Information processing system

Abstract

The invention provides an information processing system. The information processing system comprises a device for receiving a search keyword input by a user; the crawler engine device is used for collecting information related to the search keyword from a network information source; the device is used for filtering the collected information and eliminating false information; the device is used for classifying the filtered information according to a time sequence and categories; the generation type artificial intelligence device is used for generating the sorted information in a portal website form; and means for providing the information to the user terminal.

Description

Technical Field

[0001] The technology disclosed herein relates to an information processing system. Background Technology

[0002] Japanese Patent Application Publication No. 2022-180282 discloses a method for controlling a role-based chatbot executed by at least one processor. The method includes the following steps: receiving a user's speech; adding the user's speech to a prompt word, the prompt word containing instruction statements associated with an explanation of the chatbot's role; encoding the prompt word; and inputting the encoded prompt word into a language model to generate a chatbot response to the user's speech.

[0003] In existing technologies, users often face problems such as complex information sources, rampant misinformation, and disorganized information when searching for online information, making it difficult for them to quickly obtain highly credible, structured, and easy-to-understand information. Therefore, how to effectively filter, integrate, and display relevant information to improve the accuracy and convenience of information acquisition is a technical issue that urgently needs to be addressed in this field. Summary of the Invention

[0004] To address the aforementioned problems, this invention provides an information processing system, comprising: a device for receiving search keywords input by a user; a web crawler engine device for collecting information related to the keywords from online information sources; a device for filtering the collected information and eliminating false information; a device for classifying the filtered information according to time sequence and category; a generative artificial intelligence device for generating a portal website from the processed information; and a device for providing the information to a user terminal. Through these means, users can quickly obtain high-quality, clearly structured, and easily understandable reliable information, effectively solving the problems of false information and confusing display in traditional information retrieval.

[0005] "User" refers to an individual or group that uses this system to retrieve and browse information.

[0006] "Search keywords" refer to the words or phrases that users enter on the terminal to retrieve the information they need.

[0007] "Network information sources" refers to various websites, databases, social platforms, blogs, etc. on the Internet that can be crawled and used for information collection.

[0008] A "web crawler engine" is a software or device used to automatically access and collect content related to search keywords from online information sources.

[0009] "Filtering" refers to the process of screening collected information according to predetermined rules to remove false, irrelevant, or low-credibility information.

[0010] "False information" refers to information that is untrue, inaccurate, or misleading to users, including rumors, fake news, etc.

[0011] "Classification" refers to the process of grouping and organizing filtered information according to criteria such as time sequence and information content.

[0012] "Generative artificial intelligence" refers to artificial intelligence technologies or devices that can automatically generate logically structured and semantically organized information content based on input data.

[0013] "Portal website format" refers to a web display method that presents information in a structured, categorized, and easy-to-browse page layout.

[0014] "User terminal" refers to the device that users use to interact with the system and display information, such as computers, smartphones, tablets, etc. Attached Figure Description

[0015] Figure 1 This is a conceptual diagram illustrating an example of the configuration of the data processing system according to the first embodiment.

[0016] Figure 2 This is a conceptual diagram illustrating an example of the main functions of the data processing apparatus and smart device according to the first embodiment.

[0017] Figure 3 This is a conceptual diagram illustrating an example of the configuration of the data processing system according to the second embodiment.

[0018] Figure 4 This is a conceptual diagram illustrating an example of the main functions of the data processing device and smart glasses according to the second embodiment.

[0019] Figure 5 This is a conceptual diagram illustrating an example of the configuration of the data processing system according to the third embodiment.

[0020] Figure 6 This is a conceptual diagram illustrating an example of the main functions of the data processing apparatus and head-mounted terminal according to the third embodiment.

[0021] Figure 7 This is a conceptual diagram illustrating an example of the configuration of the data processing system according to the fourth embodiment.

[0022] Figure 8 This is a conceptual diagram illustrating an example of the main functions of the data processing device and robot according to the fourth embodiment.

[0023] Figure 9 This represents an emotion map that maps multiple emotions.

[0024] Figure 10This represents an emotion map that maps multiple emotions.

[0025] Figure 11 This is a sequence diagram illustrating the processing flow of the data processing system of the first embodiment.

[0026] Figure 12 This is a sequence diagram illustrating the processing flow of the data processing system in Application Example 1.

[0027] Figure 13 This is a sequence diagram illustrating the processing flow of the data processing system of the second embodiment.

[0028] Figure 14 This is a sequence diagram illustrating the processing flow of the data processing system in Application Example 2. Detailed Implementation

[0029] Hereinafter, an example of an implementation of the system according to the present disclosure will be described with reference to the accompanying drawings.

[0030] First, let me explain the terminology used in the following instructions.

[0031] In the following embodiments, the processor (hereinafter referred to as "processor") with reference numerals may be a single computing device or a combination of multiple computing devices. Furthermore, the processor may be a single computing device or a combination of multiple computing devices. Examples of computing devices include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0032] In the following embodiments, RAM (Random Access Memory), as indicated in the figures, is a memory that temporarily stores information and is used as working memory by the processor.

[0033] In the following embodiments, the memory, as indicated by the reference numerals, is one or more non-volatile storage devices that store various programs and parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), disks (e.g., hard disks), or magnetic tapes.

[0034] In the following embodiments, the communication I / F (Interface) with reference numerals is an interface that includes a communication processor and an antenna, etc. The communication I / F is responsible for communication between multiple computers. As an example of a communication specification applicable to the communication I / F, wireless communication specifications such as 5G (5th Generation Mobile Communication System), Wi-Fi (wireless fidelity) (registered trademark), or Bluetooth (registered trademark) can be listed.

[0035] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it can be only A, only B, or a combination of A and B. Furthermore, in this specification, when "and / or" connects to express more than three items, the same interpretation as "A and / or B" applies.

[0036] First Implementation Method

[0037] Figure 1 An example of the configuration of the data processing system 10 according to the first embodiment is shown.

[0038] like Figure 1 As shown, the data processing system 10 includes a data processing device 12 and an intelligent device 14. A server can be cited as an example of the data processing device 12.

[0039] The data processing apparatus 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" as understood in this disclosure. The computer 22 includes a processor 28, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, 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).

[0040] The smart device 14 includes a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. In addition, the receiving device 38, output device 40, camera 42, and communication I / F 44 are also connected to the bus 52.

[0041] The receiving device 38 includes a touchscreen 38A and a microphone 38B, and receives user input. The touchscreen 38A receives user input via touch by detecting contact with an indicator (e.g., a pen or finger). The microphone 38B receives user input via sound by detecting the user's voice. The control unit 46A in the processor 46 sends data representing the user input received by the touchscreen 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data representing the user input.

[0042] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting data in a form perceptible to the user 20 (e.g., sound and / or text). The display 40A displays visual information such as text and images according to instructions from the processor 46. The speaker 40B outputs sound according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0043] Communication I / F44 is connected to network 54. Communication I / F44 and 26 are responsible for sending and receiving various information between processor 46 and processor 28 via network 54.

[0044] Figure 2 The diagram shows an example of the main functions of the data processing device 12 and the smart device 14.

[0045] like Figure 2 As shown, in the data processing apparatus 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the memory 32. The specific processing program 56 is an example of a "program" as understood in this disclosure. The processor 28 reads the specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. Specific processing is implemented by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0046] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290. The specific processing unit 290 can use the emotion-specific model 59 to infer the user's emotions and perform specific processing based on the user's emotions. In the emotion inference function (emotion-specific function) using the emotion-specific model 59, various inferences and predictions related to the user's emotions are performed, including inferences and predictions of the user's emotions, but this is not limited to this example. Furthermore, emotion inference and prediction may also include, for example, emotion analysis (parsing).

[0047] In the smart device 14, the processor 46 performs the acceptance output processing. The memory 50 stores the acceptance output program 60. The acceptance output program 60 is used in conjunction with the data processing system 10 and the specific processing program 56. The processor 46 reads the acceptance output program 60 from the memory 50 and executes the read acceptance output program 60 on the RAM 48. The acceptance output processing is implemented by the processor 46 acting as the control unit 46A according to the acceptance output program 60 executed on the RAM 48. Furthermore, the smart device 14 has the same data generation model and emotion-specific model as the data generation model 58 and the emotion-specific model 59, and these models can also be used to perform the same processing as the specific processing unit 290. The acceptance output processing is implemented by the processor 46 acting as the control unit 46A according to the acceptance output program 60 executed on the RAM 48.

[0048] Alternatively, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains the processing results (prediction results, etc.) using the data generation model 58 by communicating with the server device that has the data generation model 58. Furthermore, the data processing device 12 may be a server device or a user-held terminal device (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of the processing of the data processing system 10 of the first embodiment will be described.

[0049] Example 1

[0050] The flow of a specific process in Example 1 will be described. Each part of the system described below is implemented by the data processing device 12 and the smart device 14. Furthermore, the data processing device 12 is referred to as the "server," and the smart device 14 is referred to as the "terminal."

[0051] The specific processing performed by the specific processing unit 290 of the data processing apparatus 12 in Embodiment 1 is achieved by the following means.

[0052] In this invention, the server includes an information acquisition device for obtaining an information retrieval string from a user; an information collection device for automatically acquiring information related to the information retrieval string from multiple information sources on an information communication network; an information evaluation device for evaluating the credibility of the acquired information and automatically excluding information that is inconsistent with the facts or has low credibility; an information processing device for organizing the evaluated information according to the acquisition time and content classification criteria; a generation processing device for providing the organized information as input to a generation processing mechanism and generating data for presentation based on prompts containing content structure and display layout design guidelines using generative artificial intelligence; and an information providing device for sending the generated data for presentation to the user terminal via a communication line for display. This automatically enables efficient collection, filtering, credibility determination, structured organization, and AI-optimized visual presentation of information tailored to user needs, thereby significantly improving the reliability, timeliness, and utilization efficiency of information obtained by users.

[0053] "Information acquisition device" refers to a hardware or software module that can receive information retrieval strings from users and use them for subsequent information processing.

