[0337] When a user searches or interacts with generative AI in a web browser, their history data and interaction data are collected. Specifically, data such as search keywords and conversation content is collected using the browser's history API and the generative AI's API.

[0338] Input: User search results, question and answer data

[0339] Output: Acquired historical data, interaction data

[0340] Step 2:

[0341] Emotional state assessment by emotion engine

[0342] The acquired data is sent to the emotion engine, which recognizes emotions from the user's tone of voice, facial expressions, and text input, and generates an evaluation result. Specifically, it uses voice analysis, image analysis, and natural language processing technology to perform emotion evaluation.

[0343] Input: historical data, dialogue data, user voice, facial expressions, text input

[0344] Output: Emotion evaluation result (e.g., confusion, dissatisfaction)

[0345] Step 3:

[0346] Send data and evaluation results to the server

[0347] The user device sends the collected history data, dialogue data, and emotion evaluation results to the server. Specifically, this data is compiled in JSON format and uploaded to the server using the HTTPS protocol.

[0348] Input: History data, dialogue data, emotion evaluation results

[0349] Output: Data sent to the server

[0350] Step 4:

[0351] Data storage on the server

[0352] The server stores the received data in a database. Specifically, it uses a relational database (e.g., MySQL or PostgreSQL) to classify and store the data by user ID.

[0353] Input: Data sent to the server

[0354] Output: Data stored in the database

[0355] Step 5:

[0356] Text Analysis and Data Summarization

[0357] The server summarizes the stored data using a text analysis algorithm, specifically using a natural language processing library (e.g., NLTK or SpaCy) to extract important keywords and sentences and generate a summary.

[0358] Input: Data stored in a database

[0359] Output: Summarized text data

[0360] Step 6:

[0361] Generate weekly reports

[0362] The server automatically generates weekly reports based on the summarized data. Specifically, it uses a template engine (e.g., Jinja2) for report generation to create reports based on user interest topics and sentiment assessment results.

[0363] Input: Summarized text data, emotion evaluation results

[0364] Output: Generated weekly report

[0365] Step 7:

[0366] Report distribution and viewing

[0367] The server then delivers the generated weekly report to the user's device or saves it in a format that can be viewed in a web browser. Specifically, it creates a visually appealing report using HTML and CSS and delivers it to the user via email or in-app notification.

[0368] Input: Generated weekly report

[0369] Output: Reports delivered to user devices, reports viewable in web browsers

[0370] Step 8:

[0371] Customize reports

[0372] Users can customize the content of the report based on their interests, and the server generates a customized report by filtering relevant information in the database based on user-specified topics and keywords.

[0373] Input: User's interest topics, keywords

[0374] Output: Customized weekly report

[0375] Step 9:

[0376] Using the archive function

[0377] The server stores an archive of all generated weekly reports and allows users to search past reports using search engine technology (e.g., Elasticsearch) based on specified topics, keywords, and dates.

[0378] Input: Past Weekly Report, Search Query

[0379] Output: Search results as a report

[0380] (Application example 2)

[0381] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0382] In today's information overload environment, users face the challenge of efficiently gathering useful information and solidifying knowledge. Furthermore, systems are needed to improve the user experience by providing information tailored to the user's emotional state. However, current systems do not adequately consider the user's emotions when providing information, and are unable to prioritize information appropriately. To solve this problem, technology is needed that can evaluate the user's emotional state and organize, summarize, and recommend information based on that information.

[0383] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0384] In this invention, the server includes a means for acquiring search result and question-and-answer data collected from a user terminal, a means for evaluating the emotional state of the acquired data, and a means for summarizing the transmitted data in the server. This allows for efficient summarization of important information and generation of a weekly report, taking into account the user's emotional state. Furthermore, the weekly report can be customized based on the user's level of interest and notified of the latest information on specific keywords or topics, improving the user experience. Furthermore, past weekly reports can be archived and made searchable, allowing for quick reuse of necessary information.

[0385] A "user terminal" is a device that can connect to the Internet and run applications, and is a device that allows a user to obtain and transmit data.

[0386] A "search result" is a list of information that a user obtains from a source such as the Internet based on a particular query.

[0387] "Question-answer data" refers to text data of answers returned to users' questions by generative AI models, etc.

[0388] A "server" is a computer or computer system on a network that can receive, store, process, and transmit data.

[0389] "Emotional state" refers to the psychological state detected from the user's tone of voice, facial expressions, and text input.

[0390] "Summarizing" is the process of shortening acquired data or information and concisely summarizing only the main points.

[0391] A "weekly report" is a report that summarizes one week's worth of data and categorizes it into specific topics and provides it to the user.

[0392] "Interactive features" are features that allow users to click on information in a report to obtain more information.

[0393] "Customization" means filtering information based on the user's interests or specified topics to provide information that meets specific criteria.

[0394] "Means for notifying users of the latest information" refers to a function that instantly notifies users when new information related to a specific keyword or topic is generated.

[0395] "Archive" is a data storage function that saves the generated weekly reports so that they can be searched and referenced later.

[0396] "Searchable" refers to the ability to efficiently find and display specific information from stored data.

[0397] The present invention provides a system for improving the efficiency of information acquisition for users and providing information preferentially according to their emotional state. This system is generally composed of a user terminal, a server, and various programs that link these together.

[0398] First, a user device is a device that can connect to the Internet and run applications, such as a smartphone or PC. When a user enters a search query, the user device collects information using the Internet. When the user asks a question to the generative AI model, the interaction data is also collected. The collected data is sent to an emotion engine on the user device to evaluate the emotional state.

[0399] The emotion engine detects emotions from the user's tone of voice, facial expressions, and text input, and evaluates how the user feels about the content. This emotional state evaluation data is sent to the server along with search results and question-and-answer data.

[0400] The server receives data sent from the user's device and stores it in a database. It then summarizes the data using text analysis algorithms. The software used here includes natural language processing libraries (e.g., TextBlob). The summarized data is prioritized based on the emotional state assessment results and compiled into a weekly report.

[0401] The weekly reports are customized based on the user's interests, categorizing information according to specific topics or keywords. Additionally, when important new information occurs, the server notifies the user of the latest information related to the specific keyword or topic, allowing the user to get the latest information immediately.

[0402] In addition, all weekly reports are archived on the server, allowing users to search and reuse past reports. For example, if a user wants to review information about "Python programming project construction" again, they can easily search for past reports using the archive function.

[0403] Here are some examples of specific prompts:

[0404] "Python programming project construction"

[0405] When a user enters this query, the user's device collects related news articles and question-and-answer data, evaluates the user's emotional state through an emotion engine, and sends the data to the server, which then generates summary data and provides it to the user as a weekly report.

[0406] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0407] Step 1:

[0408] A user enters a search query.

[0409] Input: User's search query (e.g. "Python programming project build")

[0410] Specific operation: The user inputs a search query through an input device on the user terminal.

[0411] Step 2:

[0412] The user terminal collects relevant information from the Internet.

[0413] Input: search query

[0414] Output: A list of search results (news articles, web page links, etc.)

[0415] Specific operation: The user device connects to the Internet and uses a search engine API to collect search results based on the query.

[0416] Step 3:

[0417] The user inputs dialogue data into the generative AI model.

[0418] Input: User question (e.g., "How do I get started on a project in Python programming?")

[0419] Output: Question and answer data (answers from the generative AI model)

[0420] Specific operation: The generative AI model installed on the user's device answers the user's questions and saves the data.

[0421] Step 4:

[0422] The user terminal uses an emotion engine to assess the emotional state.

[0423] Input: Search results, question and answer data

[0424] Output: Emotional state assessment (e.g., confusion, frustration, joy)

[0425] Specific operation: The camera and microphone on the user's device are used to analyze facial expressions and voice tone, and the emotion engine analyzes the text data to evaluate emotions.

[0426] Step 5:

[0427] The user terminal transmits the collected data and the evaluation results of the emotional state to the server.

[0428] Input: Search results, Q&A data, and emotional state assessment results

[0429] Output: Data transfer to the server

[0430] Specific operation: Using the communication module of the user terminal, the collected data and the evaluation results of the emotional state are sent to the server.

[0431] Step 6:

[0432] The server stores the received data in a database.

[0433] Input: Collected data, emotional state assessment results

[0434] Output: Data stored in the database

[0435] Specific operation: The server analyzes the received data and stores it in the database in the appropriate format.

[0436] Step 7:

[0437] The server uses text analysis algorithms to summarize the data.

[0438] Input: Data stored in the database

[0439] Output: Summary data

[0440] Specific behavior: The server summarizes the text data using a natural language processing library (e.g., TextBlob).

[0441] Step 8:

[0442] The server generates a weekly report based on the emotional state assessment.

[0443] Input: Summary data, emotional state assessment results

[0444] Output: Weekly report

[0445] What it does: The server combines the summary data with the emotional state to generate a customized weekly report based on the user's interests.

[0446] Step 9:

[0447] The server delivers weekly reports to user terminals and provides interactive functions.

[0448] Input: Weekly Report

[0449] Output: A report in a format that can be viewed on the user's terminal

[0450] Specific operation: The server delivers the report to the user's device and allows the user to click on information in the report to obtain more information.

[0451] Step 10:

[0452] The server archives past weekly reports and makes them searchable by users.

[0453] Input: Generated weekly report

[0454] Output: Archived report data

[0455] What it does: The server archives past weekly reports, allowing users to quickly search for the information they need.

[0456] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0457] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0458] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0459] [Second embodiment]

[0460] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0461] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0462] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0463] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0464] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0465] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0466] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0467] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0468] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0469] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0470] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0471] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0472] overview

[0473] The present invention is a system for efficiently acquiring, summarizing, storing, and sharing search results and question-and-answer data that users routinely obtain. The system includes a user terminal, a server, and various programs that link these.

[0474] User terminal operation

[0475] It collects search results from users' normal internet use and dialogue data from generative AI (e.g., question-answering systems). The user's device obtains this data through the browser history and an interface to the generative AI. The collected data is then sent to a server.

[0476] Data processing on the server

[0477] The server receives data sent from user devices and stores it in a database. The received data is summarized using a text analysis algorithm. A weekly report is automatically generated based on this summary. The weekly report is generated in HTML format or other formats to make it interactive, allowing users to click to view more detailed information.

[0478] Generate and view weekly reports

[0479] The weekly report contains a week's worth of summary data and is divided into specific topics, such as information on "Python programming" or "artificial intelligence," depending on the user's interests. The generated weekly report is either delivered to the user's device or saved in a format that can be viewed in a web browser.

[0480] Customization features

[0481] Users can customize the content of their weekly reports based on their interests. Users can specify topics and keywords of interest, and the server will collect data and generate summaries based on that information. The system also offers a smart alert function that notifies users when new information becomes available, allowing users to stay up to date with the latest information.

[0482] Archive function

[0483] The server archives all generated weekly reports and provides search functionality where necessary, allowing users to search past reports based on specific topics, keywords, or dates, and reuse information.

[0484] Specific examples

[0485] For example, if a user searches for "Python programming" and "artificial intelligence," the user's device collects the search results and sends them to the server. The server summarizes this data and generates a weekly report containing a week's worth of summaries. The user can view the report, check the summaries for each topic, and click on the summary item of interest to access more information. Additionally, the smart alert function allows users to receive instant notifications when new information related to "Python programming" or "artificial intelligence" appears.

[0486] This allows users to efficiently gather information and solidify their knowledge. This system will be a powerful tool to support intellectual activities in today's information overload environment.

[0487] The processing flow will be explained below.

[0488] Step 1:

[0489] A user searches the Internet. The user's device retrieves search results from the browser history. For example, search results related to "Python programming" and "artificial intelligence" are collected from the browser history.

[0490] Step 2:

[0491] The user terminal formats the collected search results and data from the question-answering system and sends them to the server using an HTTP POST request.

[0492] Step 3:

[0493] The server receives the data sent from the user terminal and stores it in a database. The server stores the captured data in text format for later summarization.

[0494] Step 4:

[0495] The server processes the received data and summarizes its contents. It uses text analysis algorithms (e.g., natural language processing techniques) to generate a summary of the data.

[0496] Step 5:

[0497] The server generates weekly reports based on the summarized data, organized chronologically and categorized by major topics, and structured in formats such as HTML and PDF.

[0498] Step 6:

[0499] The server delivers the generated weekly report to the user terminal or stores it in a web interface, and the user terminal displays the received or retrieved weekly report to the user.

[0500] Step 7:

[0501] Users can view weekly reports and click on each summary to view more detailed information. Interactive features allow users to learn more about specific topics.

[0502] Step 8:

[0503] Users can customize the content of reports based on their interests, and the server generates customized reports by filtering the collected data based on user-specified topics and keywords.

[0504] Step 9:

[0505] The server notifies users when there is new information about specific keywords or topics, using the Smart Alert function to instantly deliver the latest information to users' devices.

[0506] Step 10:

[0507] The server archives all generated weekly reports and makes them searchable as needed, allowing users to search past reports based on specific topics, keywords, or dates, and reuse information.

[0508] Example 1

[0509] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0510] In today's world, the amount of information available via the Internet is enormous, making it difficult for users to efficiently obtain, organize, and manage the accurate and useful information they desire. In particular, the amount of data obtained through daily Internet searches and interactions with generative AI is constantly increasing. To effectively utilize this data, appropriate data collection, summarization, storage, and organization are required. Furthermore, there is a demand for customization functions based on user interests and immediate notifications of the latest information.

[0511] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0512] In this invention, the server includes means for acquiring search results and interaction data collected from the user terminal, means for transmitting the acquired data to an information processing device, means for summarizing the transmitted data in the information processing device, means for generating periodic reports based on the summarized data, means for providing an interactive function for viewing the periodic reports, means for customizing the content of the periodic reports based on the user's interests, means for notifying the user of the latest information related to specific topics of interest, and means for saving past periodic reports and making the saved reports searchable. This allows users to efficiently obtain the information they need from a vast amount of information and review it in summarized form. Furthermore, users can instantly obtain the latest content of information of interest and easily access and reuse past information.

[0513] A "user terminal" is an information processing device used by a user, and refers to devices such as a personal computer, smartphone, or tablet.

[0514] "Search Results" refers to a list of information displayed in response to a query entered by a user using an Internet search engine.

[0515] "Dialogue data" refers to data that includes the content of questions and responses exchanged between a user and a generative AI model.

[0516] The term "information processing device" refers to a device such as a server that receives, stores, analyzes, and summarizes data sent from a user terminal.

[0517] "Summarizing" refers to the act of analyzing acquired data, extracting important information, and summarizing it concisely.

[0518] "Periodic Report" refers to a report generated based on a summary of data over a given period of time (e.g., one week).

[0519] "Interactive function" refers to a function that allows users to operate the device interactively and access detailed information by clicking or tapping.

[0520] "Customizing" refers to the act of changing content or functionality based on a user's interests and needs.

[0521] "Notifying the latest information" refers to the act of notifying the user of newly acquired related information in real time.

[0522] "Storing" refers to the act of recording and retaining information in a database or storage device.

[0523] "Searchable" refers to the ability to find stored information based on specific criteria (such as keywords or dates).

[0524] This invention is a system for efficiently acquiring, summarizing, saving, and sharing search results and dialogue data with generative AI models that users regularly obtain. This system mainly includes a user terminal, a server, an information processing device, a browser, and programs for the generative AI model.

[0525] System configuration and operation

[0526] 1. User Device

[0527] User terminals include devices such as personal computers, smartphones, and tablets. Search results and interaction data with the generative AI model that users use during normal Internet usage are collected. The user terminal acquires this data through the browser history and the interface to the generative AI model, and temporarily stores the data in local storage. The acquired data is then sent to an information processing device. Data is sent using HTTP POST requests, etc.