[0054] "Information collection device" refers to a hardware or software module that can automatically acquire and retrieve string-related data from multiple information sources on an information communication network.

[0055] "Information evaluation device" refers to a hardware or software module that determines the credibility of acquired information and automatically filters out information that is inconsistent with the facts or has low credibility.

[0056] "Information processing device" refers to a hardware or software module that classifies, integrates, and structures the evaluated information according to the acquisition time and content category.

[0057] "Generative processing mechanism" refers to a combination of hardware and software that uses generative artificial intelligence to perform semantic understanding, content integration, and layout optimization of structured information to generate data for display.

[0058] "Prompt statements" refer to natural language text that serves as input to generative artificial intelligence models, instructing on content structure or display layout design guidelines.

[0059] "Information providing device" refers to a hardware or software module that sends generated data for display to a user terminal via a communication line and assists in displaying the data on the terminal device.

[0060] "Data used for presentation" refers to information content that, after being processed by generative artificial intelligence, can be directly displayed in a visual form on the user's terminal.

[0061] "Display device" refers to a hardware or software module that displays data to a user on a terminal device in the form of a graphical interface or web page.

[0062] "Input device" refers to a hardware or software module used to receive information retrieval strings input by users and transmit those strings to an information acquisition device.

[0063] This invention relates to an information processing system based on generative artificial intelligence, which can efficiently collect, filter, organize, and display highly credible information content for users. The specific embodiments of this invention are described below.

[0064] The server can use general-purpose server hardware (such as multi-core processors based on x86 architecture, sufficient memory, and high-performance storage devices) and run a mainstream operating system (such as Linux). The server can be configured with Nginx or a similar HTTP service as the entry point for network requests, and the backend application can use a framework such as Flask in Python to implement a Web API. For the database, the server can be equipped with a relational database system, such as MySQL or PostgreSQL, to store information data and intermediate results.

[0065] The server receives user input search strings from terminals via an information acquisition device. For example, a user enters "novel coronavirus vaccine information" into the browser search box using a smartphone or personal computer terminal and sends it to the server. The server then parses the HTTP request and extracts keywords from the request body.

[0066] The server utilizes information gathering devices to automatically crawl data from multiple information sources (such as news websites, authoritative portals, social media, and professional blogs). In its implementation, it can employ the Scrapy web crawling framework written in Python and the BeautifulSoup web page parsing library. The server can dynamically generate a list of URLs based on keywords and automatically retrieve the HTML content of the web pages.

[0067] The information evaluation device assesses the credibility of the collected information. The server can utilize natural language processing models (such as the BERT model based on transformers) to perform semantic analysis of content and score the credibility of sources. The system can maintain a whitelist of authoritative domains to filter out content from non-authoritative sources or suspected false information. For example, by comparing information sources with lists of organizations, analyzing document structure, and detecting excessive emotional tone, the system can automatically distinguish between true and false information.

[0068] The information processing device structures the information being evaluated according to the time and content category of data collection. The server can retrieve and sort the data in the database using SQL commands, and automatically tag each data entry (such as "news", "side effects", "research progress") using NLP technology to facilitate information partitioning and display.

[0069] The processing unit relies on generative artificial intelligence models (such as GPT-4) to understand and reintegrate structured data. The server inputs multiple categorized content fragments, keywords, and page structure requirements into the AI ​​model via natural language prompts. The generative AI then automatically generates content for display, including page layout, content summaries, titles, and text restructuring, based on the prompts.

[0070] For example, the prompt could be: "Please compile the following news, research, and side effect data into a portal page with the structure: news at the top, side effects in the middle, and research at the bottom. The content is as follows:..." or: "Based on the information about the novel coronavirus vaccine collected from authoritative websites and media below, please compile it into a public portal webpage. The page should be divided into three parts: the latest news at the top, side effect data in the middle, and research progress at the bottom. The content should be concise and logically clear. The following is the compiled original information:..."

[0071] The server encapsulates the HTML or structured data generated by the artificial intelligence model into an HTTP response via an information delivery device and sends it over the network to the terminal device. The user's terminal device can be a smartphone, tablet, or personal computer equipped with a standard web browser.

[0072] The terminal receives data from the server using a display device and presents it on the screen in the form of web pages. For HTML format data, the terminal's browser can directly render the page; for structured data such as JSON, the front-end application can use technologies such as JavaScript to dynamically display the data, enabling multi-section content display.

[0073] Users can enter keywords of interest through input devices (such as on-screen keyboards, physical keyboards, etc.) to browse authoritative and reliable information that has been filtered and processed by the server, such as the latest developments in the epidemic, statistics on vaccine side effects, or related scientific research results, thereby improving the accuracy and timeliness of information retrieval and utilization.

[0074] use Figure 11 The processing flow is explained.

[0075] Step 1:

[0076] The user enters keywords for the information they want to search in the browser interface on the terminal and clicks the search button. The input is the search keywords entered by the user (such as "novel coronavirus vaccine information"). The output is an HTTP request containing the keywords sent to the server.

[0077] Step 2:

[0078] The terminal sends the user-input search keywords to the server's API interface via an HTTP POST request. The input consists of the user-entered keywords and related request information. Upon receiving the request, the server parses the search keywords, using them as the basis for subsequent processing. The output is the parsed keyword data.

[0079] Step 3:

[0080] The server utilizes an information gathering device to dynamically construct a queue of URLs from multiple information sources based on the parsed search keywords, and then calls web crawlers (such as Scrapy or BeautifulSoup) to automatically crawl relevant web page content. The input is the parsed search keywords. The server processes the keywords, generating a list of URL tasks and downloading the web page HTML data. The output is structured data containing raw information such as the news article, title, source, and publication time.

[0081] Step 4:

[0082] The server uses an information evaluation device to perform credibility analysis and data processing on the collected raw information. The input is structured raw information data. The server uses algorithms (such as source domain whitelist matching, natural language processing model analysis, content verification, etc.) to filter and remove false or low-credibility content, assigning a credibility score to each piece of information and filtering accordingly. The output is a set of filtered, highly credible information data.

[0083] Step 5:

[0084] The server uses an information processing device to classify and sort the filtered information based on time and content category (such as "news," "side effects," "research progress," etc.). The input is a set of filtered information data. The server sorts this data in descending order of collection time and automatically labels and classifies it using keywords or NLP models. The output is a processed, classified, and structured set of data.

[0085] Step 6:

[0086] The server invokes a processing mechanism to generate prompts from the categorized structured information, search keywords, and page display requirements, and inputs these prompts into a generative artificial intelligence model (such as GPT-4). The input consists of structured information data and prompts. Based on the prompts and the model API, the server generates HTML page content or visual data suitable for the page display requirements. The output is formatted data for the portal website page display.

[0087] Step 7:

[0088] The server, through an information providing device, repackages the generated page content (HTML or structured data) into an HTTP response and returns it to the terminal device via network transmission. The input is the page content output by the AI ​​model. The server packages the content and outputs it as response data to the terminal.

[0089] Step 8:

[0090] The terminal receives information data returned by the server and renders and displays the page content through a browser. The input is an HTML page or structured data from the server. Depending on the data type, the terminal either directly displays the webpage content or dynamically converts the structured content into a multi-functional information page using JavaScript. The output is a highly reliable, categorized information page displayed on the user's terminal screen.

[0091] Step 9:

[0092] Users browse pages on the terminal, read relevant information, and can click on news details, switch information categories, or continue entering new search keywords as needed. The input is the information page displayed on the terminal. Through interactive operations, users obtain the detailed content they need or initiate new data retrieval requests. The output is the valid information obtained by the user or the formation of a new search process.

[0093] Application Example 1

[0094] The process flow corresponding to the specific processing in Use Case 1 will be described below. The various parts of the system described below are implemented by the data processing device 12 and the intelligent device 14. Furthermore, the data processing device 12 is referred to as the "server" and the intelligent device 14 is referred to as the "terminal".

[0095] Existing information retrieval systems struggle to guarantee the authenticity and authority of information collected based on search keywords, making them susceptible to the inclusion of false or low-reliability information. Furthermore, the requirements for information display on terminal devices (especially smart wearable devices) differ from those of traditional devices, and existing systems lack support for multimodal interaction and personalized information presentation. In addition, user emotional states are not adequately considered, failing to adaptively output the most suitable information based on the user's actual needs and emotional state, thus limiting the user experience and hindering efficient, reliable, and intelligent information acquisition.

[0096] The specific processing performed by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is achieved by the following means.

[0097] In this invention, the server includes a device for receiving search criteria from a user, a crawler device for automatically acquiring relevant information from a communication network, a filtering device for assessing the credibility of the acquired information and eliminating false information, a device for structuring and classifying information according to time sequence and attribute categories, a device for generating prompts for generative artificial intelligence models and shaping the structured information into a format suitable for terminal output, a device for parsing user voice input and converting it into search criteria, a synthesis device for converting structured information data into voice data, and an output device for outputting the shaped information or voice data to the user's display device. It may also include a recognition device that adaptively adjusts the information display order based on the user's emotional state. This effectively eliminates low-credibility or false information, achieving efficient information filtering, intelligent classification, multimodal and personalized display, and optimizing the output order based on the user's emotional state, thereby improving the timeliness, accuracy, and user experience of information acquisition.

[0098] "Input device" refers to a hardware or software device used to receive user input search criteria information, including but not limited to interfaces such as keyboards, touch screens, and microphones.

[0099] "Acquisition device" refers to a data acquisition component that can automatically acquire relevant information from multiple information collections through a communication network, and may include information capture modules implemented in software and hardware.

[0100] "Evaluation device" refers to a hardware or software unit that assesses the authenticity, authority, and credibility of acquired information and can automatically exclude false or low-credibility information.

[0101] "Organization device" refers to a data processing module that structures, classifies, and sorts evaluated information according to time sequence and attribute categories.

[0102] "Shaping device" refers to a device that reorganizes or converts structured information into a format suitable for the display characteristics of terminal devices according to instructions applicable to generative artificial intelligence models.

[0103] "Generative artificial intelligence models" refer to artificial intelligence systems or algorithms that can generate natural language text, summaries, or reconstruct data content based on prompts.

[0104] "Analysis device" refers to the hardware and software components that can recognize and process voice input and convert it into search criteria.

[0105] "Information synthesis device" refers to a synthesis module that can convert text information into speech data for playback by an audio output device.

[0106] "Providing device" refers to an interface or communication device that transmits shaped information or voice data to a user display terminal.

[0107] "Identification device" refers to a device that infers the user's emotional state based on an artificial intelligence model and adaptively adjusts the order of information display according to the user's state.

[0108] "Information aggregates" refer to various information resources that can be accessed through communication networks, including data sources such as websites, data platforms, and social media.

[0109] "Prompt statements" refer to input statements or instructions used to incentivize generative artificial intelligence models to output specific, customized content.

[0110] To facilitate understanding of the present invention, the following detailed description of the "inventive form" of the system of the present invention is provided in conjunction with specific hardware and software examples.

[0111] The system of this invention can be implemented by combining an information processing device (server), user terminal equipment, and various data processing and artificial intelligence technologies. The server can be configured on a cloud platform or in a local data center, and the terminal equipment includes, but is not limited to, smart glasses, smartphones, tablets, and personal computers.

[0112] The following hardware and software can be integrated on the server:

[0113] Server hardware such as general computing devices, network communication modules, and storage devices.

[0114] - Server software can use open-source or commercial operating systems (such as Linux), Python runtime environments, and related dependency packages.

[0115] Software used for web scraping can be developed using the Python programming language, and can use tools such as the Requests library and BeautifulSoup library to automatically obtain relevant content from target data sources (such as news sites, social media platforms, brand official websites, etc.).

[0116] - Credibility assessment and filtering can be implemented using existing data quality assessment algorithms, automated rules, or machine learning models.

[0117] Data organization and structuring by time and category can be accomplished using data processing libraries such as Pandas.

[0118] - Integrate generative AI models on the server side, such as open-source GPT-like models or commercial generative language services. Use prompts to drive the model to reorganize, summarize, and optimize presentation of information.

[0119] User emotion recognition can be accomplished through emotion analysis models, such as the BERT sentiment analysis model and SKEP for Chinese, or it can be combined with a speech intonation analysis module.

[0120] - Text-to-speech (TTS) modules can be implemented using open-source TTS systems or third-party services such as the Google Speech API.

[0121] The following hardware and software can be integrated into the terminal:

[0122] - Voice input is captured by a microphone and converted into text by a speech recognition engine (such as Google Speech-to-Text).

[0123] - Display devices include the HUD display panel of smart glasses, mobile phone or computer screen.

[0124] The collected text keywords and user parameters are automatically uploaded to the server.

[0125] The received structured data can be parsed and displayed in a user-friendly manner through a browser or local application.

[0126] - The terminal is equipped with an audio output device (such as a speaker or headphones) to automatically broadcast important information using the TTS module, enabling visual and auditory multimodal information prompts.

[0127] The system workflow is as follows: Users input keywords via voice or text on the terminal, which then reports the converted search criteria to the server. The server automatically extracts a large amount of information from various data collections, performs credibility assessments, and organizes the data according to specified categories and time sequences. The organized data is then fed into a generative artificial intelligence model, driven by prompts, to generate summaries, classifications, and reorganizations. If combined with emotion recognition, the server can adjust the output order or recommended content types based on the user's current emotional state. The final generated content is sent back to the terminal via the network, where it is displayed on the screen and can automatically read aloud.

[0128] Specific application examples:

[0129] For example, a user wearing smart glasses can say into the microphone, "Latest side effects of COVID-19 vaccines." The system automatically crawls various health, medical, and official data websites, filters out low-reliability information, and uses a generative artificial intelligence model to generate concise and authoritative summary information. The summary is automatically displayed on the HUD, showing "The incidence of side effects is..., the latest research shows...", and the core content is broadcast through the speaker. Users can directly obtain important information through sight and sound.

[0130] Another concrete example is that when a user enters "spring fashion matching" on their mobile phone, the server automatically collects popular content from e-commerce platforms and brand websites, categorizes it into "tops," "trousers," and "color trends," and uses a generative artificial intelligence model to reorganize it into a concise page suitable for mobile screens. Users can click to view the content, and each category also offers an audio guide, achieving a brand-new interactive experience.

[0131] Example of a prompt statement:

[0132] Please categorize and summarize the information collected under the topic of "COVID-19 vaccine side effects" into "Latest News", "Common Side Effects", and "Scientific Statistics". Each suggestion should not exceed 100 words, be suitable for horizontal card display on mobile phones, and should not include unverified rumors.

[0133] For example:

[0134] Please compile a list of 2024 fashion trends and top-selling items on e-commerce platforms, categorized by clothing type. The summary should be concise and the format suitable for displaying SMS messages on smart glasses.

[0135] In this way, through the organic collaboration of servers, terminals, and users, and by making full use of generative artificial intelligence models and emotion perception technology, efficient, reliable, personalized, and multimodal information acquisition and presentation can be achieved.

[0136] use Figure 12 The processing flow is explained.

[0137] Step 1:

[0138] Users input information through a terminal. Users enter search keywords on the terminal (such as smart glasses, mobile phones, etc.), which can be done via voice or text input. The input consists of keywords representing the information the user wants to search for, such as "COVID-19 vaccine side effects." The terminal collects the user's input data and prepares to send it to the server.

[0139] Step 2:

[0140] The terminal performs data preprocessing and transmission. It calls speech recognition software (such as a speech-to-text module) to convert the user's voice input into text, and combines this with additional information such as device ID and geographic location to generate a request packet. The input consists of raw voice data or text data and related metadata. The output is structured request data, which is sent to the server.

[0141] Step 3:

[0142] The server receives and parses the request. It receives structured request data from the terminal and parses it to extract search keywords and user-related parameters. The input to this process is the request data uploaded by the terminal, and the output is the query parameters and user information used for subsequent searches.

[0143] Step 4:

[0144] The server performs information scraping. Based on search keywords, the server calls information scraping modules (such as web crawlers written with Requests and BeautifulSoup) to automatically scrape data related to the keywords from multiple information sources (such as news websites, official platforms, and social media). The input is the search keywords and a list of target information sources; the data processing involves web data collection and gathering; and the output is the initially collected raw data set.

[0145] Step 5:

[0146] The server performs information filtering and evaluation. Using a credibility filtering module, the server assesses the trustworthiness of the raw data, excluding false or low-credibility information based on rules (such as source domain, keyword filtering, and machine learning model scoring). The input is the raw dataset, and the data processing includes content discrimination and feature selection. The output is a set of filtered, high-credibility information.

[0147] Step 6:

[0148] The server categorizes and organizes information chronologically. It classifies and sorts the filtered information according to category (e.g., news, data, user comments) and collection time. The input is a set of highly reliable information; data processing includes content clustering and chronological sorting. The output is a structured and categorized dataset.

[0149] Step 7:

[0150] The server invokes a generative artificial intelligence model to reshape information. The server inputs structured information data, combined with prompts, into the generative AI model. Through summarization, recombination, and information simplification, it generates content in a format suitable for terminal display. The input consists of a categorized dataset and prompts; the data processing involves AI recognition and content generation. The output is an optimized, concise content summary and visualization.

[0151] Step 8:

[0152] The server performs sentiment analysis and adjusts the output order (if necessary). The server uses a sentiment recognition module to analyze user search history, interaction behavior, and voice emotion to infer the user's emotional state (e.g., anxiety, doubt). Inputs are user behavior data and query history; data processing involves sentiment inference and priority adjustment. Output is the optimized information order.

[0153] Step 9:

[0154] The server packages the final data and sends it to the terminal. Based on the terminal type (e.g., smart glasses, mobile phone), the server uses the appropriate data format (e.g., JSON, HTML) to package the shaped content and voice data, and sends it to the terminal via the network interface. The input consists of the final content data and output format parameters, processed into data encapsulation. The output is the data transmitted to the terminal.

[0155] Step 10:

[0156] The terminal receives and parses information. It receives content from the server and uses its local parsing module to reconstruct usable pages, entries, etc. The input is the data packet sent by the server; processing includes data parsing and content preparation. The output is information units that can be displayed and broadcast.

[0157] Step 11:

[0158] The terminal provides visual displays and voice broadcasts. It displays data on the screen in an appropriate format and generates audio for key content using a speech synthesis module, which is then played through a speaker. Input consists of displayable information units and TTS text; the data is processed into readable pages and a voice stream. Output is a user-visual and audible information display.

[0159] Step 12:

[0160] Users browse and utilize information. Users view presented information pages through the terminal and can listen to voice prompts. They can then click, query, or input new commands to initiate the next cycle. Input consists of visual and auditory information content, while output is the user's active or passive response to the information.

[0161] Alternatively, an emotion engine for inferring user emotions can be combined. That is, the specific processing unit 290 can also use the emotion-specific model 59 to infer user emotions and perform specific processing using user emotions.

[0162] Example 2

[0163] The flow of a specific process in Example 2 will be described. Each part of the system described below is implemented by the data processing device 12 and the smart device 14. The data processing device 12 will be referred to as the "server," and the smart device 14 as the "terminal."

[0164] Existing information retrieval systems struggle to effectively and promptly filter out highly credible and relevant information for users when faced with the vast amount of data available on the internet. Traditional systems often fail to automatically exclude low-credibility or misleading content, lack chronological and attribute-based management of information, and fail to dynamically personalize information presentation based on users' real-time emotional states. Consequently, users find it difficult to obtain authoritative and targeted information in a timely manner, and the convenience and reliability of information utilization need improvement.

[0165] The specific processing performed by the specific processing unit 290 of the data processing apparatus 12 in Embodiment 2 is achieved by the following means.