[0528] 2. Servers and Information Processing Devices

[0529] The server and information processing device are responsible for receiving and storing data sent from user terminals. The information processing device manages data using database software such as MySQL or MongoDB. The sent data is analyzed and summarized using a text analysis algorithm (such as NLTK or spaCy). Furthermore, periodic reports containing one week's worth of data are generated based on the summary results. The generated reports are formatted in HTML and can be used interactively by users.

[0530] 3. Report Delivery and Display

[0531] The server periodically delivers the generated reports to the user's device. Delivery methods include email and notification API. Users can check the delivered reports on their device and access detailed information by clicking on each item. For example, if a user clicks on information about "Python programming" or "artificial intelligence," they can view a summary and detailed information about that item.

[0532] 4. Customization features

[0533] Users can specify topics and keywords of interest through a web interface. The server uses the information to customize data collection and summarization, generating reports tailored to the user's needs. The server also notifies users in real time when new information related to a topic of interest emerges, allowing users to stay up to date with the latest information immediately.

[0534] 5. Archive function

[0535] The server archives all generated periodic reports in a database, allowing users to search for and reuse information from past reports based on specific topics, keywords, or dates, allowing them to efficiently find and review the information they need.

[0536] Specific examples

[0537] For example, if a user searches for "Python programming" and "artificial intelligence," the user's device collects the search results and sends them to the server. The server then summarizes this data using a text analysis algorithm and generates periodic reports containing a week's worth of summaries. Users can view the reports, check the summaries for each topic, and click on the summary items they are interested in to access more information. Additionally, when new information related to "Python programming" or "artificial intelligence" appears, they can receive instant notifications using the smart alert function.

[0538] Prompt Sentence Examples

[0539] 1. "What's the latest news about Python programming?"

[0540] 2. "Please summarize recent developments in the field of artificial intelligence."

[0541] This system allows users to efficiently retrieve the information they need from a vast amount of information and check it in a summarized form. It also allows users to instantly obtain the latest information about information they are interested in, and easily access and reuse past information.

[0542] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0543] Step 1:

[0544] The user device monitors the browser history and interaction data with the generated AI model.

[0545] When a user performs a search, the query and results are saved in the browser history, along with any interactions with the generative AI model.

[0546] Input: User search queries and interaction data with the generative AI model.

[0547] Output: Search history and interaction data saved in local storage.

[0548] What it does: When a user searches for "Python programming," the system retrieves the search query and results. It also collects conversational data when the AI ​​model is asked questions about recent advances in artificial intelligence.

[0549] Step 2:

[0550] The user terminal periodically transmits data stored in the local storage to the information processing device.

[0551] Submissions are made using HTTP POST requests, sending data to a specific endpoint.

[0552] Input: Search history and interaction data stored in local storage.

[0553] Output: Data sent over a broadband network to a server.

[0554] Specific behavior: Search history and interaction data are periodically sent in batches to the http: / / example.com / upload endpoint.

[0555] Step 3:

[0556] The server analyzes the received data and stores it in a database.

[0557] The server receives the HTTP request, parses it, and stores it in a database.

[0558] Input: Browser history and interaction data sent to the server.

[0559] Output: Search history and interaction data stored in a database.

[0560] Specific operation: Executes an INSERT statement to the database and saves the submitted search query and conversation content.

[0561] Step 4:

[0562] The server summarizes the data stored in the database.

[0563] Use text analysis algorithms (e.g., NLTK or spaCy) to extract and summarize important information.

[0564] Input: Search history and interaction data stored in a database.

[0565] Output: Summarized text data.

[0566] What it does: It extracts search history from the database and extracts key points about "Python programming." It also generates an overview of "Recent Advances in Artificial Intelligence" from AI interaction data.

[0567] Step 5:

[0568] The server generates periodic reports based on the summarized data.

[0569] The generated summary data is embedded in an HTML template and formatted as a report.

[0570] Input: Abstracted text data.

[0571] Output: Scheduled reports in HTML format.

[0572] Specific operation: Using Python scripts etc., generate a weekly report by properly arranging the summary data in HTML format.

[0573] Step 6:

[0574] The server generates periodic reports and delivers them to the user terminal.

[0575] Delivery is done via email or notification API.

[0576] Input: Scheduled report in HTML format.

[0577] Output: Report delivered to user terminal.

[0578] Specific operation: Uses the email sending API to send a report to the specified email address, and also uses the notification API to send a notification to the user's device.

[0579] Step 7:

[0580] Users review periodic reports and click on items of interest to access more information.

[0581] Users open the delivered report in a browser or email client.

[0582] Input: HTML format report delivered to user terminal.

[0583] Output: Detailed information displayed in a browser.

[0584] What it does: When a user clicks on a summary item about "Python programming," more information about it opens in a new tab or window.

[0585] Step 8:

[0586] Users specify topics or keywords that interest them.

[0587] Users register topics of interest through a web interface.

[0588] Input: User-specified topics or keywords.

[0589] Output: User interest data stored on the server.

[0590] Specific operation: The user specifies a topic such as "machine learning" or "data science," and it is saved on the server.

[0591] Step 9:

[0592] The server customizes the data collection and summarization process based on the user's interests.

[0593] The server adjusts the collection and summarization algorithms based on the specified data of interest.

[0594] Input: User interest data.

[0595] Output: Customized summary data and reports.

[0596] What it does: Adjust database queries to prioritize search results and interaction data related to "machine learning" and "data science."

[0597] Step 10:

[0598] If the server detects new relevant information, it notifies the user in real time.

[0599] A notification system will be in place when new information is added to the database.

[0600] Input: New information added to the database.

[0601] Output: Notification sent to the user's device.

[0602] What it does: When new information about "Python programming" or "artificial intelligence" is added, users will receive an email or app notification instantly.

[0603] Step 11:

[0604] The server stores past periodic reports and makes them available for user retrieval.

[0605] Saved reports are added to the search index.

[0606] Enter: Periodic Reports.

[0607] Output: Searchable archive data.

[0608] What it does: Periodically generated reports are archived in a database, and when a user searches for "Python programming" or a specific date, relevant past reports are displayed.

[0609] (Application example 1)

[0610] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0611] Conventional factory robot management and maintenance systems lack a means to efficiently collect and organize information about errors and troubleshooting. This prevents factory managers and maintenance teams from quickly grasping the necessary information, resulting in delayed responses and reduced efficiency. There are also insufficient methods for searching and utilizing past error and repair history.

[0612] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0613] In this invention, the server includes means for acquiring search result and question and answer data collected from user terminals, means for transmitting the acquired data to the server, means for summarizing the transmitted data in the server, means for generating a weekly report based on the summarized data, means for providing an interactive function for viewing the weekly report, means for collecting error and log data generated by the robot during operation, means for transmitting the collected error and log data to the server, means for summarizing the received error and log data in the server to generate a weekly report, and means for providing the generated weekly report in a format viewable by factory managers and maintenance teams. This allows for efficient collection and organization of information on errors and troubleshooting that occur in the factory, enabling factory managers and maintenance teams to quickly grasp the necessary information, thereby enabling faster responses and improved work efficiency.

[0614] "User terminal" refers to an electronic device that a user operates to search for information or answer questions.

[0615] "Server" refers to a central processing system that receives, stores, and processes data sent from user terminals.

[0616] "Search results" refers to a collection of information obtained as a result of a search performed by a user to find specific information on the Internet.

[0617] "Question-answering data" refers to information on questions and answers posed through dialogue systems such as generative AI.

[0618] "Summarization" refers to the process of simplifying a large amount of collected data and extracting only the important information.

[0619] A "Weekly Report" is a report that summarizes data collected over the past week and organizes it by specific topic.

[0620] "Interactive function" refers to a function that allows the user to display detailed information or perform specific operations through user operations.

[0621] An "error" refers to an abnormal condition recorded when the robot does not operate normally during a task.

[0622] "Log data" refers to data that records detailed operational history, such as the robot's work content and error occurrence status.

[0623] "Factory manager" refers to an official in charge of the operation and management of a factory.

[0624] "Maintenance team" refers to the group of workers responsible for maintaining and repairing equipment and systems within a factory.

[0625] Overall system overview

[0626] This invention is a system that includes a user terminal, a server, and various programs that link them together to efficiently support the management and maintenance of robots in factories. In particular, it aims to efficiently collect, summarize, store, and share information on errors and troubleshooting that occur during robot operation.

[0627] User device and robot behavior

[0628] The user's device and the robot receive real-time information about errors and troubleshooting that occur during work. This information is sent to a server via the Internet. The robot must have an Internet connection and transmits data using HTTP requests.

[0629] Data processing on the server

[0630] The server receives, stores, and processes a wide variety of data. It houses a database, where the received error and log data is stored. The stored data is summarized using text analysis algorithms. Based on the results of these summaries, regular weekly reports are automatically generated.

[0631] Generate and view weekly reports

[0632] The generated weekly report summarizes the data collected over the past week and is organized by specific topics. The report is generated in HTML format and is presented to the user in an interactive format, allowing the user to click for more information, such as error codes and how to troubleshoot them.

[0633] Customization features

[0634] Users can customize the content of their weekly reports based on their interests, and a smart alert feature is also provided, allowing users to receive instant notifications when new information emerges on specific keywords or topics, helping them stay up to date with the latest information.

[0635] Archive function

[0636] The server archives all generated weekly reports and provides search functionality for them as needed. Users can search past reports based on specific topics, keywords, or dates, and reuse information. This functionality allows for quick access to past error and troubleshooting information.

[0637] Specific examples

[0638] For example, if Robot A in a factory encounters a connection failure with error code 404 while in operation, the robot sends the error information to the server. The server summarizes the received error information and generates a weekly report stating, "An error has occurred regarding a connection failure for Part A. Please refer to the link below for detailed troubleshooting." Factory managers and maintenance teams can view summaries of each topic in this report and click on the summary items of interest to access detailed information. Additionally, when new information occurs, they can receive instant notifications via the smart alert function.

[0639] Prompt Sentence Examples

[0640] "Can you tell me more about the robot error code 404? What troubleshooting steps would work?"

[0641] This system will be a powerful tool for efficient robot management and maintenance within factories, allowing factory managers and maintenance teams to respond quickly when errors occur and significantly improving overall work efficiency.

[0642] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0643] Step 1:

[0644] Data collection

[0645] Subject: Device

[0646] Input: Errors and log data that occur while the robot is working

[0647] Processing: The terminal receives real-time error and log data from the robot, including error codes and troubleshooting information.

[0648] Output: Captured error and log data

[0649] Step 2:

[0650] Data transmission

[0651] Subject: Device

[0652] Input: Captured error and log data

[0653] Processing: The device sends the collected error and log data to the server using an HTTP request.

[0654] Output: Error and log data sent to the server

[0655] Step 3:

[0656] Data reception

[0657] Subject: Server

[0658] Input: Error and log data sent from the device

[0659] Processing: The server receives HTTP requests and stores error and log data in a database, using either an SQL or NoSQL database.

[0660] Output: Error and log data stored in the database

[0661] Step 4:

[0662] Data Summary

[0663] Subject: Server

[0664] Input: Stored error and log data

[0665] Processing: The server summarizes the stored error and log data using text analysis algorithms (e.g., natural language processing libraries).

[0666] Output: Summarized error and log data

[0667] Step 5:

[0668] Weekly Report Generation

[0669] Subject: Server

[0670] Input: Digest error and log data

[0671] Processing: The server generates weekly reports in HTML format based on the summary results, organized by specific topics.

[0672] Output: Generated weekly report

[0673] Step 6:

[0674] Report provision

[0675] Subject: Server

[0676] Input: Generated weekly report

[0677] Processing: The server provides weekly reports to the plant manager or maintenance team either in a format viewable in a web browser or via email.

[0678] Output: Weekly reports provided to plant managers and maintenance teams

[0679] Step 7:

[0680] Customization and Notifications

[0681] Subject: Server

[0682] Input: User interest data, keywords and topic specifications

[0683] Processing: The server customizes weekly reports based on user interests and notifies users when updates occur on specific keywords or topics using the Smart Alerts feature.

[0684] Output: User-customized weekly reports and update notifications

[0685] Step 8:

[0686] Archive and Search

[0687] Subject: Server

[0688] Input: Generated weekly report

[0689] Processing: The server archives all generated weekly reports and allows users to search historical data. For this purpose, it uses a text search engine (e.g. Elasticsearch).

[0690] Output: Searchable archive data

[0691] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0692] overview

[0693] This invention is a system for efficiently acquiring, summarizing, saving, and sharing search results and question-and-answer data that users obtain on a daily basis, and also combines an emotion engine that recognizes the user's emotions. This system includes a user terminal, a server, and various programs that link these.

[0694] User terminal operation

[0695] It collects search results and dialogue data from generative AI (e.g., question-answering systems) during a user's normal internet use. The user's device acquires this data through the browser history and an interface to the generative AI. The acquired data is sent to an emotion engine, which evaluates the user's emotional state. The user's device then sends the collected data and the evaluation of the emotional state to the server.

[0696] Data processing on the server

[0697] The server receives data sent from the user's device and stores it in a database. The received data is summarized using a text analysis algorithm and prioritized for processing based on the evaluation results of the emotion engine. A weekly report is automatically generated based on the summary results. The weekly report also includes an analysis of the user's emotional state.

[0698] Generate and view weekly reports

[0699] The weekly report contains a week's worth of summary data and is divided into specific topics, such as information on "Python programming" or "artificial intelligence," depending on the user's interests. The generated weekly report is delivered to the user's device or saved in a format that can be viewed in a web browser. The report is interactive, allowing users to click to view more detailed information.

[0700] Use of emotion engine

[0701] The user device is equipped with an emotion engine that recognizes emotions from the user's tone of voice, facial expressions, and text input. For example, if a user asks a question about "Python programming" and expresses dissatisfaction or confusion, this information is transmitted to the server, and the relevant content is highlighted in the weekly report.

[0702] Customization features

[0703] Users can customize the content of their reports based on their interests. The server filters the collected data based on user-specified topics and keywords to generate customized reports. The server also provides a smart alert feature that notifies users when new information becomes available.

[0704] Archive function

[0705] The server archives all generated weekly reports and provides search functionality for them as needed, allowing users to search past reports based on specific topics, keywords, or dates and reuse information.

[0706] Specific examples

[0707] For example, if a user searches for "Python programming" and the emotion engine detects feelings of dissatisfaction or confusion, the user's device will send this information to the server. The server will prioritize and summarize this data and include it as a special section in the weekly report. After a week, the user will receive a report with a detailed explanation of the topic that confused them. The user can then browse the report and use the interactive features to find out more details. Smart alerts will also instantly notify users when new useful information about "Python programming" becomes available.

[0708] This allows users to efficiently gather information and solidify their knowledge. In today's information overload environment, this system will be a powerful tool for supporting intellectual activities while taking into account the user's subjective feelings.

[0709] The processing flow will be explained below.

[0710] Step 1:

[0711] A user searches the Internet. The user's device retrieves search results from the browser history. For example, search results related to "Python programming" and "artificial intelligence" are collected from the browser history.

[0712] Step 2:

[0713] The user device uses an emotion engine to analyze the user's emotional state from the search results and question-and-answer data it obtains, for example, by analyzing the content of text input, voice tone, and facial expressions to evaluate the user's emotional state.

[0714] Step 3:

[0715] The user device formats the collected search results and emotional state ratings and sends them to the server. This data is sent to the server using an HTTP POST request.