[0166] In this invention, the server includes an information acquisition device for receiving user search information, a data collection device for automatically collecting and retrieving relevant literature, an information evaluation and removal device for automatically assessing and removing unreliable content based on credibility, an information classification device for partitioning and organizing information according to time sequence and attributes, an information structuring device for generating structured display data based on classification results and user characteristics using a generative artificial intelligence model, an emotion recognition and priority adjustment device for identifying user emotional information and dynamically prioritizing the displayed content, and a data transmission device for transmitting structured data to the user terminal in an optimized format. This allows for the automatic and efficient acquisition, filtering, classification, and generation of highly credible, structured information display content that meets the user's personalized needs, significantly improving information filtering accuracy, response speed, and user experience.

[0167] "Information acquisition device" refers to a hardware or software structure used to receive retrieved information from users and transmit that information to the internal processing module of a server.

[0168] "Automatic information collection device" refers to a system component that can automatically collect relevant documents and data from multiple information sources on a communication network based on user-retrieved information, including web crawler engines.

[0169] "Information evaluation and elimination device" refers to a processing module that performs credibility analysis on the collected document content and filters and eliminates low credibility and erroneous information according to preset rules or algorithms.

[0170] An "information classification device" refers to a data processing unit that classifies and sorts document information based on its acquisition time and attributes after evaluating and eliminating undesirable information.

[0171] "Information generation and structuring device" refers to a system unit that, based on information classification results and user characteristics, uses a generative artificial intelligence model to transform document content into a structured display format.

[0172] "Emotion recognition and priority adjustment device" refers to a software or hardware module that automatically identifies a user's current emotional state by analyzing user input and historical behavior data, and automatically adjusts the priority of information display accordingly.

[0173] "Data transmission device" refers to a functional unit that sends structured display data in an optimized format to the user's operating terminal via a network interface.

[0174] In one embodiment of the present invention, the system includes a server, a terminal, and a user as the main components. The server adopts a general-purpose computer hardware platform, in conjunction with a database server and a high-performance network communication device. The terminal can be an information processing device with network communication capabilities, such as a smartphone, tablet computer, or desktop computer.

[0175] The server is configured with an operating system (e.g., Linux), web service middleware (e.g., Nginx or Apache), a database management system (e.g., MySQL or MongoDB), and a programming language environment (e.g., Python or Java). Deployed on the server are modules for information acquisition, automated information collection (e.g., using Scrapy or Nutch as crawler engines), information evaluation and elimination (utilizing judgment algorithms and a trusted source list), information classification (using natural language processing tools such as jieba or NLTK), information generation and structuring (integrating generative artificial intelligence models such as GPT-3 or BERT), sentiment recognition and prioritization (calling sentiment analysis models such as BERT), and data transmission (implementing an HTTP API interface).

[0176] Users can enter keywords for the information they want to search for, such as "side effects of novel coronavirus vaccine," into the user interface of the browser or client app on the terminal. The terminal then sends the keywords to the server API interface using local or built-in applications.

[0177] After receiving a request from the terminal, the server first verifies the format of the input information using the information acquisition module. Then, the automatic information collection module crawls data related to the keywords from multiple online channels, including news articles, official publications, and social media content. The server then uses an information evaluation and filtering module to automatically analyze the credibility of the information sources, excluding suspicious or low-credibility content and retaining only reliable original documents. Finally, the server calls the information classification module to automatically categorize the documents according to time, topic, and information attributes, assigning relevant documents to categories such as "Latest News," "Side Effects Information," and "Research Progress."

[0178] Furthermore, the server invokes generative artificial intelligence models (such as GPT-3 and BERT) and, in conjunction with standard prompts, summarizes and structures the categorized information. For example, a prompt might be used: "Please compile information about 'side effects of the novel coronavirus vaccine' in a way that follows the style of a portal news website homepage, categorizing and highlighting authoritative data to reassure concerned users." The system then categorizes and arranges the information according to this prompt, allowing users to easily navigate the page structure.

[0179] The server's emotion recognition and prioritization module analyzes user emotions, such as anxiety and concern for safety, based on user input and behavioral data. The server then dynamically adjusts the priority of information display accordingly. For example, for users expressing concern, authoritative safety data on vaccines and related frequently asked questions are highlighted first.

[0180] After the above processing, the server pushes the structured page content to the user's terminal in HTML, JSON, or other formats via the data sending module. The terminal receives the data and uses its built-in browser or application to render the front-end page, displaying content such as "Latest News," "Side Effect Statistics," and "Expert Q&A" in sections. Users can then click on content of interest to browse authoritative original documents, medical data, and more, achieving an efficient, secure, and personalized information retrieval experience.

[0181] Specific examples:

[0182] When a user enters "side effects of novel coronavirus vaccine" into a smartphone app, the server completes all the above processing steps in sequence, and finally displays information such as "official side effect statistics and analysis", "latest research conclusions" and "interpretation of frequently asked questions" in a portal website style on the mobile device.

[0183] Typical prompts for generative artificial intelligence models are as follows:

[0184] Please summarize the latest authoritative information, side effects, and research data on "novel coronavirus vaccine information" in the style of a portal website news section, highlighting reassuring data to calm concerned users.

[0185] When users input "side effects of novel coronavirus vaccine", please prioritize displaying the most credible and up-to-date statistics and official statements related to side effects, following the layout structure of authoritative news portals.

[0186] The following data is categorized into "Latest News," "Side Effects Data," and "Research Findings," and presented as a portal website homepage, suitable for mobile users who want to quickly access information.

[0187] use Figure 13 The processing flow is explained.

[0188] Step 1:

[0189] Users enter search keywords (such as "side effects of novel coronavirus vaccine") on the terminal and submit a query request through the interface.

[0190] Input: Search keywords entered by the user.

[0191] Output: Structured query request data, sent to the server.

[0192] Specific actions: The terminal collects user input, encapsulates it into JSON format data, and sends it to the server API interface via the HTTP protocol.

[0193] Step 2:

[0194] The server receives query requests from the terminal and parses and standardizes the search keywords.

[0195] Input: Query request data from the terminal.

[0196] Output: Normalized keyword data.

[0197] Specific actions: The server uses a word segmentation tool (such as jieba) to perform semantic segmentation and spell correction on the received keywords, forming a standard structure to prepare data for subsequent information collection.

[0198] Step 3:

[0199] The server uses an automatic information gathering module (such as Scrapy) to crawl relevant documents and data from online information sources based on standardized keywords.

[0200] Input: Standardized keyword data.

[0201] Output: Raw, unprocessed collection of multi-source documents and data.

[0202] Specific actions: The server generates a list of target URLs to be crawled and schedules the crawler engine to access news websites, official agency sites and social media platforms to collect web page content, posts, data files and so on related to keywords.

[0203] Step 4:

[0204] The server performs credibility assessments on the crawled information and removes invalid data.

[0205] Input: The raw, unprocessed dataset.

[0206] Output: A highly reliable dataset.

[0207] Specific actions: The server calls the information evaluation algorithm, combined with reputation database, source scoring, content similarity analysis and other means, to remove low-credibility content such as advertisements and anonymous posts, and retain only information from authoritative channels.

[0208] Step 5:

[0209] The server categorizes and organizes highly reliable information according to time sequence and attributes.

[0210] Input: A highly reliable dataset.

[0211] Output: The sorted and categorized dataset.

[0212] Specific actions: The server analyzes the publication time and topic of each document (such as side effects, latest news, academic research, etc.), stores it using database indexes, and adds category tags to form structured categorized data.

[0213] Step 6:

[0214] The server uses a generative artificial intelligence model (such as GPT-3) to perform structured generation based on classification results and preset prompts.

[0215] Input: The categorized and sorted data set and the prompt statement.

[0216] Output: Structured page content in a portal website style.

[0217] Specific actions: The server passes data along with prompts such as "Please compile and highlight authoritative data on 'side effects of the novel coronavirus vaccine' in a way that resembles the homepage style of a portal news website, suitable for reassuring concerned users." to a generative AI model. The model then generates page text, paragraph structure, and a recommendation list.

[0218] Step 7:

[0219] The server analyzes the user's emotional state and dynamically adjusts the priority of information display accordingly.

[0220] Input: User's search keywords, historical behavior records, and structured page content.

[0221] Output: Page content after priority is dynamically adjusted.

[0222] Specific actions: The server calls the emotion recognition module to identify the user's possible concerns or areas of focus, highlights reassuring and authoritative content at the top of the page, and adjusts the resource order.

[0223] Step 8:

[0224] The server sends the final structured page content to the terminal in HTML or JSON format.

[0225] Input: Page content after priority is dynamically adjusted.

[0226] Output: A terminal-oriented structured data file.

[0227] Specific actions: The server calls the API interface, packages the generated page content, and returns it to the terminal via the HTTP protocol, ensuring that the data format is adapted to the display requirements of different terminals.

[0228] Step 9:

[0229] The terminal receives and renders the structured page content returned by the server, displaying a portal-style information page to the user.

[0230] Input: The structured data file returned by the server.

[0231] Output: Interactive information pages displayed in sections on the terminal interface.

[0232] Specific actions: The terminal parses HTML or JSON content and uses a local rendering engine to display sections such as the latest news, authoritative statistics, and explanations of side effects. Users can click to view detailed content or initiate a new round of queries.

[0233] Application Example 2

[0234] The process flow corresponding to the specific processing in Use Case 2 will be described below. The various parts of the system described below are implemented by the data processing device 12 and the intelligent device 14. In addition, the data processing device 12 is referred to as the "server" and the intelligent device 14 is referred to as the "terminal".

[0235] With the development of the information society, the internet contains a massive amount of information resources. Users are easily misled by false, inaccurate, or low-reliability information when searching for information, making it difficult to obtain the high-reliability content they need in a timely and accurate manner. Furthermore, current technologies rarely dynamically adjust the display order of information based on users' search behavior and psychological state, leading to anxiety, stress, or misjudgment during the information acquisition process. Therefore, providing users with a reliable and clearly categorized information presentation method based on their emotional state has become an urgent technical challenge.

[0236] The specific processing performed by the specific processing unit 290 of the data processing apparatus 12 in Application Example 2 is achieved by the following means.

[0237] In this invention, the server includes a device for receiving information retrieval terms, an information collection and processing device for automatically collecting relevant information from multiple information sources, an information evaluation device for assessing information credibility and eliminating inaccurate information, an information structuring device for organizing, classifying, and sorting information according to time and content attributes, an artificial intelligence model device for automatically generating information pages, a sentiment analysis device for analyzing user emotional states and dynamically adjusting the information display order, and a sending and display device for sending and displaying the generated information pages to an external information processing device. This enables the automatic filtering and priority display of credible and structured information content based on the individual user's emotional state, effectively avoiding the influence of false information and improving the user's information acquisition efficiency and psychological comfort.

[0238] "External information processing device" refers to a computing device used to interact with a server via a network, receive and display information pages, and allow users to input information for retrieval, including but not limited to smart terminals, computers, tablets and other electronic devices.

[0239] "Information retrieval terms" refer to the text, phrases, or keywords that users input into external information processing devices to express their information needs.

[0240] "Information collection and processing device" refers to a functional unit used to automatically capture and organize data related to information retrieval terms from multiple information sources on a wide area communication network.

[0241] "Information set" refers to the sum of data, in a structured or raw state, that is related to a specific search term and is obtained by an information collection and processing device.

[0242] "Information evaluation device" refers to a processing unit that automatically analyzes and judges the credibility and authenticity of the acquired information set and filters out inaccurate information.

[0243] "Information structuring device" refers to a data processing unit that organizes, classifies, and arranges information sets after evaluation and screening according to rules such as time attributes and content categories.

[0244] "Artificial intelligence model device" refers to a tool component that uses generative artificial intelligence models to automatically summarize, edit, and beautify structured information sets, thereby generating suitable information pages.

[0245] "Emotional analysis device" refers to a functional unit that automatically analyzes and judges the user's current emotional state based on the user's operation history and search behavior, and adjusts the display priority of information content according to the analysis results.

[0246] "Transmission device" refers to a communication unit that transmits the generated structured information page data to an external information processing device via a network.

[0247] "Display device" refers to the hardware and software modules that display received information pages to users in a visual interface on an external information processing device.

[0248] "Multiple information sources" refers to various information acquisition platforms on the Internet, including news websites, official agency websites, social media, blogs, and other types of platforms.

[0249] To facilitate understanding and implementation of the present invention, the specific embodiments of the system of the present invention will be described in detail below with reference to examples.

[0250] The system of this invention consists of a server and external information processing devices (such as smartphones, tablets, or computers). The server is equipped with multiple functional modules and communicates with the terminal through a network. It can efficiently provide users with reliable, structured information pages that can be prioritized based on the user's emotional state.

[0251] The server hardware can utilize high-performance general-purpose servers, such as processors based on x86 or ARM architectures, equipped with sufficient RAM and SSD storage. Network communication can maintain real-time data interaction with terminal devices via wired or wireless means.

[0252] The terminal can be a portable device with network communication and screen display capabilities, such as a smartphone or tablet running Android or iOS, or a regular desktop computer.

[0253] The following key software modules are configured on the server:

[0254] (1) Web crawling engines (such as Scrapy, Selenium, etc.) are used to crawl information related to user-input search terms from multiple information sources on the Internet, such as news sites, government websites, social platforms, and blogs.

[0255] (2) Information evaluation and filtering system, which can use natural language processing (NLP) models such as BERT, RoBERTa, or integrated text fingerprint comparison library to perform credibility and authenticity analysis and scoring on the captured information, and automatically identify and remove false, inaccurate information and low-quality content.

[0256] (3) Classification and structuring module: The filtered information can be organized and archived according to different content categories (such as "news reports", "academic research", "user comments" etc.) and time dimensions through data processing tools such as Pandas or search engine databases (such as Elasticsearch).

[0257] (4) Generative artificial intelligence models (such as GPT-4, ChatGPT, Wenxin Yiyan, etc.) accept structured information and content classification as input, and automatically generate page summaries, reorganize information structures, optimize page layout, and output content suitable for portal website display through prompts.

[0258] (5) Sentiment analysis module: Analyzes the user's recent operation history, uses emotion recognition AI (such as statistical models or deep learning models based on emotion dictionaries) to identify the user's emotions such as anxiety, doubt, curiosity, calmness, etc., and adjusts the information arrangement order accordingly.

[0259] (6) The data push and page display system pushes the final generated information page to the terminal in HTML, JSON, or other formats via protocols such as HTTP and HTTPS. The terminal renders and visualizes the page interface through a built-in browser or App front-end module (such as React Native or Vue.js) for users to view and operate intuitively.

[0260] The software implementation of the above system can be deployed on mainstream operating systems (such as Linux, Windows Server, etc.), using open-source or commercially licensed tool components, and is highly scalable.

[0261] Specific examples:

[0262] Suppose a user uses a smartphone, enters the query "COVID-19 vaccine information", and clicks search.

[0263] The server activates its web crawler engine, automatically collecting relevant text and comments from several authoritative news websites, public academic databases, and social media. A filtering system automatically scores the collected information, directly removing content that matches the characteristics of rumors or has an unknown source. The remaining data is categorized into "Related News Reports," "Side Effects Discussions," and "Scientific Data," sorted by publication time, prioritizing the retention of the most recent developments.

[0264] The generative AI model receives the processed data, extracts key points through prompts, and organizes them in a user-friendly manner with sections, titles, summaries, and expert opinions to form the portal page HTML. The sentiment analysis module identifies users' recent activity history, showing multiple searches for related side effects, and judges their anxiety. For such users, it prioritizes pushing information with expert interpretations, data evidence, and positive reviews, reducing the display of misleading content. The server then pushes the rendered page to the user's device, allowing for clear browsing on their mobile phone.

[0265] Data exchange between the server and the terminal uses the HTTPS protocol to ensure secure information transmission. The page layout is compatible with various device resolutions.

[0266] Example of prompts for generative artificial intelligence models:

[0267] "Please summarize and refine the following content to generate a structured summary suitable for a mobile portal page, emphasizing recent authoritative news and science-based information addressing user anxieties, and excluding unverified rumors."

[0268] The above methods enable users to collect, filter, structure, intelligently generate, emotionally adaptively sort, and dynamically display information on the terminal, helping them obtain the knowledge and information they need efficiently, reliably, and with peace of mind.

[0269] use Figure 14 The processing flow is explained.

[0270] Step 1:

[0271] Users enter search terms (such as "COVID-19 vaccine information") on the terminal and click the search button.

[0272] Input: Search terms entered by the user.

[0273] Output: The search request and keywords are sent to the server.

[0274] Specific actions: The terminal packages the input content and sends it to the specified interface of the server via the HTTP POST method.

[0275] Step 2:

[0276] The server receives search terms from the terminal and initiates the information collection and processing device.

[0277] Input: Search request and keywords.

[0278] Output: A collection of information related to the keywords (raw data).

[0279] Specific actions: The server calls a web crawler engine, such as Scrapy or Selenium, to automatically access multiple news websites, official platforms, social media and other information sources, and crawl relevant web page content, comments and data based on keywords to form a preliminary information set.

[0280] Step 3:

[0281] The server uses an information evaluation device to analyze the credibility and authenticity of the collected information sets, filtering out false and unreliable content.

[0282] Input: The original set of information.

[0283] Output: A filtered set of highly reliable information.

[0284] Specific actions: The server uses NLP models (such as BERT) to analyze the content, source, author, and time of each message, and removes data that does not meet credibility requirements (such as rumors and content from unknown sources). Data is then compared with a known database of false information to further strengthen the filtering.

[0285] Step 4:

[0286] The server uses information structuring devices to classify and sort collections of highly reliable information.

[0287] Input: A set of filtered, highly reliable information.

[0288] Output: A structured, grouped dataset sorted by time and category.

[0289] Specific actions: The server uses Pandas or Elasticsearch to automatically categorize and sort the information according to preset categories (such as "news reports", "academic research", "user comments") and timestamps, and generates data structures for page display.

[0290] Step 5:

[0291] The server invokes a generative artificial intelligence model device to summarize and optimize the structured information based on certain prompts, generating a portal page format.

[0292] Input: Structured categorized and sorted data, and optimized page prompts.

[0293] Output: Portal page content suitable for terminal display (e.g., HTML / JSON format).

[0294] Specific actions: The server submits a prompt containing information content and categories to the generative artificial intelligence model (such as GPT-4), such as "Please categorize the following content into news, comments, and scientific research, and generate a portal page summary and recommended description." The AI ​​automatically outputs the corresponding page blocks and summary.

[0295] Step 6:

[0296] The server analyzes the user's emotional state using a sentiment analysis device and readjusts the priority and display order of information based on the analysis results.

[0297] Input: User's recent activity history, search terms, and AI-generated page content.

[0298] Output: Information priority and final page content after emotion adaptation.

[0299] Specific actions: The server analyzes data such as the user's historical search behavior and click trajectory, calls the sentiment recognition model (such as sentiment dictionary + deep learning network), and if it determines that the user is in an anxious state, it will prioritize and display positive, authoritative and reassuring information.

[0300] Step 7:

[0301] The server will push the final generated portal page content to the terminal device.

[0302] Input: The final page content after editing.

[0303] Output: Page data is transmitted to the terminal.

[0304] Specific actions: The server sends data in HTML or JSON format to the terminal through a secure protocol (such as HTTPS) to ensure the security and integrity of data transmission.

[0305] Step 8:

[0306] The terminal receives and renders the page content from the server and displays it to the user.

[0307] Input: Portal page data pushed by the server.

[0308] Output: A page displaying structured information about the partitions on the terminal.

[0309] Specific actions: The terminal parses the data through the browser or App front-end rendering engine, and clearly displays news, comments, academic content, etc., according to categories and priorities, making it easy for users to browse and click on detailed content.

[0310] The specific processing unit 290 sends 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 sound representing user input regarding the result of the specific processing. The control unit 46A sends the sound data representing 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 sound data.