[0716] Step 4:

[0717] The server receives data sent from the user terminal and stores it in a database, including search results, question-and-answer data, and the user's emotional state.

[0718] Step 5:

[0719] The server then summarises the stored data using a text analysis algorithm, taking into account the user's emotional state and prioritising particularly important information or content closely related to the user's emotions.

[0720] Step 6:

[0721] The server then uses the summarized data to generate a weekly report customized for each user, with analysis and relevant information based on their emotional state, including detailed explanations and additional information on topics that confuse the user.

[0722] Step 7:

[0723] The generated weekly report is delivered to the user terminal or stored in a web interface, and the user terminal displays the received or retrieved weekly report to the user.

[0724] Step 8:

[0725] Users can view weekly reports and click on each summary to view more detailed information. Interactive features allow users to learn more about specific topics.

[0726] Step 9:

[0727] Users can customize the content of reports based on their interests, and the server generates customized reports by filtering the collected data based on user-specified topics and keywords.

[0728] Step 10:

[0729] The server notifies users when there is new information about specific keywords or topics, using the Smart Alert function to instantly deliver the latest information to users' devices.

[0730] Step 11:

[0731] The server archives all generated weekly reports and makes them searchable as needed, allowing users to search past reports based on specific topics, keywords, or dates, and reuse information.

[0732] Example 2

[0733] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0734] In today's information-overloaded world, it is important to efficiently retrieve, consolidate, and effectively utilize user search results and question-answer data. However, existing systems lack the functionality to prioritize information based on the user's emotional state and customize reports accordingly. Furthermore, archiving and searching for old information is also inadequate, making it difficult for users to reuse past information.

[0735] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0736] In this invention, the server includes means for acquiring search result and question-and-answer data collected from the user terminal, means for transmitting the acquired data to an emotion engine to evaluate the user's emotional state, means for transmitting data including the evaluation results to the server, means for summarizing the transmitted data in the server, means for generating a weekly report based on the summarized data and the emotion evaluation results, and means for providing an interactive function for viewing the weekly report. This enables prioritization of information and generation of customized reports taking into account the user's emotional state, and also enables effective retrieval and reuse of past information.

[0737] A "user terminal" is a device that a user uses to access the Internet, and includes a PC, smartphone, tablet, etc.

[0738] "Search results" refers to the list of information that a user can obtain by using an Internet search engine.

[0739] "Question-answer data" refers to data that includes questions posed to generative AI by users and the AI's answers to those questions.

[0740] An "emotion engine" refers to a system that recognizes and evaluates emotions from a user's tone of voice, facial expressions, text input, etc.

[0741] "Server" refers to a computer that operates on a network and processes and stores data sent from user devices.

[0742] A "summarization method" is a method that uses a text analysis algorithm to extract the main parts of the transmitted data and present them in a concise form.

[0743] A "Weekly Report" is a report that compiles a week's worth of summary data and categorizes it by specific topic.

[0744] "Interactive function" refers to a function that allows a user to view detailed information by clicking on a link in a report.

[0745] "Customization methods" are ways to tailor and change the content of a report based on user interests.

[0746] "Means for notifying" refers to a method for notifying the user when new information is generated.

[0747] "Archiving means" refers to a method for storing generated weekly reports as historical records and making them available for later retrieval.

[0748] MODE FOR CARRYING OUT THE INVENTION

[0749] overview

[0750] This invention is a system for efficiently acquiring, summarizing, saving, and sharing search results and question-and-answer data that users obtain on a daily basis, and also combines an emotion engine that recognizes the user's emotions. This system includes a user terminal, a server, and various programs that link these.

[0751] User terminal operation

[0752] The user device collects the search results the user has obtained online and data on their interactions with the generative AI. Specifically, it obtains search keywords, questions, and conversation history through browser history data and the generative AI's API. It also uses an emotion engine to evaluate the user's emotional state based on their tone of voice, facial expression, and text input. The results of this emotional state evaluation are sent to the server along with the collected data.

[0753] Data processing on the server

[0754] The server receives data sent from the user's device and stores it in a relational database (e.g., MySQL, PostgreSQL). The received data is summarized using a text analysis algorithm (e.g., NLTK, SpaCy). Based on this summary data and the evaluation results of the emotion engine, the server automatically generates a weekly report. A template engine for report generation (e.g., Jinja2) is used to create a report that highlights the most important content based on the user's emotion evaluation results.

[0755] Generate and view weekly reports

[0756] The generated weekly reports are generated in a visually friendly format using HTML and CSS. The reports are delivered to the user's device or saved in a format that can be viewed in a web browser. The reports include interactive features, allowing users to click to view more detailed information.

[0757] Customize reports

[0758] Users can customize the content of their reports based on their interests. The server generates a customized report by filtering relevant information in the database based on topics and keywords specified by the user. For example, a report can be tailored to include only information about "AI." The server also notifies users when new information becomes available.

[0759] Archive function

[0760] The server stores an archive of all generated weekly reports and provides users with the ability to search past reports. Users can search past reports based on specific topics, keywords, or dates, allowing them to reuse information. This search functionality can utilize search engine technology (e.g., Elasticsearch).

[0761] Specific examples

[0762] For example, if a user searches for "Python programming" and the emotion engine detects feelings of dissatisfaction or confusion, the user's device will send this information to the server. The server will prioritize and summarize this data and include it as a special section in the weekly report. After a week, the user will receive a report with a detailed explanation of the topic that confused them. The user can then view the report and use the interactive features to find out more details. Additionally, the smart alert feature will instantly notify the user when new useful information about "Python programming" becomes available.

[0763] Example prompt sentence:

[0764] "Explain how you can measure how users learn about Python programming, summarize that data, and generate a report based on the sentiment engine's assessment."

[0765] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0766] Step 1:

[0767] Data collection from user devices

[0768] When a user searches or interacts with generative AI in a web browser, their history data and interaction data are collected. Specifically, data such as search keywords and conversation content is collected using the browser's history API and the generative AI's API.

[0769] Input: User search results, question and answer data

[0770] Output: Acquired historical data, interaction data

[0771] Step 2:

[0772] Emotional state assessment by emotion engine

[0773] The acquired data is sent to the emotion engine, which recognizes emotions from the user's tone of voice, facial expressions, and text input, and generates an evaluation result. Specifically, it uses voice analysis, image analysis, and natural language processing technology to perform emotion evaluation.

[0774] Input: historical data, dialogue data, user voice, facial expressions, text input

[0775] Output: Emotion evaluation result (e.g., confusion, dissatisfaction)

[0776] Step 3:

[0777] Send data and evaluation results to the server

[0778] The user device sends the collected history data, dialogue data, and emotion evaluation results to the server. Specifically, this data is compiled in JSON format and uploaded to the server using the HTTPS protocol.

[0779] Input: History data, dialogue data, emotion evaluation results

[0780] Output: Data sent to the server

[0781] Step 4:

[0782] Data storage on the server

[0783] The server stores the received data in a database. Specifically, it uses a relational database (e.g., MySQL or PostgreSQL) to classify and store the data by user ID.

[0784] Input: Data sent to the server

[0785] Output: Data stored in the database

[0786] Step 5:

[0787] Text Analysis and Data Summarization

[0788] The server summarizes the stored data using a text analysis algorithm, specifically using a natural language processing library (e.g., NLTK or SpaCy) to extract important keywords and sentences and generate a summary.

[0789] Input: Data stored in a database

[0790] Output: Summarized text data

[0791] Step 6:

[0792] Generate weekly reports

[0793] The server automatically generates weekly reports based on the summarized data. Specifically, it uses a template engine (e.g., Jinja2) for report generation to create reports based on user interest topics and sentiment assessment results.

[0794] Input: Summarized text data, emotion evaluation results

[0795] Output: Generated weekly report

[0796] Step 7:

[0797] Report distribution and viewing

[0798] The server then delivers the generated weekly report to the user's device or saves it in a format that can be viewed in a web browser. Specifically, it creates a visually appealing report using HTML and CSS and delivers it to the user via email or in-app notification.

[0799] Input: Generated weekly report

[0800] Output: Reports delivered to user devices, reports viewable in web browsers

[0801] Step 8:

[0802] Customize reports

[0803] Users can customize the content of the report based on their interests, and the server generates a customized report by filtering relevant information in the database based on user-specified topics and keywords.

[0804] Input: User's interest topics, keywords

[0805] Output: Customized weekly report

[0806] Step 9:

[0807] Using the archive function

[0808] The server stores an archive of all generated weekly reports and allows users to search past reports using search engine technology (e.g., Elasticsearch) based on specified topics, keywords, and dates.

[0809] Input: Past Weekly Report, Search Query

[0810] Output: Search results as a report

[0811] (Application example 2)

[0812] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0813] In today's information overload environment, users face the challenge of efficiently gathering useful information and solidifying knowledge. Furthermore, systems are needed to improve the user experience by providing information tailored to the user's emotional state. However, current systems do not adequately consider the user's emotions when providing information, and are unable to prioritize information appropriately. To solve this problem, technology is needed that can evaluate the user's emotional state and organize, summarize, and recommend information based on that information.

[0814] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0815] In this invention, the server includes a means for acquiring search result and question-and-answer data collected from a user terminal, a means for evaluating the emotional state of the acquired data, and a means for summarizing the transmitted data in the server. This allows for efficient summarization of important information and generation of a weekly report, taking into account the user's emotional state. Furthermore, the weekly report can be customized based on the user's level of interest and notified of the latest information on specific keywords or topics, improving the user experience. Furthermore, past weekly reports can be archived and made searchable, allowing for quick reuse of necessary information.

[0816] A "user terminal" is a device that can connect to the Internet and run applications, and is a device that allows a user to obtain and transmit data.

[0817] A "search result" is a list of information that a user obtains from a source such as the Internet based on a particular query.

[0818] "Question-answer data" refers to text data of answers returned to users' questions by generative AI models, etc.

[0819] A "server" is a computer or computer system on a network that can receive, store, process, and transmit data.

[0820] "Emotional state" refers to the psychological state detected from the user's tone of voice, facial expressions, and text input.

[0821] "Summarizing" is the process of shortening acquired data or information and concisely summarizing only the main points.

[0822] A "weekly report" is a report that summarizes one week's worth of data and categorizes it into specific topics and provides it to the user.

[0823] "Interactive features" are features that allow users to click on information in a report to obtain more information.

[0824] "Customization" means filtering information based on the user's interests or specified topics to provide information that meets specific criteria.

[0825] "Means for notifying users of the latest information" refers to a function that instantly notifies users when new information related to a specific keyword or topic is generated.

[0826] "Archive" is a data storage function that saves the generated weekly reports so that they can be searched and referenced later.

[0827] "Searchable" refers to the ability to efficiently find and display specific information from stored data.

[0828] The present invention provides a system for improving the efficiency of information acquisition for users and providing information preferentially according to their emotional state. This system is generally composed of a user terminal, a server, and various programs that link these together.

[0829] First, a user device is a device that can connect to the Internet and run applications, such as a smartphone or PC. When a user enters a search query, the user device collects information using the Internet. When the user asks a question to the generative AI model, the interaction data is also collected. The collected data is sent to an emotion engine on the user device to evaluate the emotional state.

[0830] The emotion engine detects emotions from the user's tone of voice, facial expressions, and text input, and evaluates how the user feels about the content. This emotional state evaluation data is sent to the server along with search results and question-and-answer data.

[0831] The server receives data sent from the user's device and stores it in a database. It then summarizes the data using text analysis algorithms. The software used here includes natural language processing libraries (e.g., TextBlob). The summarized data is prioritized based on the emotional state assessment results and compiled into a weekly report.

[0832] The weekly reports are customized based on the user's interests, categorizing information according to specific topics or keywords. Additionally, when important new information occurs, the server notifies the user of the latest information related to the specific keyword or topic, allowing the user to get the latest information immediately.

[0833] In addition, all weekly reports are archived on the server, allowing users to search and reuse past reports. For example, if a user wants to review information about "Python programming project construction" again, they can easily search for past reports using the archive function.

[0834] Here are some examples of specific prompts:

[0835] "Python programming project construction"

[0836] When a user enters this query, the user's device collects related news articles and question-and-answer data, evaluates the user's emotional state through an emotion engine, and sends the data to the server, which then generates summary data and provides it to the user as a weekly report.

[0837] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0838] Step 1:

[0839] A user enters a search query.

[0840] Input: User's search query (e.g. "Python programming project build")

[0841] Specific operation: The user inputs a search query through an input device on the user terminal.

[0842] Step 2:

[0843] The user terminal collects relevant information from the Internet.

[0844] Input: search query

[0845] Output: A list of search results (news articles, web page links, etc.)

[0846] Specific operation: The user device connects to the Internet and uses a search engine API to collect search results based on the query.

[0847] Step 3:

[0848] The user inputs dialogue data into the generative AI model.

[0849] Input: User question (e.g., "How do I get started on a project in Python programming?")

[0850] Output: Question and answer data (answers from the generative AI model)

[0851] Specific operation: The generative AI model installed on the user's device answers the user's questions and saves the data.

[0852] Step 4:

[0853] The user terminal uses an emotion engine to assess the emotional state.

[0854] Input: Search results, question and answer data

[0855] Output: Emotional state assessment (e.g., confusion, frustration, joy)

[0856] Specific operation: The camera and microphone on the user's device are used to analyze facial expressions and voice tone, and the emotion engine analyzes the text data to evaluate emotions.

[0857] Step 5:

[0858] The user terminal transmits the collected data and the evaluation results of the emotional state to the server.

[0859] Input: Search results, Q&A data, and emotional state assessment results

[0860] Output: Data transfer to the server

[0861] Specific operation: Using the communication module of the user terminal, the collected data and the evaluation results of the emotional state are sent to the server.

[0862] Step 6:

[0863] The server stores the received data in a database.

[0864] Input: Collected data, emotional state assessment results

[0865] Output: Data stored in the database

[0866] Specific operation: The server analyzes the received data and stores it in the database in the appropriate format.

[0867] Step 7:

[0868] The server uses text analysis algorithms to summarize the data.

[0869] Input: Data stored in the database

[0870] Output: Summary data

[0871] Specific behavior: The server summarizes the text data using a natural language processing library (e.g., TextBlob).

[0872] Step 8:

[0873] The server generates a weekly report based on the emotional state assessment.

[0874] Input: Summary data, emotional state assessment results

[0875] Output: Weekly report

[0876] What it does: The server combines the summary data with the emotional state to generate a customized weekly report based on the user's interests.

[0877] Step 9:

[0878] The server delivers weekly reports to user terminals and provides interactive functions.

[0879] Input: Weekly Report

[0880] Output: A report in a format that can be viewed on the user's terminal

[0881] Specific operation: The server delivers the report to the user's device and allows the user to click on information in the report to obtain more information.

[0882] Step 10:

[0883] The server archives past weekly reports and makes them searchable by users.

[0884] Input: Generated weekly report

[0885] Output: Archived report data

[0886] What it does: The server archives past weekly reports, allowing users to quickly search for the information they need.

[0887] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0888] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0889] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0890] [Third embodiment]

[0891] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0892] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0893] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0894] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0895] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0896] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0897] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0898] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0899] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0900] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0901] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0902] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0903] overview

[0904] The present invention is a system for efficiently acquiring, summarizing, storing, and sharing search results and question-and-answer data that users routinely obtain. The system includes a user terminal, a server, and various programs that link these.

[0905] User terminal operation

[0906] It collects search results from users' normal internet use and dialogue data from generative AI (e.g., question-answering systems). The user's device obtains this data through the browser history and an interface to the generative AI. The collected data is then sent to a server.