[0311] Data generation model 58 is a so-called generative AI (Artificial Intelligence). Examples of data generation models 58 include ChatGPT (registered trademark) (accessible via the internet (URL: https: / / openai.com / blog / chatgpt)). Data generation model 58 is obtained through deep learning on a neural network. Input to data generation model 58 are prompt words containing instructions, and inference data such as sound data representing sound, text data representing text, and image data representing images (e.g., still image data or animation data). Data generation model 58 infers from the input inference data based on the instructions represented by the prompt words and outputs the inference result in one or more data forms, such as sound data, text data, and image data. Data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or induction. The specific processing unit 290 performs the aforementioned specific processing while using data generation model 58. The data generation model 58 can also be a model finely tuned to output inference results from prompts that do not contain instructions. In this case, the data generation model 58 can output inference results based on prompts that do not contain instructions. The data processing apparatus 12, etc., includes various data generation models 58, including AI other than the generation AI. AI other than the generation AI can be, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or Naive Bayes, and can perform various processes, but is not limited to this example. Furthermore, the AI ​​can also be an AI agent. Furthermore, when the processing of the above-mentioned parts is performed by AI, the processing can be partially or entirely performed by AI, but is not limited to this example. Furthermore, the processing performed by the AI ​​including the generation AI can be replaced by processing in the rule base, and the processing in the rule base can also be replaced by processing performed by the AI ​​including the generation AI.

[0312] Furthermore, the processing of the aforementioned data processing system 10 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it can also be performed by both the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Additionally, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or external devices, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or external devices.

[0313] For example, the collection unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit uses the camera 42 or communication I / F 44 of the smart device 14 to acquire step data, which is then processed by the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes the data from the collection unit and the acquisition unit. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12, which uses a generation AI to generate a menu. For example, the serving unit is implemented by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing device 12, which provides the generated menu to the user. The correspondence between each unit and the device or control unit is not limited to the above examples and various changes can be made.

[0314] In the above embodiments, examples of specific processing by the data processing device 12 are given, but the technology disclosed herein is not limited to this, and specific processing may also be performed by the smart device 14.

[0315] Second Implementation Method

[0316] Figure 3 An example of the configuration of the data processing system 210 according to the second embodiment is shown.

[0317] like Figure 3 As shown, the data processing system 210 includes a data processing device 12 and smart glasses 214. A server can be cited as an example of the data processing device 12.

[0318] The data processing apparatus 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" as understood in this disclosure. The computer 22 includes a processor 28, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, 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).

[0319] 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, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the microphone 238, speaker 240, camera 42, and communication I / F 44 are also connected to the bus 52.

[0320] Microphone 238 receives instructions from user 20 by receiving sounds emitted by user 20. Microphone 238 captures sounds emitted by user 20 and converts the captured sounds into sound data, which is then output to processor 46. Speaker 240 outputs sound according to instructions from processor 46.

[0321] Camera 42 is a small digital camera equipped with an optical system such as a lens, aperture and shutter, and imaging elements such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, to capture images of the user 20's surroundings (e.g., the field of view defined by an angle equivalent to the field of vision of an average healthy person).

[0322] Communication I / F44 is connected to network 54. Communication I / F44 and 26 are responsible for the transmission and reception of various information between processor 46 and processor 28 via network 54. The transmission and reception of various information between processor 46 and processor 28 using communication I / F44 and 26 is performed in a secure state.

[0323] Figure 4 This illustrates an example of the main functions of the data processing device 12 and the smart glasses 214. For example... Figure 4 As shown, in the data processing device 12, specific processing is performed by the processor 28. The specific processing program 56 is stored in the memory 32.

[0324] The specific processing program 56 is an example of a "program" involved in the technology of this disclosure. The processor 28 reads the specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. Specific processing is implemented by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0325] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290. The specific processing unit 290 can use the emotion-specific model 59 to infer the user's emotions and perform specific processing based on the user's emotions. In the emotion inference function (emotion-specific function) using the emotion-specific model 59, various inferences and predictions related to the user's emotions are performed, including inferences and predictions of the user's emotions, but this is not limited to this example. Furthermore, emotion inference and prediction may also include, for example, emotion analysis (parsing).

[0326] In the smart glasses 214, the processor 46 performs reception and output processing. The memory 50 stores the reception and output program 60. The processor 46 reads the reception and output program 60 from the memory 50 and executes the read reception and output program 60 on the RAM 48. The reception and output processing is implemented by the processor 46 operating as a control unit 46A according to the reception and output program 60 executed on the RAM 48. Furthermore, the smart glasses 214 has the same data generation model and emotion-specific model as the data generation model 58 and the emotion-specific model 59, and these models can also be used to perform the same processing as the specific processing unit 290.

[0327] Next, the specific processing of the specific processing unit 290 of the data processing device 12 will be described. Each part of the system described below is implemented by the data processing device 12 and the smart glasses 214. 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".

[0328] Example 1

[0329] The process is the same as that of the specific process described in Embodiment 1 in the first embodiment above, so the description is omitted.

[0330] Application Example 1

[0331] The process is the same as that in the specific processing described in Application Example 1 of the first embodiment above, so the description is omitted.

[0332] Example 2

[0333] The process is the same as that of the specific process in Embodiment 2 described in the first embodiment above, so the description is omitted.

[0334] Application Example 2

[0335] The process is the same as that in the specific processing described in Application Example 2 of the first embodiment above, so the description is omitted.

[0336] The specific processing unit 290 sends the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A outputs the result of the specific processing to the speaker 240. The microphone 238 acquires sound input representing the user's input regarding the result of the specific processing. The control unit 46A sends the sound data representing the user's 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 sound data.

[0337] Data generation model 58 is a so-called generative AI (Artificial Intelligence). Examples of data generation models 58 include ChatGPT (registered trademark) (accessible via the internet (URL: https: / / openai.com / blog / chatgpt)). Data generation model 58 is obtained through deep learning on a neural network. Input to data generation model 58 are prompt words containing instructions, and inference data such as sound data representing sound, text data representing text, and image data representing images (e.g., still image data or animation data). Data generation model 58 infers from the input inference data based on the instructions represented by the prompt words and outputs the inference result in one or more data forms, such as sound data, text data, and image data. Data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or induction. The specific processing unit 290 performs the aforementioned specific processing while using data generation model 58. The data generation model 58 can also be a model finely tuned to output inference results from prompts that do not contain instructions. In this case, the data generation model 58 can output inference results based on prompts that do not contain instructions. The data processing apparatus 12, etc., includes various data generation models 58, including AI other than the generation AI. AI other than the generation AI can be, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or Naive Bayes, and can perform various processes, but is not limited to this example. Furthermore, the AI ​​can also be an AI agent. Furthermore, when the processing of the above-mentioned parts is performed by AI, the processing can be partially or entirely performed by AI, but is not limited to this example. Furthermore, the processing performed by the AI ​​including the generation AI can be replaced by processing in the rule base, and the processing in the rule base can also be replaced by processing performed by the AI ​​including the generation AI.

[0338] Furthermore, the processing of the aforementioned data processing system 10 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it can also be performed by both the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Additionally, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or external devices, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or external devices.

[0339] For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit uses the camera 42 or communication I / F 44 of the smart glasses 214 to acquire step data, which is then processed by the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes the data from the collection unit and the acquisition unit. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12, which uses a generation AI to generate a menu. For example, the serving unit is implemented by the speaker 240 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, which provides the generated menu to the user. The correspondence between each unit and the device or control unit is not limited to the above examples and various changes can be made.

[0340] In the above embodiments, examples of specific processing by the data processing device 12 are given, but the technology disclosed herein is not limited to this, and specific processing may also be performed by the smart glasses 214.

[0341] Third Implementation Method

[0342] Figure 5 An example of the configuration of the data processing system 310 according to the third embodiment is shown.

[0343] like Figure 5 As shown, the data processing system 310 includes a data processing device 12 and a head-mounted terminal 314. A server can be cited as an example of the data processing device 12.

[0344] The data processing apparatus 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" as understood in this disclosure. The computer 22 includes a processor 28, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, 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).

[0345] The head-mounted 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, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the microphone 238, speaker 240, camera 42, display 343, and communication I / F 44 are also connected to the bus 52.

[0346] Microphone 238 receives instructions from user 20 by receiving sounds emitted by user 20. Microphone 238 captures sounds emitted by user 20 and converts the captured sounds into sound data, which is then output to processor 46. Speaker 240 outputs sound according to instructions from processor 46.

[0347] Camera 42 is a small digital camera equipped with an optical system such as a lens, aperture and shutter, and imaging elements such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, to capture images of the user 20's surroundings (e.g., the field of view defined by an angle equivalent to the field of vision of an average healthy person).

[0348] Communication I / F44 is connected to network 54. Communication I / F44 and 26 are responsible for the transmission and reception of various information between processor 46 and processor 28 via network 54. The transmission and reception of various information between processor 46 and processor 28 using communication I / F44 and 26 is performed in a secure state.

[0349] Figure 6 This illustrates an example of the main functions of the data processing device 12 and the head-mounted terminal 314. For example... Figure 6 As shown, in the data processing device 12, specific processing is performed by the processor 28. The specific processing program 56 is stored in the memory 32.

[0350] The specific processing program 56 is an example of a "program" involved in the technology of this disclosure. The processor 28 reads the specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. Specific processing is implemented by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0351] The memory 32 stores the data generation model 58 and the emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290.

[0352] In the head-mounted terminal 314, the processor 46 performs the acceptance / output processing. The memory 50 stores the acceptance / output program 60. The processor 46 reads the acceptance / output program 60 from the memory 50 and executes the read acceptance / output program 60 on the RAM 48. The acceptance / output processing is implemented by the processor 46 operating as a control unit 46A according to the acceptance / output program 60 executed on the RAM 48.

[0353] Next, the specific processing of the specific processing unit 290 of the data processing device 12 will be described. Each part of the system described below is implemented by the data processing device 12 and the head-mounted terminal 314. In the following description, the data processing device 12 will be referred to as the "server" and the head-mounted terminal 314 will be referred to as the "terminal".

[0354] Example 1

[0355] The process is the same as that of the specific process described in Embodiment 1 in the first embodiment above, so the description is omitted.