[0907] Data processing on the server

[0908] The server receives data sent from user devices and stores it in a database. The received data is summarized using a text analysis algorithm. A weekly report is automatically generated based on this summary. The weekly report is generated in HTML format or other formats to make it interactive, allowing users to click to view more detailed information.

[0909] Generate and view weekly reports

[0910] The weekly report contains a week's worth of summary data and is divided into specific topics, such as information on "Python programming" or "artificial intelligence," depending on the user's interests. The generated weekly report is either delivered to the user's device or saved in a format that can be viewed in a web browser.

[0911] Customization features

[0912] Users can customize the content of their weekly reports based on their interests. Users can specify topics and keywords of interest, and the server will collect data and generate summaries based on that information. The system also offers a smart alert function that notifies users when new information becomes available, allowing users to stay up to date with the latest information.

[0913] Archive function

[0914] The server archives all generated weekly reports and provides search functionality where necessary, allowing users to search past reports based on specific topics, keywords, or dates, and reuse information.

[0915] Specific examples

[0916] For example, if a user searches for "Python programming" and "artificial intelligence," the user's device collects the search results and sends them to the server. The server summarizes this data and generates a weekly report containing a week's worth of summaries. The user can view the report, check the summaries for each topic, and click on the summary item of interest to access more information. Additionally, the smart alert function allows users to receive instant notifications when new information related to "Python programming" or "artificial intelligence" appears.

[0917] This allows users to efficiently gather information and solidify their knowledge. This system will be a powerful tool to support intellectual activities in today's information overload environment.

[0918] The processing flow will be explained below.

[0919] Step 1:

[0920] A user searches the Internet. The user's device retrieves search results from the browser history. For example, search results related to "Python programming" and "artificial intelligence" are collected from the browser history.

[0921] Step 2:

[0922] The user terminal formats the collected search results and data from the question-answering system and sends them to the server using an HTTP POST request.

[0923] Step 3:

[0924] The server receives the data sent from the user terminal and stores it in a database. The server stores the captured data in text format for later summarization.

[0925] Step 4:

[0926] The server processes the received data and summarizes its contents. It uses text analysis algorithms (e.g., natural language processing techniques) to generate a summary of the data.

[0927] Step 5:

[0928] The server generates weekly reports based on the summarized data, organized chronologically and categorized by major topics, and structured in formats such as HTML and PDF.

[0929] Step 6:

[0930] The server delivers the generated weekly report to the user terminal or stores it in a web interface, and the user terminal displays the received or retrieved weekly report to the user.

[0931] Step 7:

[0932] Users can view weekly reports and click on each summary to view more detailed information. Interactive features allow users to learn more about specific topics.

[0933] Step 8:

[0934] Users can customize the content of reports based on their interests, and the server generates customized reports by filtering the collected data based on user-specified topics and keywords.

[0935] Step 9:

[0936] The server notifies users when there is new information about specific keywords or topics, using the Smart Alert function to instantly deliver the latest information to users' devices.

[0937] Step 10:

[0938] The server archives all generated weekly reports and makes them searchable as needed, allowing users to search past reports based on specific topics, keywords, or dates, and reuse information.

[0939] Example 1

[0940] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0941] In today's world, the amount of information available via the Internet is enormous, making it difficult for users to efficiently obtain, organize, and manage the accurate and useful information they desire. In particular, the amount of data obtained through daily Internet searches and interactions with generative AI is constantly increasing. To effectively utilize this data, appropriate data collection, summarization, storage, and organization are required. Furthermore, there is a demand for customization functions based on user interests and immediate notifications of the latest information.

[0942] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0943] In this invention, the server includes means for acquiring search results and interaction data collected from the user terminal, means for transmitting the acquired data to an information processing device, means for summarizing the transmitted data in the information processing device, means for generating periodic reports based on the summarized data, means for providing an interactive function for viewing the periodic reports, means for customizing the content of the periodic reports based on the user's interests, means for notifying the user of the latest information related to specific topics of interest, and means for saving past periodic reports and making the saved reports searchable. This allows users to efficiently obtain the information they need from a vast amount of information and review it in summarized form. Furthermore, users can instantly obtain the latest content of information of interest and easily access and reuse past information.

[0944] A "user terminal" is an information processing device used by a user, and refers to devices such as a personal computer, smartphone, or tablet.

[0945] "Search Results" refers to a list of information displayed in response to a query entered by a user using an Internet search engine.

[0946] "Dialogue data" refers to data that includes the content of questions and responses exchanged between a user and a generative AI model.

[0947] The term "information processing device" refers to a device such as a server that receives, stores, analyzes, and summarizes data sent from a user terminal.

[0948] "Summarizing" refers to the act of analyzing acquired data, extracting important information, and summarizing it concisely.

[0949] "Periodic Report" refers to a report generated based on a summary of data over a given period of time (e.g., one week).

[0950] "Interactive function" refers to a function that allows users to operate the device interactively and access detailed information by clicking or tapping.

[0951] "Customizing" refers to the act of changing content or functionality based on a user's interests and needs.

[0952] "Notifying the latest information" refers to the act of notifying the user of newly acquired related information in real time.

[0953] "Storing" refers to the act of recording and retaining information in a database or storage device.

[0954] "Searchable" refers to the ability to find stored information based on specific criteria (such as keywords or dates).

[0955] This invention is a system for efficiently acquiring, summarizing, saving, and sharing search results and dialogue data with generative AI models that users regularly obtain. This system mainly includes a user terminal, a server, an information processing device, a browser, and programs for the generative AI model.

[0956] System configuration and operation

[0957] 1. User Device

[0958] User terminals include devices such as personal computers, smartphones, and tablets. Search results and interaction data with the generative AI model that users use during normal Internet usage are collected. The user terminal acquires this data through the browser history and the interface to the generative AI model, and temporarily stores the data in local storage. The acquired data is then sent to an information processing device. Data is sent using HTTP POST requests, etc.

[0959] 2. Servers and Information Processing Devices

[0960] The server and information processing device are responsible for receiving and storing data sent from user terminals. The information processing device manages data using database software such as MySQL or MongoDB. The sent data is analyzed and summarized using a text analysis algorithm (such as NLTK or spaCy). Furthermore, periodic reports containing one week's worth of data are generated based on the summary results. The generated reports are formatted in HTML and can be used interactively by users.

[0961] 3. Report Delivery and Display

[0962] The server periodically delivers the generated reports to the user's device. Delivery methods include email and notification API. Users can check the delivered reports on their device and access detailed information by clicking on each item. For example, if a user clicks on information about "Python programming" or "artificial intelligence," they can view a summary and detailed information about that item.

[0963] 4. Customization features

[0964] Users can specify topics and keywords of interest through a web interface. The server uses the information to customize data collection and summarization, generating reports tailored to the user's needs. The server also notifies users in real time when new information related to a topic of interest emerges, allowing users to stay up to date with the latest information immediately.

[0965] 5. Archive function

[0966] The server archives all generated periodic reports in a database, allowing users to search for and reuse information from past reports based on specific topics, keywords, or dates, allowing them to efficiently find and review the information they need.

[0967] Specific examples

[0968] For example, if a user searches for "Python programming" and "artificial intelligence," the user's device collects the search results and sends them to the server. The server then summarizes this data using a text analysis algorithm and generates periodic reports containing a week's worth of summaries. Users can view the reports, check the summaries for each topic, and click on the summary items they are interested in to access more information. Additionally, when new information related to "Python programming" or "artificial intelligence" appears, they can receive instant notifications using the smart alert function.

[0969] Prompt Sentence Examples

[0970] 1. "What's the latest news about Python programming?"

[0971] 2. "Please summarize recent developments in the field of artificial intelligence."

[0972] This system allows users to efficiently retrieve the information they need from a vast amount of information and check it in a summarized form. It also allows users to instantly obtain the latest information about information they are interested in, and easily access and reuse past information.

[0973] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0974] Step 1:

[0975] The user device monitors the browser history and interaction data with the generated AI model.

[0976] When a user performs a search, the query and results are saved in the browser history, along with any interactions with the generative AI model.

[0977] Input: User search queries and interaction data with the generative AI model.

[0978] Output: Search history and interaction data saved in local storage.

[0979] What it does: When a user searches for "Python programming," the system retrieves the search query and results. It also collects conversational data when the AI ​​model is asked questions about recent advances in artificial intelligence.

[0980] Step 2:

[0981] The user terminal periodically transmits data stored in the local storage to the information processing device.

[0982] Submissions are made using HTTP POST requests, sending data to a specific endpoint.

[0983] Input: Search history and interaction data stored in local storage.

[0984] Output: Data sent over a broadband network to a server.

[0985] Specific behavior: Search history and interaction data are periodically sent in batches to the http: / / example.com / upload endpoint.

[0986] Step 3:

[0987] The server analyzes the received data and stores it in a database.

[0988] The server receives the HTTP request, parses it, and stores it in a database.

[0989] Input: Browser history and interaction data sent to the server.

[0990] Output: Search history and interaction data stored in a database.

[0991] Specific operation: Executes an INSERT statement to the database and saves the submitted search query and conversation content.

[0992] Step 4:

[0993] The server summarizes the data stored in the database.

[0994] Use text analysis algorithms (e.g., NLTK or spaCy) to extract and summarize important information.

[0995] Input: Search history and interaction data stored in a database.

[0996] Output: Summarized text data.

[0997] What it does: It extracts search history from the database and extracts key points about "Python programming." It also generates an overview of "Recent Advances in Artificial Intelligence" from AI interaction data.

[0998] Step 5:

[0999] The server generates periodic reports based on the summarized data.

[1000] The generated summary data is embedded in an HTML template and formatted as a report.

[1001] Input: Abstracted text data.

[1002] Output: Scheduled reports in HTML format.

[1003] Specific operation: Using Python scripts etc., generate a weekly report by properly arranging the summary data in HTML format.

[1004] Step 6:

[1005] The server generates periodic reports and delivers them to the user terminal.

[1006] Delivery is done via email or notification API.

[1007] Input: Scheduled report in HTML format.

[1008] Output: Report delivered to user terminal.

[1009] Specific operation: Uses the email sending API to send a report to the specified email address, and also uses the notification API to send a notification to the user's device.

[1010] Step 7:

[1011] Users review periodic reports and click on items of interest to access more information.

[1012] Users open the delivered report in a browser or email client.

[1013] Input: HTML format report delivered to user terminal.

[1014] Output: Detailed information displayed in a browser.

[1015] What it does: When a user clicks on a summary item about "Python programming," more information about it opens in a new tab or window.

[1016] Step 8:

[1017] Users specify topics or keywords that interest them.

[1018] Users register topics of interest through a web interface.

[1019] Input: User-specified topics or keywords.

[1020] Output: User interest data stored on the server.

[1021] Specific operation: The user specifies a topic such as "machine learning" or "data science," and it is saved on the server.

[1022] Step 9:

[1023] The server customizes the data collection and summarization process based on the user's interests.

[1024] The server adjusts the collection and summarization algorithms based on the specified data of interest.

[1025] Input: User interest data.

[1026] Output: Customized summary data and reports.

[1027] What it does: Adjust database queries to prioritize search results and interaction data related to "machine learning" and "data science."

[1028] Step 10:

[1029] If the server detects new relevant information, it notifies the user in real time.

[1030] A notification system will be in place when new information is added to the database.

[1031] Input: New information added to the database.

[1032] Output: Notification sent to the user's device.

[1033] What it does: When new information about "Python programming" or "artificial intelligence" is added, users will receive an email or app notification instantly.

[1034] Step 11:

[1035] The server stores past periodic reports and makes them available for user retrieval.

[1036] Saved reports are added to the search index.

[1037] Enter: Periodic Reports.

[1038] Output: Searchable archive data.

[1039] What it does: Periodically generated reports are archived in a database, and when a user searches for "Python programming" or a specific date, relevant past reports are displayed.

[1040] (Application example 1)

[1041] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1042] Conventional factory robot management and maintenance systems lack a means to efficiently collect and organize information about errors and troubleshooting. This prevents factory managers and maintenance teams from quickly grasping the necessary information, resulting in delayed responses and reduced efficiency. There are also insufficient methods for searching and utilizing past error and repair history.

[1043] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1044] In this invention, the server includes means for acquiring search result and question and answer data collected from user terminals, means for transmitting the acquired data to the server, means for summarizing the transmitted data in the server, means for generating a weekly report based on the summarized data, means for providing an interactive function for viewing the weekly report, means for collecting error and log data generated by the robot during operation, means for transmitting the collected error and log data to the server, means for summarizing the received error and log data in the server to generate a weekly report, and means for providing the generated weekly report in a format viewable by factory managers and maintenance teams. This allows for efficient collection and organization of information on errors and troubleshooting that occur in the factory, enabling factory managers and maintenance teams to quickly grasp the necessary information, thereby enabling faster responses and improved work efficiency.

[1045] "User terminal" refers to an electronic device that a user operates to search for information or answer questions.

[1046] "Server" refers to a central processing system that receives, stores, and processes data sent from user terminals.

[1047] "Search results" refers to a collection of information obtained as a result of a search performed by a user to find specific information on the Internet.

[1048] "Question-answering data" refers to information on questions and answers posed through dialogue systems such as generative AI.

[1049] "Summarization" refers to the process of simplifying a large amount of collected data and extracting only the important information.

[1050] A "Weekly Report" is a report that summarizes data collected over the past week and organizes it by specific topic.

[1051] "Interactive function" refers to a function that allows the user to display detailed information or perform specific operations through user operations.

[1052] An "error" refers to an abnormal condition recorded when the robot does not operate normally during a task.

[1053] "Log data" refers to data that records detailed operational history, such as the robot's work content and error occurrence status.

[1054] "Factory manager" refers to an official in charge of the operation and management of a factory.

[1055] "Maintenance team" refers to the group of workers responsible for maintaining and repairing equipment and systems within a factory.

[1056] Overall system overview

[1057] This invention is a system that includes a user terminal, a server, and various programs that link them together to efficiently support the management and maintenance of robots in factories. In particular, it aims to efficiently collect, summarize, store, and share information on errors and troubleshooting that occur during robot operation.

[1058] User device and robot behavior

[1059] The user's device and the robot receive real-time information about errors and troubleshooting that occur during work. This information is sent to a server via the Internet. The robot must have an Internet connection and transmits data using HTTP requests.

[1060] Data processing on the server

[1061] The server receives, stores, and processes a wide variety of data. It houses a database, where the received error and log data is stored. The stored data is summarized using text analysis algorithms. Based on the results of these summaries, regular weekly reports are automatically generated.

[1062] Generate and view weekly reports

[1063] The generated weekly report summarizes the data collected over the past week and is organized by specific topics. The report is generated in HTML format and is presented to the user in an interactive format, allowing the user to click for more information, such as error codes and how to troubleshoot them.

[1064] Customization features

[1065] Users can customize the content of their weekly reports based on their interests, and a smart alert feature is also provided, allowing users to receive instant notifications when new information emerges on specific keywords or topics, helping them stay up to date with the latest information.

[1066] Archive function

[1067] The server archives all generated weekly reports and provides search functionality for them as needed. Users can search past reports based on specific topics, keywords, or dates, and reuse information. This functionality allows for quick access to past error and troubleshooting information.

[1068] Specific examples

[1069] For example, if Robot A in a factory encounters a connection failure with error code 404 while in operation, the robot sends the error information to the server. The server summarizes the received error information and generates a weekly report stating, "An error has occurred regarding a connection failure for Part A. Please refer to the link below for detailed troubleshooting." Factory managers and maintenance teams can view summaries of each topic in this report and click on the summary items of interest to access detailed information. Additionally, when new information occurs, they can receive instant notifications via the smart alert function.

[1070] Prompt Sentence Examples

[1071] "Can you tell me more about the robot error code 404? What troubleshooting steps would work?"

[1072] This system will be a powerful tool for efficient robot management and maintenance within factories, allowing factory managers and maintenance teams to respond quickly when errors occur and significantly improving overall work efficiency.

[1073] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1074] Step 1:

[1075] Data collection

[1076] Subject: Device

[1077] Input: Errors and log data that occur while the robot is working

[1078] Processing: The terminal receives real-time error and log data from the robot, including error codes and troubleshooting information.

[1079] Output: Captured error and log data

[1080] Step 2:

[1081] Data transmission

[1082] Subject: Device

[1083] Input: Captured error and log data

[1084] Processing: The device sends the collected error and log data to the server using an HTTP request.

[1085] Output: Error and log data sent to the server

[1086] Step 3:

[1087] Data reception

[1088] Subject: Server

[1089] Input: Error and log data sent from the device

[1090] Processing: The server receives HTTP requests and stores error and log data in a database, using either an SQL or NoSQL database.

[1091] Output: Error and log data stored in the database

[1092] Step 4:

[1093] Data Summary

[1094] Subject: Server

[1095] Input: Stored error and log data

[1096] Processing: The server summarizes the stored error and log data using text analysis algorithms (e.g., natural language processing libraries).

[1097] Output: Summarized error and log data

[1098] Step 5:

[1099] Weekly Report Generation

[1100] Subject: Server

[1101] Input: Digest error and log data

[1102] Processing: The server generates weekly reports in HTML format based on the summary results, organized by specific topics.

[1103] Output: Generated weekly report

[1104] Step 6:

[1105] Report provision

[1106] Subject: Server

[1107] Input: Generated weekly report

[1108] Processing: The server provides weekly reports to the plant manager or maintenance team either in a format viewable in a web browser or via email.

[1109] Output: Weekly reports provided to plant managers and maintenance teams

[1110] Step 7:

[1111] Customization and Notifications

[1112] Subject: Server

[1113] Input: User interest data, keywords and topic specifications

[1114] Processing: The server customizes weekly reports based on user interests and notifies users when updates occur on specific keywords or topics using the Smart Alerts feature.

[1115] Output: User-customized weekly reports and update notifications

[1116] Step 8:

[1117] Archive and Search

[1118] Subject: Server

[1119] Input: Generated weekly report

[1120] Processing: The server archives all generated weekly reports and allows users to search historical data. For this purpose, it uses a text search engine (e.g. Elasticsearch).

[1121] Output: Searchable archive data

[1122] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1123] overview

[1124] This invention is a system for efficiently acquiring, summarizing, saving, and sharing search results and question-and-answer data that users obtain on a daily basis, and also combines an emotion engine that recognizes the user's emotions. This system includes a user terminal, a server, and various programs that link these.

[1125] User terminal operation

[1126] It collects search results and dialogue data from generative AI (e.g., question-answering systems) during a user's normal internet use. The user's device acquires this data through the browser history and an interface to the generative AI. The acquired data is sent to an emotion engine, which evaluates the user's emotional state. The user's device then sends the collected data and the evaluation of the emotional state to the server.

[1127] Data processing on the server

[1128] The server receives data sent from the user's device and stores it in a database. The received data is summarized using a text analysis algorithm and prioritized for processing based on the evaluation results of the emotion engine. A weekly report is automatically generated based on the summary results. The weekly report also includes an analysis of the user's emotional state.

[1129] Generate and view weekly reports

[1130] The weekly report contains a week's worth of summary data and is divided into specific topics, such as information on "Python programming" or "artificial intelligence," depending on the user's interests. The generated weekly report is delivered to the user's device or saved in a format that can be viewed in a web browser. The report is interactive, allowing users to click to view more detailed information.

[1131] Use of emotion engine

[1132] The user device is equipped with an emotion engine that recognizes emotions from the user's tone of voice, facial expressions, and text input. For example, if a user asks a question about "Python programming" and expresses dissatisfaction or confusion, this information is transmitted to the server, and the relevant content is highlighted in the weekly report.

[1133] Customization features

[1134] Users can customize the content of their reports based on their interests. The server filters the collected data based on user-specified topics and keywords to generate customized reports. The server also provides a smart alert feature that notifies users when new information becomes available.

[1135] Archive function

[1136] The server archives all generated weekly reports and provides search functionality for them as needed, allowing users to search past reports based on specific topics, keywords, or dates and reuse information.

[1137] Specific examples

[1138] For example, if a user searches for "Python programming" and the emotion engine detects feelings of dissatisfaction or confusion, the user's device will send this information to the server. The server will prioritize and summarize this data and include it as a special section in the weekly report. After a week, the user will receive a report with a detailed explanation of the topic that confused them. The user can then browse the report and use the interactive features to find out more details. Smart alerts will also instantly notify users when new useful information about "Python programming" becomes available.

[1139] This allows users to efficiently gather information and solidify their knowledge. In today's information overload environment, this system will be a powerful tool for supporting intellectual activities while taking into account the user's subjective feelings.

[1140] The processing flow will be explained below.

[1141] Step 1:

[1142] A user searches the Internet. The user's device retrieves search results from the browser history. For example, search results related to "Python programming" and "artificial intelligence" are collected from the browser history.

[1143] Step 2:

[1144] The user device uses an emotion engine to analyze the user's emotional state from the search results and question-and-answer data it obtains, for example, by analyzing the content of text input, voice tone, and facial expressions to evaluate the user's emotional state.

[1145] Step 3:

[1146] The user device formats the collected search results and emotional state ratings and sends them to the server. This data is sent to the server using an HTTP POST request.

[1147] Step 4:

[1148] The server receives data sent from the user terminal and stores it in a database, including search results, question-and-answer data, and the user's emotional state.

[1149] Step 5:

[1150] The server then summarises the stored data using a text analysis algorithm, taking into account the user's emotional state and prioritising particularly important information or content closely related to the user's emotions.

[1151] Step 6:

[1152] The server then uses the summarized data to generate a weekly report customized for each user, with analysis and relevant information based on their emotional state, including detailed explanations and additional information on topics that confuse the user.

[1153] Step 7:

[1154] The generated weekly report is delivered to the user terminal or stored in a web interface, and the user terminal displays the received or retrieved weekly report to the user.

[1155] Step 8:

[1156] Users can view weekly reports and click on each summary to view more detailed information. Interactive features allow users to learn more about specific topics.

[1157] Step 9:

[1158] Users can customize the content of reports based on their interests, and the server generates customized reports by filtering the collected data based on user-specified topics and keywords.

[1159] Step 10:

[1160] The server notifies users when there is new information about specific keywords or topics, using the Smart Alert function to instantly deliver the latest information to users' devices.

[1161] Step 11:

[1162] The server archives all generated weekly reports and makes them searchable as needed, allowing users to search past reports based on specific topics, keywords, or dates, and reuse information.

[1163] Example 2

[1164] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1165] In today's information-overloaded world, it is important to efficiently retrieve, consolidate, and effectively utilize user search results and question-answer data. However, existing systems lack the functionality to prioritize information based on the user's emotional state and customize reports accordingly. Furthermore, archiving and searching for old information is also inadequate, making it difficult for users to reuse past information.

[1166] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1167] In this invention, the server includes means for acquiring search result and question-and-answer data collected from the user terminal, means for transmitting the acquired data to an emotion engine to evaluate the user's emotional state, means for transmitting data including the evaluation results to the server, means for summarizing the transmitted data in the server, means for generating a weekly report based on the summarized data and the emotion evaluation results, and means for providing an interactive function for viewing the weekly report. This enables prioritization of information and generation of customized reports taking into account the user's emotional state, and also enables effective retrieval and reuse of past information.

[1168] A "user terminal" is a device that a user uses to access the Internet, and includes a PC, smartphone, tablet, etc.

[1169] "Search results" refers to the list of information that a user can obtain by using an Internet search engine.

[1170] "Question-answer data" refers to data that includes questions posed to generative AI by users and the AI's answers to those questions.

[1171] An "emotion engine" refers to a system that recognizes and evaluates emotions from a user's tone of voice, facial expressions, text input, etc.

[1172] "Server" refers to a computer that operates on a network and processes and stores data sent from user devices.

[1173] A "summarization method" is a method that uses a text analysis algorithm to extract the main parts of the transmitted data and present them in a concise form.

[1174] A "Weekly Report" is a report that compiles a week's worth of summary data and categorizes it by specific topic.

[1175] "Interactive function" refers to a function that allows a user to view detailed information by clicking on a link in a report.

[1176] "Customization methods" are ways to tailor and change the content of a report based on user interests.

[1177] "Means for notifying" refers to a method for notifying the user when new information is generated.

[1178] "Archiving means" refers to a method for storing generated weekly reports as historical records and making them available for later retrieval.

[1179] MODE FOR CARRYING OUT THE INVENTION

[1180] overview

[1181] This invention is a system for efficiently acquiring, summarizing, saving, and sharing search results and question-and-answer data that users obtain on a daily basis, and also combines an emotion engine that recognizes the user's emotions. This system includes a user terminal, a server, and various programs that link these.

[1182] User terminal operation

[1183] The user device collects the search results the user has obtained online and data on their interactions with the generative AI. Specifically, it obtains search keywords, questions, and conversation history through browser history data and the generative AI's API. It also uses an emotion engine to evaluate the user's emotional state based on their tone of voice, facial expression, and text input. The results of this emotional state evaluation are sent to the server along with the collected data.

[1184] Data processing on the server

[1185] The server receives data sent from the user's device and stores it in a relational database (e.g., MySQL, PostgreSQL). The received data is summarized using a text analysis algorithm (e.g., NLTK, SpaCy). Based on this summary data and the evaluation results of the emotion engine, the server automatically generates a weekly report. A template engine for report generation (e.g., Jinja2) is used to create a report that highlights the most important content based on the user's emotion evaluation results.

[1186] Generate and view weekly reports

[1187] The generated weekly reports are generated in a visually friendly format using HTML and CSS. The reports are delivered to the user's device or saved in a format that can be viewed in a web browser. The reports include interactive features, allowing users to click to view more detailed information.

[1188] Customize reports

[1189] Users can customize the content of their reports based on their interests. The server generates a customized report by filtering relevant information in the database based on topics and keywords specified by the user. For example, a report can be tailored to include only information about "AI." The server also notifies users when new information becomes available.

[1190] Archive function

[1191] The server stores an archive of all generated weekly reports and provides users with the ability to search past reports. Users can search past reports based on specific topics, keywords, or dates, allowing them to reuse information. This search functionality can utilize search engine technology (e.g., Elasticsearch).

[1192] Specific examples

[1193] For example, if a user searches for "Python programming" and the emotion engine detects feelings of dissatisfaction or confusion, the user's device will send this information to the server. The server will prioritize and summarize this data and include it as a special section in the weekly report. After a week, the user will receive a report with a detailed explanation of the topic that confused them. The user can then view the report and use the interactive features to find out more details. Additionally, the smart alert feature will instantly notify the user when new useful information about "Python programming" becomes available.

[1194] Example prompt sentence:

[1195] "Explain how you can measure how users learn about Python programming, summarize that data, and generate a report based on the sentiment engine's assessment."

[1196] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1197] Step 1:

[1198] Data collection from user devices

[1199] When a user searches or interacts with generative AI in a web browser, their history data and interaction data are collected. Specifically, data such as search keywords and conversation content is collected using the browser's history API and the generative AI's API.

[1200] Input: User search results, question and answer data

[1201] Output: Acquired historical data, interaction data

[1202] Step 2:

[1203] Emotional state assessment by emotion engine

[1204] The acquired data is sent to the emotion engine, which recognizes emotions from the user's tone of voice, facial expressions, and text input, and generates an evaluation result. Specifically, it uses voice analysis, image analysis, and natural language processing technology to perform emotion evaluation.

[1205] Input: historical data, dialogue data, user voice, facial expressions, text input

[1206] Output: Emotion evaluation result (e.g., confusion, dissatisfaction)

[1207] Step 3:

[1208] Send data and evaluation results to the server

[1209] The user device sends the collected history data, dialogue data, and emotion evaluation results to the server. Specifically, this data is compiled in JSON format and uploaded to the server using the HTTPS protocol.

[1210] Input: History data, dialogue data, emotion evaluation results

[1211] Output: Data sent to the server

[1212] Step 4:

[1213] Data storage on the server

[1214] The server stores the received data in a database. Specifically, it uses a relational database (e.g., MySQL or PostgreSQL) to classify and store the data by user ID.

[1215] Input: Data sent to the server

[1216] Output: Data stored in the database

[1217] Step 5:

[1218] Text Analysis and Data Summarization

[1219] The server summarizes the stored data using a text analysis algorithm, specifically using a natural language processing library (e.g., NLTK or SpaCy) to extract important keywords and sentences and generate a summary.

[1220] Input: Data stored in a database

[1221] Output: Summarized text data

[1222] Step 6:

[1223] Generate weekly reports

[1224] The server automatically generates weekly reports based on the summarized data. Specifically, it uses a template engine (e.g., Jinja2) for report generation to create reports based on user interest topics and sentiment assessment results.

[1225] Input: Summarized text data, emotion evaluation results

[1226] Output: Generated weekly report

[1227] Step 7:

[1228] Report distribution and viewing

[1229] The server then delivers the generated weekly report to the user's device or saves it in a format that can be viewed in a web browser. Specifically, it creates a visually appealing report using HTML and CSS and delivers it to the user via email or in-app notification.

[1230] Input: Generated weekly report

[1231] Output: Reports delivered to user devices, reports viewable in web browsers

[1232] Step 8:

[1233] Customize reports

[1234] Users can customize the content of the report based on their interests, and the server generates a customized report by filtering relevant information in the database based on user-specified topics and keywords.

[1235] Input: User's interest topics, keywords

[1236] Output: Customized weekly report

[1237] Step 9:

[1238] Using the archive function

[1239] The server stores an archive of all generated weekly reports and allows users to search past reports using search engine technology (e.g., Elasticsearch) based on specified topics, keywords, and dates.

[1240] Input: Past Weekly Report, Search Query

[1241] Output: Search results as a report

[1242] (Application example 2)

[1243] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1244] In today's information overload environment, users face the challenge of efficiently gathering useful information and solidifying knowledge. Furthermore, systems are needed to improve the user experience by providing information tailored to the user's emotional state. However, current systems do not adequately consider the user's emotions when providing information, and are unable to prioritize information appropriately. To solve this problem, technology is needed that can evaluate the user's emotional state and organize, summarize, and recommend information based on that information.

[1245] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1246] In this invention, the server includes a means for acquiring search result and question-and-answer data collected from a user terminal, a means for evaluating the emotional state of the acquired data, and a means for summarizing the transmitted data in the server. This allows for efficient summarization of important information and generation of a weekly report, taking into account the user's emotional state. Furthermore, the weekly report can be customized based on the user's level of interest and notified of the latest information on specific keywords or topics, improving the user experience. Furthermore, past weekly reports can be archived and made searchable, allowing for quick reuse of necessary information.

[1247] A "user terminal" is a device that can connect to the Internet and run applications, and is a device that allows a user to obtain and transmit data.

[1248] A "search result" is a list of information that a user obtains from a source such as the Internet based on a particular query.

[1249] "Question-answer data" refers to text data of answers returned to users' questions by generative AI models, etc.

[1250] A "server" is a computer or computer system on a network that can receive, store, process, and transmit data.

[1251] "Emotional state" refers to the psychological state detected from the user's tone of voice, facial expressions, and text input.