[0356] Application Example 1

[0357] The process is the same as that in the specific processing described in Application Example 1 of the first embodiment above, so the description is omitted.

[0358] Example 2

[0359] The process is the same as that of the specific process in Embodiment 2 described in the first embodiment above, so the description is omitted.

[0360] Application Example 2

[0361] The process is the same as that in the specific processing described in Application Example 2 of the first embodiment above, so the description is omitted.

[0362] The specific processing unit 290 sends the result of the specific processing to the head-mounted terminal 314. In the head-mounted terminal 314, the control unit 46A outputs the result of the specific processing to the speaker 240 and the display 343. The microphone 238 acquires sound input representing the user's input regarding the result of the specific processing. The control unit 46A sends the sound data representing the user's 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 sound data.

[0363] Data generation model 58 is a so-called generative AI (Artificial Intelligence). Examples of data generation models 58 include ChatGPT (registered trademark) (accessible via the internet (URL: https: / / openai.com / blog / chatgpt)). Data generation model 58 is obtained through deep learning on a neural network. Input to data generation model 58 includes prompt words containing instructions, and inference data such as sound data representing sound, text data representing text, and image data representing images (e.g., still image data or animation data). Data generation model 58 infers the input inference data based on the instructions represented by the prompt words and outputs the inference result in one or more data forms such as sound data, text data, and image data. Data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or induction. The specific processing unit 290 performs the aforementioned specific processing while using data generation model 58. The data generation model 58 can also be a model finely tuned to output inference results from prompts that do not contain instructions. In this case, the data generation model 58 can output inference results based on prompts that do not contain instructions. The data processing apparatus 12, etc., includes various data generation models 58, including AI other than the generation AI. AI other than the generation AI can be, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or Naive Bayes, and can perform various processes, but is not limited to this example. Furthermore, the AI ​​can also be an AI agent. Furthermore, when the processing of the above-mentioned parts is performed by AI, the processing can be partially or entirely performed by AI, but is not limited to this example. Furthermore, the processing performed by the AI ​​including the generation AI can be replaced by processing in the rule base, and the processing in the rule base can also be replaced by processing performed by the AI ​​including the generation AI.

[0364] Furthermore, the processing of the aforementioned data processing system 10 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the head-mounted terminal 314, but it can also be performed by both the specific processing unit 290 of the data processing device 12 and the control unit 46A of the head-mounted terminal 314. Additionally, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the head-mounted terminal 314 or external devices, and the head-mounted terminal 314 acquires or collects information required for processing from the data processing device 12 or external devices.

[0365] For example, the collection unit is implemented by the control unit 46A of the head-mounted terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit uses the camera 42 or communication I / F 44 of the head-mounted terminal 314 to acquire step data, which is then processed by the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 to analyze the data from the collection unit and the acquisition unit. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12 to generate a menu using a generation AI. For example, the serving unit is implemented by the speaker 240 and display 343 of the head-mounted terminal 314 or the specific processing unit 290 of the data processing device 12 to provide the generated menu to the user. The correspondence between each unit and the device or control unit is not limited to the above examples and various changes can be made.

[0366] In the above embodiments, examples of specific processing by the data processing device 12 are given, but the technology disclosed herein is not limited to this, and specific processing may also be performed by the head-mounted terminal 314.

[0367] Fourth Implementation Method

[0368] Figure 7 An example of the configuration of the data processing system 410 according to the fourth embodiment is shown.

[0369] like Figure 7 As shown, the data processing system 410 includes a data processing device 12 and a robot 414. A server can be cited as an example of the data processing device 12.

[0370] The data processing apparatus 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" as understood in this disclosure. The computer 22 includes a processor 28, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, 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).

[0371] Robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the microphone 238, speaker 240, camera 42, controlled object 443, and communication I / F 44 are also connected to the bus 52.

[0372] Microphone 238 receives instructions from user 20 by receiving sounds emitted by user 20. Microphone 238 captures sounds emitted by user 20 and converts the captured sounds into sound data, which is then output to processor 46. Speaker 240 outputs sound according to instructions from processor 46.

[0373] Camera 42 is a small digital camera equipped with an optical system such as a lens, aperture and shutter, and imaging elements such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, to photograph the area around robot 414 (e.g., the field of view defined by a perspective equivalent to the field of vision of an average healthy person).

[0374] Communication I / F44 is connected to network 54. Communication I / F44 and 26 are responsible for the transmission and reception of various information between processor 46 and processor 28 via network 54. The transmission and reception of various information between processor 46 and processor 28 using communication I / F44 and 26 is performed in a secure state.

[0375] The controlled object 443 includes a display device, LEDs (light-emitting diodes) for the eyes, and motors for driving the arms, hands, and feet. The posture or movement of the robot 414 is controlled by controlling the motors in the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. In addition, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0376] Figure 8 This illustrates an example of the main functions of the data processing device 12 and the robot 414. For example... Figure 8 As shown, in the data processing device 12, specific processing is performed by the processor 28. The specific processing program 56 is stored in the memory 32.

[0377] The specific processing program 56 is an example of a "program" involved in the technology of this disclosure. The processor 28 reads the specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. Specific processing is implemented by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0378] The memory 32 stores the data generation model 58 and the emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290.

[0379] In robot 414, the processor 46 performs the acceptance and output processing. The memory 50 stores the acceptance and output program 60. The processor 46 reads the acceptance and output program 60 from the memory 50 and executes the read acceptance and output program 60 on RAM 48. The acceptance and output processing is implemented by the processor 46 acting as the control unit 46A according to the acceptance and output program 60 executed on RAM 48.

[0380] Next, the specific processing of the specific processing unit 290 of the data processing device 12 will be described. Each part of the system described below is implemented by the data processing device 12 and the robot 414. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal".

[0381] Example 1

[0382] The process is the same as that of the specific process described in Embodiment 1 in the first embodiment above, so the description is omitted.

[0383] Application Example 1

[0384] The process is the same as that in the specific processing described in Application Example 1 of the first embodiment above, so the description is omitted.

[0385] Example 2

[0386] The process is the same as that of the specific process in Embodiment 2 described in the first embodiment above, so the description is omitted.

[0387] Application Example 2

[0388] The process is the same as that in the specific processing described in Application Example 2 of the first embodiment above, so the description is omitted.

[0389] The specific processing unit 290 sends the result of the specific processing to the robot 414. In the robot 414, the control unit 46A outputs the result of the specific processing to the speaker 240 and the controlled object 443. The microphone 238 acquires sound input representing the result of the specific processing. The control unit 46A sends the sound data representing 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 sound data.

[0390] Data generation model 58 is a so-called generative AI (Artificial Intelligence). Examples of data generation models 58 include ChatGPT (registered trademark) (accessible via the internet (URL: https: / / openai.com / blog / chatgpt)). Data generation model 58 is obtained through deep learning on a neural network. Input to data generation model 58 are prompt words containing instructions, and inference data such as sound data representing sound, text data representing text, and image data representing images (e.g., still image data or animation data). Data generation model 58 infers from the input inference data based on the instructions represented by the prompt words and outputs the inference result in one or more data forms, such as sound data, text data, and image data. Data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or induction. The specific processing unit 290 performs the aforementioned specific processing while using data generation model 58. The data generation model 58 can also be a model finely tuned to output inference results from prompts that do not contain instructions. In this case, the data generation model 58 can output inference results based on prompts that do not contain instructions. The data processing apparatus 12, etc., includes various data generation models 58, including AI other than the generation AI. AI other than the generation AI can be, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or Naive Bayes, and can perform various processes, but is not limited to this example. Furthermore, the AI ​​can also be an AI agent. Furthermore, when the processing of the above-mentioned parts is performed by AI, the processing can be partially or entirely performed by AI, but is not limited to this example. Furthermore, the processing performed by the AI ​​including the generation AI can be replaced by processing in the rule base, and the processing in the rule base can also be replaced by processing performed by the AI ​​including the generation AI.

[0391] Furthermore, the processing of the aforementioned data processing system 10 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it can also be performed by both the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Additionally, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or external devices, and the robot 414 acquires or collects information required for processing from the data processing device 12 or external devices.

[0392] For example, the collection unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit uses the camera 42 or communication I / F 44 of the robot 414 to acquire step data, which is then processed by the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes the data from the collection unit and the acquisition unit. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12, which uses a generation AI to generate a menu. For example, the serving unit is implemented by the speaker 240 of the robot 414 and the control object 443 or the specific processing unit 290 of the data processing device 12, which provides the generated menu to the user. The correspondence between each unit and the device or control unit is not limited to the above examples and various changes can be made.

[0393] In the above embodiments, examples of specific processing by the data processing device 12 are given, but the technology disclosed herein is not limited to this, and specific processing may also be performed by the robot 414.

[0394] Furthermore, the emotion-specific model 59, acting as an emotion engine, can determine a user's emotion based on a specific mapping. Specifically, the emotion-specific model 59 can determine a user's emotion based on an emotion graph that serves as a specific mapping (see [reference]). Figure 9 The system determines the user's emotions. Furthermore, the emotion-specific model 59 can similarly determine the robot's emotions, and the specific processing unit 290 performs specific processing based on the robot's emotions.

[0395] Figure 9 This is a diagram representing an emotion map 400 that maps multiple emotions. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotion is. On the outer side of the concentric circles, emotions representing states or behaviors arising from mood are arranged. Emotions are concepts that include feelings and mental states. Emotions generated by reactions occurring in the brain are arranged roughly to the left of the concentric circles. Emotions derived from situational judgments are arranged roughly to the right of the concentric circles. Emotions generated by reactions occurring in the brain and derived from situational judgments are arranged roughly above and below the concentric circles. Furthermore, "pleasant" emotions are arranged above the concentric circles, and "unpleasant" emotions are arranged below them. Thus, in the emotion map 400, multiple emotions are mapped based on the structure that generates emotions, and emotions that are likely to occur simultaneously are mapped close to each other.

[0396] These emotions are distributed at the three o'clock position of the emotion map 400, typically fluctuating between peace and anxiety. In the right half of the emotion map 400, situational awareness dominates over internal sensation, thus resulting in an impression of calm.