[1252] "Summarizing" is the process of shortening acquired data or information and concisely summarizing only the main points.

[1253] A "weekly report" is a report that summarizes one week's worth of data and categorizes it into specific topics and provides it to the user.

[1254] "Interactive features" are features that allow users to click on information in a report to obtain more information.

[1255] "Customization" means filtering information based on the user's interests or specified topics to provide information that meets specific criteria.

[1256] "Means for notifying users of the latest information" refers to a function that instantly notifies users when new information related to a specific keyword or topic is generated.

[1257] "Archive" is a data storage function that saves the generated weekly reports so that they can be searched and referenced later.

[1258] "Searchable" refers to the ability to efficiently find and display specific information from stored data.

[1259] The present invention provides a system for improving the efficiency of information acquisition for users and providing information preferentially according to their emotional state. This system is generally composed of a user terminal, a server, and various programs that link these together.

[1260] First, a user device is a device that can connect to the Internet and run applications, such as a smartphone or PC. When a user enters a search query, the user device collects information using the Internet. When the user asks a question to the generative AI model, the interaction data is also collected. The collected data is sent to an emotion engine on the user device to evaluate the emotional state.

[1261] The emotion engine detects emotions from the user's tone of voice, facial expressions, and text input, and evaluates how the user feels about the content. This emotional state evaluation data is sent to the server along with search results and question-and-answer data.

[1262] The server receives data sent from the user's device and stores it in a database. It then summarizes the data using text analysis algorithms. The software used here includes natural language processing libraries (e.g., TextBlob). The summarized data is prioritized based on the emotional state assessment results and compiled into a weekly report.

[1263] The weekly reports are customized based on the user's interests, categorizing information according to specific topics or keywords. Additionally, when important new information occurs, the server notifies the user of the latest information related to the specific keyword or topic, allowing the user to get the latest information immediately.

[1264] In addition, all weekly reports are archived on the server, allowing users to search and reuse past reports. For example, if a user wants to review information about "Python programming project construction" again, they can easily search for past reports using the archive function.

[1265] Here are some examples of specific prompts:

[1266] "Python programming project construction"

[1267] When a user enters this query, the user's device collects related news articles and question-and-answer data, evaluates the user's emotional state through an emotion engine, and sends the data to the server, which then generates summary data and provides it to the user as a weekly report.

[1268] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1269] Step 1:

[1270] A user enters a search query.

[1271] Input: User's search query (e.g. "Python programming project build")

[1272] Specific operation: The user inputs a search query through an input device on the user terminal.

[1273] Step 2:

[1274] The user terminal collects relevant information from the Internet.

[1275] Input: search query

[1276] Output: A list of search results (news articles, web page links, etc.)

[1277] Specific operation: The user device connects to the Internet and uses a search engine API to collect search results based on the query.

[1278] Step 3:

[1279] The user inputs dialogue data into the generative AI model.

[1280] Input: User question (e.g., "How do I get started on a project in Python programming?")

[1281] Output: Question and answer data (answers from the generative AI model)

[1282] Specific operation: The generative AI model installed on the user's device answers the user's questions and saves the data.

[1283] Step 4:

[1284] The user terminal uses an emotion engine to assess the emotional state.

[1285] Input: Search results, question and answer data

[1286] Output: Emotional state assessment (e.g., confusion, frustration, joy)

[1287] Specific operation: The camera and microphone on the user's device are used to analyze facial expressions and voice tone, and the emotion engine analyzes the text data to evaluate emotions.

[1288] Step 5:

[1289] The user terminal transmits the collected data and the evaluation results of the emotional state to the server.

[1290] Input: Search results, Q&A data, and emotional state assessment results

[1291] Output: Data transfer to the server

[1292] Specific operation: Using the communication module of the user terminal, the collected data and the evaluation results of the emotional state are sent to the server.

[1293] Step 6:

[1294] The server stores the received data in a database.

[1295] Input: Collected data, emotional state assessment results

[1296] Output: Data stored in the database

[1297] Specific operation: The server analyzes the received data and stores it in the database in the appropriate format.

[1298] Step 7:

[1299] The server uses text analysis algorithms to summarize the data.

[1300] Input: Data stored in the database

[1301] Output: Summary data

[1302] Specific behavior: The server summarizes the text data using a natural language processing library (e.g., TextBlob).

[1303] Step 8:

[1304] The server generates a weekly report based on the emotional state assessment.

[1305] Input: Summary data, emotional state assessment results

[1306] Output: Weekly report

[1307] What it does: The server combines the summary data with the emotional state to generate a customized weekly report based on the user's interests.

[1308] Step 9:

[1309] The server delivers weekly reports to user terminals and provides interactive functions.

[1310] Input: Weekly Report

[1311] Output: A report in a format that can be viewed on the user's terminal

[1312] Specific operation: The server delivers the report to the user's device and allows the user to click on information in the report to obtain more information.

[1313] Step 10:

[1314] The server archives past weekly reports and makes them searchable by users.

[1315] Input: Generated weekly report

[1316] Output: Archived report data

[1317] What it does: The server archives past weekly reports, allowing users to quickly search for the information they need.

[1318] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1319] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1320] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1321] [Fourth embodiment]

[1322] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1323] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1324] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1325] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1326] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1327] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1328] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1329] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1330] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1331] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1332] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1333] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1334] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1335] overview

[1336] The present invention is a system for efficiently acquiring, summarizing, storing, and sharing search results and question-and-answer data that users routinely obtain. The system includes a user terminal, a server, and various programs that link these.

[1337] User terminal operation

[1338] It collects search results from users' normal internet use and dialogue data from generative AI (e.g., question-answering systems). The user's device obtains this data through the browser history and an interface to the generative AI. The collected data is then sent to a server.

[1339] Data processing on the server

[1340] The server receives data sent from user devices and stores it in a database. The received data is summarized using a text analysis algorithm. A weekly report is automatically generated based on this summary. The weekly report is generated in HTML format or other formats to make it interactive, allowing users to click to view more detailed information.

[1341] Generate and view weekly reports

[1342] The weekly report contains a week's worth of summary data and is divided into specific topics, such as information on "Python programming" or "artificial intelligence," depending on the user's interests. The generated weekly report is either delivered to the user's device or saved in a format that can be viewed in a web browser.

[1343] Customization features

[1344] Users can customize the content of their weekly reports based on their interests. Users can specify topics and keywords of interest, and the server will collect data and generate summaries based on that information. The system also offers a smart alert function that notifies users when new information becomes available, allowing users to stay up to date with the latest information.

[1345] Archive function

[1346] The server archives all generated weekly reports and provides search functionality where necessary, allowing users to search past reports based on specific topics, keywords, or dates, and reuse information.

[1347] Specific examples

[1348] For example, if a user searches for "Python programming" and "artificial intelligence," the user's device collects the search results and sends them to the server. The server summarizes this data and generates a weekly report containing a week's worth of summaries. The user can view the report, check the summaries for each topic, and click on the summary item of interest to access more information. Additionally, the smart alert function allows users to receive instant notifications when new information related to "Python programming" or "artificial intelligence" appears.

[1349] This allows users to efficiently gather information and solidify their knowledge. This system will be a powerful tool to support intellectual activities in today's information overload environment.

[1350] The processing flow will be explained below.

[1351] Step 1:

[1352] A user searches the Internet. The user's device retrieves search results from the browser history. For example, search results related to "Python programming" and "artificial intelligence" are collected from the browser history.

[1353] Step 2:

[1354] The user terminal formats the collected search results and data from the question-answering system and sends them to the server using an HTTP POST request.

[1355] Step 3:

[1356] The server receives the data sent from the user terminal and stores it in a database. The server stores the captured data in text format for later summarization.

[1357] Step 4:

[1358] The server processes the received data and summarizes its contents. It uses text analysis algorithms (e.g., natural language processing techniques) to generate a summary of the data.

[1359] Step 5:

[1360] The server generates weekly reports based on the summarized data, organized chronologically and categorized by major topics, and structured in formats such as HTML and PDF.

[1361] Step 6:

[1362] The server delivers the generated weekly report to the user terminal or stores it in a web interface, and the user terminal displays the received or retrieved weekly report to the user.

[1363] Step 7:

[1364] Users can view weekly reports and click on each summary to view more detailed information. Interactive features allow users to learn more about specific topics.

[1365] Step 8:

[1366] Users can customize the content of reports based on their interests, and the server generates customized reports by filtering the collected data based on user-specified topics and keywords.

[1367] Step 9:

[1368] The server notifies users when there is new information about specific keywords or topics, using the Smart Alert function to instantly deliver the latest information to users' devices.

[1369] Step 10:

[1370] The server archives all generated weekly reports and makes them searchable as needed, allowing users to search past reports based on specific topics, keywords, or dates, and reuse information.

[1371] Example 1

[1372] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1373] In today's world, the amount of information available via the Internet is enormous, making it difficult for users to efficiently obtain, organize, and manage the accurate and useful information they desire. In particular, the amount of data obtained through daily Internet searches and interactions with generative AI is constantly increasing. To effectively utilize this data, appropriate data collection, summarization, storage, and organization are required. Furthermore, there is a demand for customization functions based on user interests and immediate notifications of the latest information.

[1374] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1375] In this invention, the server includes means for acquiring search results and interaction data collected from the user terminal, means for transmitting the acquired data to an information processing device, means for summarizing the transmitted data in the information processing device, means for generating periodic reports based on the summarized data, means for providing an interactive function for viewing the periodic reports, means for customizing the content of the periodic reports based on the user's interests, means for notifying the user of the latest information related to specific topics of interest, and means for saving past periodic reports and making the saved reports searchable. This allows users to efficiently obtain the information they need from a vast amount of information and review it in summarized form. Furthermore, users can instantly obtain the latest content of information of interest and easily access and reuse past information.

[1376] A "user terminal" is an information processing device used by a user, and refers to devices such as a personal computer, smartphone, or tablet.

[1377] "Search Results" refers to a list of information displayed in response to a query entered by a user using an Internet search engine.

[1378] "Dialogue data" refers to data that includes the content of questions and responses exchanged between a user and a generative AI model.

[1379] The term "information processing device" refers to a device such as a server that receives, stores, analyzes, and summarizes data sent from a user terminal.

[1380] "Summarizing" refers to the act of analyzing acquired data, extracting important information, and summarizing it concisely.

[1381] "Periodic Report" refers to a report generated based on a summary of data over a given period of time (e.g., one week).

[1382] "Interactive function" refers to a function that allows users to operate the device interactively and access detailed information by clicking or tapping.

[1383] "Customizing" refers to the act of changing content or functionality based on a user's interests and needs.

[1384] "Notifying the latest information" refers to the act of notifying the user of newly acquired related information in real time.

[1385] "Storing" refers to the act of recording and retaining information in a database or storage device.

[1386] "Searchable" refers to the ability to find stored information based on specific criteria (such as keywords or dates).

[1387] This invention is a system for efficiently acquiring, summarizing, saving, and sharing search results and dialogue data with generative AI models that users regularly obtain. This system mainly includes a user terminal, a server, an information processing device, a browser, and programs for the generative AI model.

[1388] System configuration and operation

[1389] 1. User Device

[1390] User terminals include devices such as personal computers, smartphones, and tablets. Search results and interaction data with the generative AI model that users use during normal Internet usage are collected. The user terminal acquires this data through the browser history and the interface to the generative AI model, and temporarily stores the data in local storage. The acquired data is then sent to an information processing device. Data is sent using HTTP POST requests, etc.

[1391] 2. Servers and Information Processing Devices

[1392] The server and information processing device are responsible for receiving and storing data sent from user terminals. The information processing device manages data using database software such as MySQL or MongoDB. The sent data is analyzed and summarized using a text analysis algorithm (such as NLTK or spaCy). Furthermore, periodic reports containing one week's worth of data are generated based on the summary results. The generated reports are formatted in HTML and can be used interactively by users.

[1393] 3. Report Delivery and Display

[1394] The server periodically delivers the generated reports to the user's device. Delivery methods include email and notification API. Users can check the delivered reports on their device and access detailed information by clicking on each item. For example, if a user clicks on information about "Python programming" or "artificial intelligence," they can view a summary and detailed information about that item.

[1395] 4. Customization features

[1396] Users can specify topics and keywords of interest through a web interface. The server uses the information to customize data collection and summarization, generating reports tailored to the user's needs. The server also notifies users in real time when new information related to a topic of interest emerges, allowing users to stay up to date with the latest information immediately.

[1397] 5. Archive function

[1398] The server archives all generated periodic reports in a database, allowing users to search for and reuse information from past reports based on specific topics, keywords, or dates, allowing them to efficiently find and review the information they need.

[1399] Specific examples

[1400] For example, if a user searches for "Python programming" and "artificial intelligence," the user's device collects the search results and sends them to the server. The server then summarizes this data using a text analysis algorithm and generates periodic reports containing a week's worth of summaries. Users can view the reports, check the summaries for each topic, and click on the summary items they are interested in to access more information. Additionally, when new information related to "Python programming" or "artificial intelligence" appears, they can receive instant notifications using the smart alert function.

[1401] Prompt Sentence Examples

[1402] 1. "What's the latest news about Python programming?"

[1403] 2. "Please summarize recent developments in the field of artificial intelligence."

[1404] This system allows users to efficiently retrieve the information they need from a vast amount of information and check it in a summarized form. It also allows users to instantly obtain the latest information about information they are interested in, and easily access and reuse past information.

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

[1406] Step 1:

[1407] The user device monitors the browser history and interaction data with the generated AI model.

[1408] When a user performs a search, the query and results are saved in the browser history, along with any interactions with the generative AI model.

[1409] Input: User search queries and interaction data with the generative AI model.

[1410] Output: Search history and interaction data saved in local storage.

[1411] What it does: When a user searches for "Python programming," the system retrieves the search query and results. It also collects conversational data when the AI ​​model is asked questions about recent advances in artificial intelligence.

[1412] Step 2:

[1413] The user terminal periodically transmits data stored in the local storage to the information processing device.

[1414] Submissions are made using HTTP POST requests, sending data to a specific endpoint.

[1415] Input: Search history and interaction data stored in local storage.

[1416] Output: Data sent over a broadband network to a server.

[1417] Specific behavior: Search history and interaction data are periodically sent in batches to the http: / / example.com / upload endpoint.

[1418] Step 3:

[1419] The server analyzes the received data and stores it in a database.

[1420] The server receives the HTTP request, parses it, and stores it in a database.

[1421] Input: Browser history and interaction data sent to the server.

[1422] Output: Search history and interaction data stored in a database.

[1423] Specific operation: Executes an INSERT statement to the database and saves the submitted search query and conversation content.

[1424] Step 4:

[1425] The server summarizes the data stored in the database.

[1426] Use text analysis algorithms (e.g., NLTK or spaCy) to extract and summarize important information.

[1427] Input: Search history and interaction data stored in a database.

[1428] Output: Summarized text data.

[1429] What it does: It extracts search history from the database and extracts key points about "Python programming." It also generates an overview of "Recent Advances in Artificial Intelligence" from AI interaction data.

[1430] Step 5:

[1431] The server generates periodic reports based on the summarized data.

[1432] The generated summary data is embedded in an HTML template and formatted as a report.

[1433] Input: Abstracted text data.

[1434] Output: Scheduled reports in HTML format.

[1435] Specific operation: Using Python scripts etc., generate a weekly report by properly arranging the summary data in HTML format.

[1436] Step 6:

[1437] The server generates periodic reports and delivers them to the user terminal.

[1438] Delivery is done via email or notification API.

[1439] Input: Scheduled report in HTML format.

[1440] Output: Report delivered to user terminal.

[1441] Specific operation: Uses the email sending API to send a report to the specified email address, and also uses the notification API to send a notification to the user's device.

[1442] Step 7:

[1443] Users review periodic reports and click on items of interest to access more information.

[1444] Users open the delivered report in a browser or email client.

[1445] Input: HTML format report delivered to user terminal.

[1446] Output: Detailed information displayed in a browser.

[1447] What it does: When a user clicks on a summary item about "Python programming," more information about it opens in a new tab or window.

[1448] Step 8:

[1449] Users specify topics or keywords that interest them.

[1450] Users register topics of interest through a web interface.

[1451] Input: User-specified topics or keywords.

[1452] Output: User interest data stored on the server.

[1453] Specific operation: The user specifies a topic such as "machine learning" or "data science," and it is saved on the server.

[1454] Step 9:

[1455] The server customizes the data collection and summarization process based on the user's interests.

[1456] The server adjusts the collection and summarization algorithms based on the specified data of interest.

[1457] Input: User interest data.

[1458] Output: Customized summary data and reports.

[1459] What it does: Adjust database queries to prioritize search results and interaction data related to "machine learning" and "data science."

[1460] Step 10:

[1461] If the server detects new relevant information, it notifies the user in real time.

[1462] A notification system will be in place when new information is added to the database.

[1463] Input: New information added to the database.

[1464] Output: Notification sent to the user's device.

[1465] What it does: When new information about "Python programming" or "artificial intelligence" is added, users will receive an email or app notification instantly.

[1466] Step 11:

[1467] The server stores past periodic reports and makes them available for user retrieval.

[1468] Saved reports are added to the search index.

[1469] Enter: Periodic Reports.

[1470] Output: Searchable archive data.

[1471] What it does: Periodically generated reports are archived in a database, and when a user searches for "Python programming" or a specific date, relevant past reports are displayed.

[1472] (Application example 1)

[1473] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1474] Conventional factory robot management and maintenance systems lack a means to efficiently collect and organize information about errors and troubleshooting. This prevents factory managers and maintenance teams from quickly grasping the necessary information, resulting in delayed responses and reduced efficiency. There are also insufficient methods for searching and utilizing past error and repair history.

[1475] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1476] In this invention, the server includes means for acquiring search result and question and answer data collected from user terminals, means for transmitting the acquired data to the server, means for summarizing the transmitted data in the server, means for generating a weekly report based on the summarized data, means for providing an interactive function for viewing the weekly report, means for collecting error and log data generated by the robot during operation, means for transmitting the collected error and log data to the server, means for summarizing the received error and log data in the server to generate a weekly report, and means for providing the generated weekly report in a format viewable by factory managers and maintenance teams. This allows for efficient collection and organization of information on errors and troubleshooting that occur in the factory, enabling factory managers and maintenance teams to quickly grasp the necessary information, thereby enabling faster responses and improved work efficiency.

[1477] "User terminal" refers to an electronic device that a user operates to search for information or answer questions.

[1478] "Server" refers to a central processing system that receives, stores, and processes data sent from user terminals.

[1479] "Search results" refers to a collection of information obtained as a result of a search performed by a user to find specific information on the Internet.

[1480] "Question-answering data" refers to information on questions and answers posed through dialogue systems such as generative AI.

[1481] "Summarization" refers to the process of simplifying a large amount of collected data and extracting only the important information.

[1482] A "Weekly Report" is a report that summarizes data collected over the past week and organizes it by specific topic.

[1483] "Interactive function" refers to a function that allows the user to display detailed information or perform specific operations through user operations.

[1484] An "error" refers to an abnormal condition recorded when the robot does not operate normally during a task.

[1485] "Log data" refers to data that records detailed operational history, such as the robot's work content and error occurrence status.

[1486] "Factory manager" refers to an official in charge of the operation and management of a factory.

[1487] "Maintenance team" refers to the group of workers responsible for maintaining and repairing equipment and systems within a factory.

[1488] Overall system overview

[1489] This invention is a system that includes a user terminal, a server, and various programs that link them together to efficiently support the management and maintenance of robots in factories. In particular, it aims to efficiently collect, summarize, store, and share information on errors and troubleshooting that occur during robot operation.

[1490] User device and robot behavior

[1491] The user's device and the robot receive real-time information about errors and troubleshooting that occur during work. This information is sent to a server via the Internet. The robot must have an Internet connection and transmits data using HTTP requests.

[1492] Data processing on the server

[1493] The server receives, stores, and processes a wide variety of data. It houses a database, where the received error and log data is stored. The stored data is summarized using text analysis algorithms. Based on the results of these summaries, regular weekly reports are automatically generated.

[1494] Generate and view weekly reports

[1495] The generated weekly report summarizes the data collected over the past week and is organized by specific topics. The report is generated in HTML format and is presented to the user in an interactive format, allowing the user to click for more information, such as error codes and how to troubleshoot them.

[1496] Customization features

[1497] Users can customize the content of their weekly reports based on their interests, and a smart alert feature is also provided, allowing users to receive instant notifications when new information emerges on specific keywords or topics, helping them stay up to date with the latest information.

[1498] Archive function

[1499] The server archives all generated weekly reports and provides search functionality for them as needed. Users can search past reports based on specific topics, keywords, or dates, and reuse information. This functionality allows for quick access to past error and troubleshooting information.

[1500] Specific examples

[1501] For example, if Robot A in a factory encounters a connection failure with error code 404 while in operation, the robot sends the error information to the server. The server summarizes the received error information and generates a weekly report stating, "An error has occurred regarding a connection failure for Part A. Please refer to the link below for detailed troubleshooting." Factory managers and maintenance teams can view summaries of each topic in this report and click on the summary items of interest to access detailed information. Additionally, when new information occurs, they can receive instant notifications via the smart alert function.

[1502] Prompt Sentence Examples

[1503] "Can you tell me more about the robot error code 404? What troubleshooting steps would work?"

[1504] This system will be a powerful tool for efficient robot management and maintenance within factories, allowing factory managers and maintenance teams to respond quickly when errors occur and significantly improving overall work efficiency.

[1505] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1506] Step 1:

[1507] Data collection

[1508] Subject: Device

[1509] Input: Errors and log data that occur while the robot is working

[1510] Processing: The terminal receives real-time error and log data from the robot, including error codes and troubleshooting information.

[1511] Output: Captured error and log data

[1512] Step 2:

[1513] Data transmission

[1514] Subject: Device

[1515] Input: Captured error and log data

[1516] Processing: The device sends the collected error and log data to the server using an HTTP request.

[1517] Output: Error and log data sent to the server

[1518] Step 3:

[1519] Data reception

[1520] Subject: Server

[1521] Input: Error and log data sent from the device

[1522] Processing: The server receives HTTP requests and stores error and log data in a database, using either an SQL or NoSQL database.

[1523] Output: Error and log data stored in the database

[1524] Step 4:

[1525] Data Summary

[1526] Subject: Server

[1527] Input: Stored error and log data

[1528] Processing: The server summarizes the stored error and log data using text analysis algorithms (e.g., natural language processing libraries).

[1529] Output: Summarized error and log data

[1530] Step 5:

[1531] Weekly Report Generation

[1532] Subject: Server

[1533] Input: Digest error and log data

[1534] Processing: The server generates weekly reports in HTML format based on the summary results, organized by specific topics.

[1535] Output: Generated weekly report

[1536] Step 6:

[1537] Report provision

[1538] Subject: Server

[1539] Input: Generated weekly report

[1540] Processing: The server provides weekly reports to the plant manager or maintenance team either in a format viewable in a web browser or via email.

[1541] Output: Weekly reports provided to plant managers and maintenance teams

[1542] Step 7:

[1543] Customization and Notifications

[1544] Subject: Server

[1545] Input: User interest data, keywords and topic specifications

[1546] Processing: The server customizes weekly reports based on user interests and notifies users when updates occur on specific keywords or topics using the Smart Alerts feature.

[1547] Output: User-customized weekly reports and update notifications

[1548] Step 8:

[1549] Archive and Search

[1550] Subject: Server

[1551] Input: Generated weekly report

[1552] Processing: The server archives all generated weekly reports and allows users to search historical data. For this purpose, it uses a text search engine (e.g. Elasticsearch).

[1553] Output: Searchable archive data

[1554] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1555] overview

[1556] This invention is a system for efficiently acquiring, summarizing, saving, and sharing search results and question-and-answer data that users obtain on a daily basis, and also combines an emotion engine that recognizes the user's emotions. This system includes a user terminal, a server, and various programs that link these.

[1557] User terminal operation

[1558] It collects search results and dialogue data from generative AI (e.g., question-answering systems) during a user's normal internet use. The user's device acquires this data through the browser history and an interface to the generative AI. The acquired data is sent to an emotion engine, which evaluates the user's emotional state. The user's device then sends the collected data and the evaluation of the emotional state to the server.

[1559] Data processing on the server

[1560] The server receives data sent from the user's device and stores it in a database. The received data is summarized using a text analysis algorithm and prioritized for processing based on the evaluation results of the emotion engine. A weekly report is automatically generated based on the summary results. The weekly report also includes an analysis of the user's emotional state.

[1561] Generate and view weekly reports

[1562] The weekly report contains a week's worth of summary data and is divided into specific topics, such as information on "Python programming" or "artificial intelligence," depending on the user's interests. The generated weekly report is delivered to the user's device or saved in a format that can be viewed in a web browser. The report is interactive, allowing users to click to view more detailed information.

[1563] Use of emotion engine

[1564] The user device is equipped with an emotion engine that recognizes emotions from the user's tone of voice, facial expressions, and text input. For example, if a user asks a question about "Python programming" and expresses dissatisfaction or confusion, this information is transmitted to the server, and the relevant content is highlighted in the weekly report.

[1565] Customization features

[1566] Users can customize the content of their reports based on their interests. The server filters the collected data based on user-specified topics and keywords to generate customized reports. The server also provides a smart alert feature that notifies users when new information becomes available.

[1567] Archive function

[1568] The server archives all generated weekly reports and provides search functionality for them as needed, allowing users to search past reports based on specific topics, keywords, or dates and reuse information.

[1569] Specific examples

[1570] For example, if a user searches for "Python programming" and the emotion engine detects feelings of dissatisfaction or confusion, the user's device will send this information to the server. The server will prioritize and summarize this data and include it as a special section in the weekly report. After a week, the user will receive a report with a detailed explanation of the topic that confused them. The user can then browse the report and use the interactive features to find out more details. Smart alerts will also instantly notify users when new useful information about "Python programming" becomes available.

[1571] This allows users to efficiently gather information and solidify their knowledge. In today's information overload environment, this system will be a powerful tool for supporting intellectual activities while taking into account the user's subjective feelings.

[1572] The processing flow will be explained below.

[1573] Step 1:

[1574] A user searches the Internet. The user's device retrieves search results from the browser history. For example, search results related to "Python programming" and "artificial intelligence" are collected from the browser history.

[1575] Step 2:

[1576] The user device uses an emotion engine to analyze the user's emotional state from the search results and question-and-answer data it obtains, for example, by analyzing the content of text input, voice tone, and facial expressions to evaluate the user's emotional state.

[1577] Step 3:

[1578] The user device formats the collected search results and emotional state ratings and sends them to the server. This data is sent to the server using an HTTP POST request.

[1579] Step 4:

[1580] The server receives data sent from the user terminal and stores it in a database, including search results, question-and-answer data, and the user's emotional state.

[1581] Step 5:

[1582] The server then summarises the stored data using a text analysis algorithm, taking into account the user's emotional state and prioritising particularly important information or content closely related to the user's emotions.

[1583] Step 6:

[1584] The server then uses the summarized data to generate a weekly report customized for each user, with analysis and relevant information based on their emotional state, including detailed explanations and additional information on topics that confuse the user.

[1585] Step 7:

[1586] The generated weekly report is delivered to the user terminal or stored in a web interface, and the user terminal displays the received or retrieved weekly report to the user.

[1587] Step 8:

[1588] Users can view weekly reports and click on each summary to view more detailed information. Interactive features allow users to learn more about specific topics.

[1589] Step 9:

[1590] Users can customize the content of reports based on their interests, and the server generates customized reports by filtering the collected data based on user-specified topics and keywords.

[1591] Step 10:

[1592] The server notifies users when there is new information about specific keywords or topics, using the Smart Alert function to instantly deliver the latest information to users' devices.

[1593] Step 11:

[1594] The server archives all generated weekly reports and makes them searchable as needed, allowing users to search past reports based on specific topics, keywords, or dates, and reuse information.

[1595] Example 2

[1596] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1597] In today's information-overloaded world, it is important to efficiently retrieve, consolidate, and effectively utilize user search results and question-answer data. However, existing systems lack the functionality to prioritize information based on the user's emotional state and customize reports accordingly. Furthermore, archiving and searching for old information is also inadequate, making it difficult for users to reuse past information.

[1598] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1599] In this invention, the server includes means for acquiring search result and question-and-answer data collected from the user terminal, means for transmitting the acquired data to an emotion engine to evaluate the user's emotional state, means for transmitting data including the evaluation results to the server, means for summarizing the transmitted data in the server, means for generating a weekly report based on the summarized data and the emotion evaluation results, and means for providing an interactive function for viewing the weekly report. This enables prioritization of information and generation of customized reports taking into account the user's emotional state, and also enables effective retrieval and reuse of past information.

[1600] A "user terminal" is a device that a user uses to access the Internet, and includes a PC, smartphone, tablet, etc.

[1601] "Search results" refers to the list of information that a user can obtain by using an Internet search engine.

[1602] "Question-answer data" refers to data that includes questions posed to generative AI by users and the AI's answers to those questions.

[1603] An "emotion engine" refers to a system that recognizes and evaluates emotions from a user's tone of voice, facial expressions, text input, etc.

[1604] "Server" refers to a computer that operates on a network and processes and stores data sent from user devices.

[1605] A "summarization method" is a method that uses a text analysis algorithm to extract the main parts of the transmitted data and present them in a concise form.

[1606] A "Weekly Report" is a report that compiles a week's worth of summary data and categorizes it by specific topic.

[1607] "Interactive function" refers to a function that allows a user to view detailed information by clicking on a link in a report.

[1608] "Customization methods" are ways to tailor and change the content of a report based on user interests.

[1609] "Means for notifying" refers to a method for notifying the user when new information is generated.

[1610] "Archiving means" refers to a method for storing generated weekly reports as historical records and making them available for later retrieval.

[1611] MODE FOR CARRYING OUT THE INVENTION

[1612] overview

[1613] This invention is a system for efficiently acquiring, summarizing, saving, and sharing search results and question-and-answer data that users obtain on a daily basis, and also combines an emotion engine that recognizes the user's emotions. This system includes a user terminal, a server, and various programs that link these.

[1614] User terminal operation

[1615] The user device collects the search results the user has obtained online and data on their interactions with the generative AI. Specifically, it obtains search keywords, questions, and conversation history through browser history data and the generative AI's API. It also uses an emotion engine to evaluate the user's emotional state based on their tone of voice, facial expression, and text input. The results of this emotional state evaluation are sent to the server along with the collected data.

[1616] Data processing on the server

[1617] The server receives data sent from the user's device and stores it in a relational database (e.g., MySQL, PostgreSQL). The received data is summarized using a text analysis algorithm (e.g., NLTK, SpaCy). Based on this summary data and the evaluation results of the emotion engine, the server automatically generates a weekly report. A template engine for report generation (e.g., Jinja2) is used to create a report that highlights the most important content based on the user's emotion evaluation results.