[0397] The inner side of the emotion map 400 represents the inner state, while the outer side represents behavior. Therefore, the further outward you are from the emotion map 400, the more visible the emotion becomes (manifested in behavior).

[0398] Here, human emotions are based on various balances such as posture and blood sugar levels. When these balances deviate from an ideal state, it indicates an unpleasant state; when they approach the ideal state, it indicates a pleasant state. Emotions in robots, cars, motorcycles, etc., can also be created in the following way: based on various balances such as posture and battery level, when these balances deviate from an ideal state, it indicates an unpleasant state; when they approach the ideal state, it indicates a pleasant state. Emotion maps, for example, can be based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Brain Physiological Signal Analysis Systems for Emotions, Tokushima University, Doctoral Dissertation: https: / /

[0399] The map is generated using the index / / ci.nii.ac.jp / naid / 500000375379. In the left half of the emotion map, emotions belonging to the "response" region, where sensation is dominant, are arranged. Furthermore, in the right half of the emotion map, emotions belonging to the "situation" region, where situational cognition is dominant, are arranged.

[0400] In the emotion map, two types of emotions that promote learning are defined. One is a negative emotion on the situational side, in the middle or peripheral region of "repentance" or "reflection." This occurs when the robot experiences negative emotions such as "I don't want to experience this feeling again" or "I don't want to be blamed again." The other is a positive emotion on the response side, near the "desire" region. This occurs when there are positive feelings such as "wanting more" or "wanting to know more."

[0401] The emotion-specific model 59 inputs user input into a pre-trained neural network to obtain emotion values ​​representing each emotion shown in the emotion map 400, thereby determining the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network... Figure 10 As shown in the sentiment graph 900, it was trained in a way that sentiments that are configured close to each other have similar values. Figure 10 The text shows examples of emotions such as "peace of mind", "stability", and "reassurance" that have similar emotion values.

[0402] The above description focuses on the functions of the data processing device 12, but the system of this disclosure is not necessarily installed on a server. The system of this disclosure can also be installed as a general information processing system. This disclosure can also be installed, for example, as a software program running on a personal computer, an application running on a smartphone, etc. The method of this disclosure can also be provided to users in the form of SaaS (Software as a Service).

[0403] In the above embodiments, an example of a specific process being performed by a single computer 22 is given. However, the technology disclosed herein is not limited to this, and the specific process can also be distributed among multiple computers, including computer 22. For example, the data generation model 58 can be located on an external device of the data processing apparatus 12, where data is generated based on the input data.

[0404] In the above embodiments, examples of storing a specific processing program 56 in the memory 32 have been described, but the technology disclosed herein is not limited thereto. For example, the specific processing program 56 may also 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 into the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0405] Alternatively, a specific processing program 56 may be pre-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 according to the requirements of the data processing device 12.

[0406] In addition, it is not necessary to store all the specific processing program 56 in the storage device such as the server connected to the data processing device 12 via the network 54 or in the memory 32; a portion of the specific processing program 56 may be stored in advance.

[0407] As hardware resources for performing specific processes, various processors, as shown below, can be used. For example, a CPU can be listed as a processor, which functions as a general-purpose processor that performs specific processes by executing software, i.e., a program. Furthermore, processors can be listed as special-purpose circuits such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application-Specific Integrated Circuits), which are processors with circuitry specifically designed to perform specific processes. Each processor has built-in or connected memory, and each processor executes specific processes using that memory.

[0408] The hardware resources for performing a specific process can consist of one of these various processors, or a combination of two or more processors of the same or different types (e.g., a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resources for performing a specific process can be a single processor.

[0409] As an example of a single processor, there are two approaches: First, a processor is composed of a combination of one or more CPUs and software, which functions as a hardware resource to perform a specific process; second, as represented by a SoC (System-on-a-chip), a processor is used to implement the functionality of the entire system, which includes multiple hardware resources for performing a specific process, using a single IC (Integrated Circuit) chip. In this way, the specific process is implemented by using one or more of the aforementioned processors as hardware resources.

[0410] Furthermore, the hardware architecture of these various processors, more specifically, can utilize circuits that combine semiconductor elements and other circuit components. Moreover, the specific process described above is just one example. Therefore, without departing from the main point, unnecessary steps can certainly be deleted, new steps added, or the processing order changed.

[0411] The descriptions and illustrations above are detailed explanations of a portion of the technology disclosed herein, and are merely one example of the technology disclosed herein. For example, the above descriptions of the structure, function, effect, and results are just one example of the structure, function, effect, and results of a portion of the technology disclosed herein. Therefore, without departing from the spirit of the technology disclosed herein, unnecessary parts may be deleted, new elements added, or replacements may be made to the descriptions and illustrations above. Furthermore, to avoid confusion and facilitate understanding of a portion of the technology disclosed herein, explanations of common technical knowledge that do not require special explanation under the premise of being able to implement the technology disclosed herein have been omitted from the descriptions and illustrations above.

[0412] All documents, patent applications and technical specifications set forth in this specification are incorporated herein by reference to the same extent that each document, patent application and technical specification is specifically and individually described therein and referenced by reference.

[0413] In addition, the following notes are provided in response to the above explanation.

[0414] Example 1

[0415] (Note 1)

[0416] An information processing system includes: an information acquisition device for obtaining an information retrieval string from a user; an information collection device for automatically acquiring information related to the information retrieval string from multiple information sources on an information communication network; an information evaluation device for evaluating the credibility of the acquired information and automatically excluding information that is inconsistent with the facts or has low credibility; an information processing device for organizing the evaluated information according to the acquisition time and content classification criteria; a generation processing device for providing the organized information as input to a generation processing unit and generating data for presentation using generative artificial intelligence based on prompts containing content structure and display layout design guidelines; and an information providing device for sending the generated data for presentation to a user terminal via a communication line for display.

[0417] (Note 2)

[0418] The information processing system according to Appendix 1 further includes a display device for receiving results generated by the generation processing mechanism and visually displaying the data for presentation.

[0419] (Note 3)

[0420] The information processing system according to Appendix 1 further includes an input device for inputting an information retrieval string and sending it to the information acquisition device.

[0421] Application Example 1

[0422] (Note 1)

[0423] An information processing system includes: a device for receiving search criteria from a user via an input device; a device for automatically acquiring information related to the search criteria from multiple information collections in a communication network via an acquisition device; a device for evaluating the credibility of the acquired information and automatically excluding false or low-credibility information via an evaluation device; a device for classifying and structuring the evaluated information according to time sequence and attribute categories via an organization device; a device for shaping structured information data into a format suitable for the output characteristics of the user device via a shaping device based on prompt information containing instruction statements for a generative artificial intelligence model; a device for parsing voice information input and generating search criteria via a parsing device; a device for converting structured information data into voice data for an audio output device via an information synthesis device; a device for outputting the shaped information or voice data to the user's information display device via a providing device; and (optionally) a device for adaptively changing the order of information prompts according to the state of the information user via a recognition device based on an artificial intelligence model for inferring user emotions.

[0424] (Note 2)

[0425] The information processing system according to Appendix 1 includes a display device that receives structured and shaped information or voice data and outputs it to a visual and audio output device.

[0426] (Note 3)

[0427] The information processing system according to Appendix 1 includes an input device that inputs and sends search criteria by voice or text information.

[0428] Example 2

[0429] (Note 1)

[0430] An information processing system includes: an information acquisition device for receiving retrieval information from a user; an automatic information collection device for collecting documents related to the retrieval information from information sources on a communication network; an information evaluation and elimination device for excluding content with low credibility and erroneous information based on the credibility of the collected documents; an information classification device for classifying the evaluated and eliminated documents according to time sequence and attributes; an information generation and structuring device for converting documents into structured display data using a generative artificial intelligence model based on classification results and user characteristics; an emotion recognition and priority adjustment device for parsing user emotional information and dynamically adjusting the information display priority accordingly; and a data transmission device for outputting structured display data to a user's operating terminal in an optimized format.

[0431] (Note 2)

[0432] The information processing system according to Appendix 1 further includes a display device for receiving classified and structured information and visually displaying it based on multiple attribute partitions.

[0433] (Note 3)

[0434] The information processing system according to Appendix 1 also includes an operating device for inputting and sending retrieved information.

[0435] Application Example 2

[0436] (Note 1)

[0437] An information processing system includes: a device for receiving information retrieval terms from an external information processing device; an information collection and processing device for automatically acquiring information sets related to the information retrieval terms from multiple information sources on a wide area communication network; an information evaluation device for assessing the credibility and authenticity of the acquired information sets and excluding inaccurate information; an information structuring device for classifying and sorting the evaluated information sets according to time information and content classification attributes; an artificial intelligence model device for automatically generating and editing the structured information sets into information page format; a sentiment analysis device for parsing the user state of the external information processing device and dynamically changing the display order and priority of the information sets based on the user's sentiment indicators; a sending device for sending the information pages generated by the artificial intelligence model to the external information processing device; and a display device for displaying the information pages to the user on the external information processing device.

[0438] (Note 2)

[0439] According to the information processing system described in Appendix 1, the emotion analysis device includes a function for analyzing multiple operation histories of users to determine their emotional state, and automatically selecting priority display content related to the information set based on the emotional state.

[0440] (Note 3)

[0441] According to the information processing system described in Appendix 1, the artificial intelligence model device includes the function of automatically summarizing and editing the page layout of the information set using prompt statements based on content classification attributes.

Claims

1. An information processing system, characterized in that, include: A device for receiving search keywords input by a user; A crawler engine device for collecting information related to the search keywords from online information sources; A device for filtering collected information and eliminating false information; A device for classifying filtered information according to time sequence and category; A generative artificial intelligence device used to generate a portal website from organized information. A device for providing the information to a user terminal.

2. The information processing system according to claim 1, characterized in that, Also includes: A device for receiving filtered information and displaying it in the form of a portal website.

3. The information processing system according to claim 1, characterized in that, Also includes: A device for inputting search keywords and sending the keywords.

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