[1618] Generate and view weekly reports

[1619] The generated weekly reports are generated in a visually friendly format using HTML and CSS. The reports are delivered to the user's device or saved in a format that can be viewed in a web browser. The reports include interactive features, allowing users to click to view more detailed information.

[1620] Customize reports

[1621] Users can customize the content of their reports based on their interests. The server generates a customized report by filtering relevant information in the database based on topics and keywords specified by the user. For example, a report can be tailored to include only information about "AI." The server also notifies users when new information becomes available.

[1622] Archive function

[1623] The server stores an archive of all generated weekly reports and provides users with the ability to search past reports. Users can search past reports based on specific topics, keywords, or dates, allowing them to reuse information. This search functionality can utilize search engine technology (e.g., Elasticsearch).

[1624] Specific examples

[1625] For example, if a user searches for "Python programming" and the emotion engine detects feelings of dissatisfaction or confusion, the user's device will send this information to the server. The server will prioritize and summarize this data and include it as a special section in the weekly report. After a week, the user will receive a report with a detailed explanation of the topic that confused them. The user can then view the report and use the interactive features to find out more details. Additionally, the smart alert feature will instantly notify the user when new useful information about "Python programming" becomes available.

[1626] Example prompt sentence:

[1627] "Explain how you can measure how users learn about Python programming, summarize that data, and generate a report based on the sentiment engine's assessment."

[1628] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1629] Step 1:

[1630] Data collection from user devices

[1631] When a user searches or interacts with generative AI in a web browser, their history data and interaction data are collected. Specifically, data such as search keywords and conversation content is collected using the browser's history API and the generative AI's API.

[1632] Input: User search results, question and answer data

[1633] Output: Acquired historical data, interaction data

[1634] Step 2:

[1635] Emotional state assessment by emotion engine

[1636] The acquired data is sent to the emotion engine, which recognizes emotions from the user's tone of voice, facial expressions, and text input, and generates an evaluation result. Specifically, it uses voice analysis, image analysis, and natural language processing technology to perform emotion evaluation.

[1637] Input: historical data, dialogue data, user voice, facial expressions, text input

[1638] Output: Emotion evaluation result (e.g., confusion, dissatisfaction)

[1639] Step 3:

[1640] Send data and evaluation results to the server

[1641] The user device sends the collected history data, dialogue data, and emotion evaluation results to the server. Specifically, this data is compiled in JSON format and uploaded to the server using the HTTPS protocol.

[1642] Input: History data, dialogue data, emotion evaluation results

[1643] Output: Data sent to the server

[1644] Step 4:

[1645] Data storage on the server

[1646] The server stores the received data in a database. Specifically, it uses a relational database (e.g., MySQL or PostgreSQL) to classify and store the data by user ID.

[1647] Input: Data sent to the server

[1648] Output: Data stored in the database

[1649] Step 5:

[1650] Text Analysis and Data Summarization

[1651] The server summarizes the stored data using a text analysis algorithm, specifically using a natural language processing library (e.g., NLTK or SpaCy) to extract important keywords and sentences and generate a summary.

[1652] Input: Data stored in a database

[1653] Output: Summarized text data

[1654] Step 6:

[1655] Generate weekly reports

[1656] The server automatically generates weekly reports based on the summarized data. Specifically, it uses a template engine (e.g., Jinja2) for report generation to create reports based on user interest topics and sentiment assessment results.

[1657] Input: Summarized text data, emotion evaluation results

[1658] Output: Generated weekly report

[1659] Step 7:

[1660] Report distribution and viewing

[1661] The server then delivers the generated weekly report to the user's device or saves it in a format that can be viewed in a web browser. Specifically, it creates a visually appealing report using HTML and CSS and delivers it to the user via email or in-app notification.

[1662] Input: Generated weekly report

[1663] Output: Reports delivered to user devices, reports viewable in web browsers

[1664] Step 8:

[1665] Customize reports

[1666] Users can customize the content of the report based on their interests, and the server generates a customized report by filtering relevant information in the database based on user-specified topics and keywords.

[1667] Input: User's interest topics, keywords

[1668] Output: Customized weekly report

[1669] Step 9:

[1670] Using the archive function

[1671] The server stores an archive of all generated weekly reports and allows users to search past reports using search engine technology (e.g., Elasticsearch) based on specified topics, keywords, and dates.

[1672] Input: Past Weekly Report, Search Query

[1673] Output: Search results as a report

[1674] (Application example 2)

[1675] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1676] In today's information overload environment, users face the challenge of efficiently gathering useful information and solidifying knowledge. Furthermore, systems are needed to improve the user experience by providing information tailored to the user's emotional state. However, current systems do not adequately consider the user's emotions when providing information, and are unable to prioritize information appropriately. To solve this problem, technology is needed that can evaluate the user's emotional state and organize, summarize, and recommend information based on that information.

[1677] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1678] In this invention, the server includes a means for acquiring search result and question-and-answer data collected from a user terminal, a means for evaluating the emotional state of the acquired data, and a means for summarizing the transmitted data in the server. This allows for efficient summarization of important information and generation of a weekly report, taking into account the user's emotional state. Furthermore, the weekly report can be customized based on the user's level of interest and notified of the latest information on specific keywords or topics, improving the user experience. Furthermore, past weekly reports can be archived and made searchable, allowing for quick reuse of necessary information.

[1679] A "user terminal" is a device that can connect to the Internet and run applications, and is a device that allows a user to obtain and transmit data.

[1680] A "search result" is a list of information that a user obtains from a source such as the Internet based on a particular query.

[1681] "Question-answer data" refers to text data of answers returned to users' questions by generative AI models, etc.

[1682] A "server" is a computer or computer system on a network that can receive, store, process, and transmit data.

[1683] "Emotional state" refers to the psychological state detected from the user's tone of voice, facial expressions, and text input.

[1684] "Summarizing" is the process of shortening acquired data or information and concisely summarizing only the main points.

[1685] A "weekly report" is a report that summarizes one week's worth of data and categorizes it into specific topics and provides it to the user.

[1686] "Interactive features" are features that allow users to click on information in a report to obtain more information.

[1687] "Customization" means filtering information based on the user's interests or specified topics to provide information that meets specific criteria.

[1688] "Means for notifying users of the latest information" refers to a function that instantly notifies users when new information related to a specific keyword or topic is generated.

[1689] "Archive" is a data storage function that saves the generated weekly reports so that they can be searched and referenced later.

[1690] "Searchable" refers to the ability to efficiently find and display specific information from stored data.

[1691] The present invention provides a system for improving the efficiency of information acquisition for users and providing information preferentially according to their emotional state. This system is generally composed of a user terminal, a server, and various programs that link these together.

[1692] First, a user device is a device that can connect to the Internet and run applications, such as a smartphone or PC. When a user enters a search query, the user device collects information using the Internet. When the user asks a question to the generative AI model, the interaction data is also collected. The collected data is sent to an emotion engine on the user device to evaluate the emotional state.

[1693] The emotion engine detects emotions from the user's tone of voice, facial expressions, and text input, and evaluates how the user feels about the content. This emotional state evaluation data is sent to the server along with search results and question-and-answer data.

[1694] The server receives data sent from the user's device and stores it in a database. It then summarizes the data using text analysis algorithms. The software used here includes natural language processing libraries (e.g., TextBlob). The summarized data is prioritized based on the emotional state assessment results and compiled into a weekly report.

[1695] The weekly reports are customized based on the user's interests, categorizing information according to specific topics or keywords. Additionally, when important new information occurs, the server notifies the user of the latest information related to the specific keyword or topic, allowing the user to get the latest information immediately.

[1696] In addition, all weekly reports are archived on the server, allowing users to search and reuse past reports. For example, if a user wants to review information about "Python programming project construction" again, they can easily search for past reports using the archive function.

[1697] Here are some examples of specific prompts:

[1698] "Python programming project construction"

[1699] When a user enters this query, the user's device collects related news articles and question-and-answer data, evaluates the user's emotional state through an emotion engine, and sends the data to the server, which then generates summary data and provides it to the user as a weekly report.

[1700] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1701] Step 1:

[1702] A user enters a search query.

[1703] Input: User's search query (e.g. "Python programming project build")

[1704] Specific operation: The user inputs a search query through an input device on the user terminal.

[1705] Step 2:

[1706] The user terminal collects relevant information from the Internet.

[1707] Input: search query

[1708] Output: A list of search results (news articles, web page links, etc.)

[1709] Specific operation: The user device connects to the Internet and uses a search engine API to collect search results based on the query.

[1710] Step 3:

[1711] The user inputs dialogue data into the generative AI model.

[1712] Input: User question (e.g., "How do I get started on a project in Python programming?")

[1713] Output: Question and answer data (answers from the generative AI model)

[1714] Specific operation: The generative AI model installed on the user's device answers the user's questions and saves the data.

[1715] Step 4:

[1716] The user terminal uses an emotion engine to assess the emotional state.

[1717] Input: Search results, question and answer data

[1718] Output: Emotional state assessment (e.g., confusion, frustration, joy)

[1719] Specific operation: The camera and microphone on the user's device are used to analyze facial expressions and voice tone, and the emotion engine analyzes the text data to evaluate emotions.

[1720] Step 5:

[1721] The user terminal transmits the collected data and the evaluation results of the emotional state to the server.

[1722] Input: Search results, Q&A data, and emotional state assessment results

[1723] Output: Data transfer to the server

[1724] Specific operation: Using the communication module of the user terminal, the collected data and the evaluation results of the emotional state are sent to the server.

[1725] Step 6:

[1726] The server stores the received data in a database.

[1727] Input: Collected data, emotional state assessment results

[1728] Output: Data stored in the database

[1729] Specific operation: The server analyzes the received data and stores it in the database in the appropriate format.

[1730] Step 7:

[1731] The server uses text analysis algorithms to summarize the data.

[1732] Input: Data stored in the database

[1733] Output: Summary data

[1734] Specific behavior: The server summarizes the text data using a natural language processing library (e.g., TextBlob).

[1735] Step 8:

[1736] The server generates a weekly report based on the emotional state assessment.

[1737] Input: Summary data, emotional state assessment results

[1738] Output: Weekly report

[1739] What it does: The server combines the summary data with the emotional state to generate a customized weekly report based on the user's interests.

[1740] Step 9:

[1741] The server delivers weekly reports to user terminals and provides interactive functions.

[1742] Input: Weekly Report

[1743] Output: A report in a format that can be viewed on the user's terminal

[1744] Specific operation: The server delivers the report to the user's device and allows the user to click on information in the report to obtain more information.

[1745] Step 10:

[1746] The server archives past weekly reports and makes them searchable by users.

[1747] Input: Generated weekly report

[1748] Output: Archived report data

[1749] What it does: The server archives past weekly reports, allowing users to quickly search for the information they need.

[1750] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1751] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1752] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1753] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1754] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1755] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1756] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1757] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1758] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1759] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1760] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1761] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1762] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1763] 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.

[1764] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1765] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1766] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1767] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1768] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1769] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1770] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1771] The following is further disclosed regarding the above embodiment.

[1772] (Claim 1)

[1773] A means for acquiring search result and question response data collected from a user terminal;

[1774] means for transmitting the acquired data to a server;

[1775] means at said server for summarizing said transmitted data;

[1776] a means for generating weekly reports based on the summarized data;

[1777] means for providing interactive functionality for viewing said weekly report;

[1778] A system including:

[1779] (Claim 2)

[1780] a means for customizing the content of the weekly report based on the user's interests;

[1781] A way to notify you of the latest information on specific keywords or topics,

[1782] The system of claim 1 further comprising:

[1783] (Claim 3)

[1784] 10. The system of claim 1, further comprising means for archiving past weekly reports and making the archived reports searchable.

[1785] "Example 1"

[1786] (Claim 1)

[1787] A means for acquiring search results and dialogue data collected from a user terminal;

[1788] means for transmitting the acquired data to an information processing device;

[1789] means for summarizing the transmitted data in the information processing device;

[1790] a means for generating periodic reports based on the summarized data;

[1791] means for providing interactive functionality for viewing said periodic reports;

[1792] A system including:

[1793] (Claim 2)

[1794] a means for customizing the content of periodic reports based on user interests;

[1795] a means of notifying you of updates on matters of particular interest;

[1796] The system of claim 1 further comprising:

[1797] (Claim 3)

[1798] 10. The system of claim 1, further comprising means for storing past periodic reports and making the stored reports searchable.

[1799] "Application Example 1"

[1800] (Claim 1)

[1801] A means for acquiring search result and question response data collected from a user terminal;

[1802] means for transmitting the acquired data to a server;

[1803] means at said server for summarizing said transmitted data;

[1804] a means for generating weekly reports based on the summarized data;

[1805] means for providing interactive functionality for viewing said weekly report;

[1806] A means for collecting error and log data that occurs while the robot is working;

[1807] A means for transmitting collected error and log data to a server;

[1808] means for generating weekly reports summarizing error and log data received by the server;

[1809] A means to provide the generated weekly reports in a format that can be viewed by plant management and maintenance teams;

[1810] A system including:

[1811] (Claim 2)

[1812] a means for customizing the content of the weekly report based on the user's interests;

[1813] A way to notify you of the latest information on specific keywords or topics,

[1814] The system of claim 1 further comprising:

[1815] (Claim 3)

[1816] 10. The system of claim 1, further comprising means for archiving past weekly reports and making the archived reports searchable.

[1817] "Example 2: Combining Emotion Engines"

[1818] (Claim 1)

[1819] A means for acquiring search result and question response data collected from a user terminal;

[1820] means for transmitting the acquired data to an emotion engine to evaluate the user's emotional state;

[1821] means for transmitting data including the evaluation result to a server;

[1822] means at said server for summarizing said transmitted data;

[1823] a means for generating a weekly report based on the summarized data and emotion assessment results;

[1824] means for providing interactive functionality for viewing said weekly report;

[1825] A system including:

[1826] (Claim 2)

[1827] a means for customizing the content of the weekly report based on the user's interests;

[1828] A way to notify you of the latest information on specific keywords or topics,

[1829] a means for setting priorities based on the evaluation results;

[1830] The system of claim 1 further comprising:

[1831] (Claim 3)

[1832] 10. The system of claim 1, further comprising means for archiving past weekly reports and making the archived reports searchable.

[1833] "Application example 2 when combining emotion engines"

[1834] (Claim 1)

[1835] A means for acquiring search result and question response data collected from a user terminal;

[1836] means for transmitting the acquired data to a server;

[1837] means for assessing the emotional state of the acquired data;

[1838] means at said server for summarizing said transmitted data;

[1839] a means for generating weekly reports based on the summarized data;

[1840] means for providing interactive functionality for viewing said weekly report;

[1841] A system including:

[1842] (Claim 2)

[1843] a means for customizing the content of the weekly report based on the user's interests;

[1844] A way to notify you of the latest information on specific keywords or topics,

[1845] means for performing sentiment analysis on the retrieved content and generating and sending reports based on the results;

[1846] The system of claim 1 further comprising:

[1847] (Claim 3)

[1848] 10. The system of claim 1, further comprising means for archiving past weekly reports and making the archived reports searchable. [Explanation of symbols]

[1849] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for acquiring search result and question response data collected from a user terminal; means for transmitting the acquired data to a server; means at said server for summarizing said transmitted data; a means for generating weekly reports based on the summarized data; means for providing interactive functionality for viewing said weekly report; A system including:

2. a means for customizing the content of the weekly report based on the user's interests; A way to notify you of the latest information on specific keywords or topics, The system of claim 1 further comprising:

3. 10. The system of claim 1, further comprising means for archiving past weekly reports and making the archived reports searchable.

Images