system

Abstract

We provide the system. [Solution] A means of receiving a specific information category requested by the user, Means for collecting relevant digital data from online sources, A means of inputting collected digital data into a generative artificial intelligence model, extracting important information, and generating a summary, A means for ranking the generated summaries and selecting them based on relevance, A means of sending the selected summary to the user, A visualization method that dynamically displays selected summaries and makes them accessible through a user interface, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, with the development of information technology, the amount of information available online has increased rapidly. For information collectors, including busy business people, it has become difficult to efficiently grasp the necessary information and make appropriate decisions. The situation of information overload poses problems such as the risk of missing important information and the need to spend a great deal of time on information screening. The present invention aims to effectively perform information screening and summarization, enabling users to obtain the necessary information in a short time.

Means for Solving the Problems

[0005] The present invention first provides a means for receiving requests from users regarding specific information categories, and in response to those requests, collecting relevant digital information from online information sources. Next, the collected digital information is input into a generative artificial intelligence model to extract important information and generate summaries. Furthermore, the generated summaries are ranked and sorted based on relevance. Finally, the sorted summaries are sent to the user, and feedback from the user is received and used to improve the model's performance, thereby providing a means to deliver more accurate information to users.

[0006] A "user" is an entity that utilizes a system and makes requests for information or provides feedback.

[0007] An "information category" is a classification metric for news and digital information that users specify based on a particular field or topic.

[0008] "Online information sources" refer to media from which digital information can be obtained via the internet, such as news sites, RSS feeds, or information provision APIs.

[0009] "Digital information" refers to news articles, reports, and other informational content presented in electronic format.

[0010] A "generative artificial intelligence model" is an algorithm that learns from large amounts of data and has the ability to generate summaries or new texts in response to given input.

[0011] A "summary" is a short, concise text or document that extracts only the main information.

[0012] "Ranking" is the process of evaluating the importance and relevance of the generated summaries and prioritizing them.

[0013] "Selection" is the process of choosing the most useful and relevant summaries from among those that have been evaluated.

[0014] A "link" is a connection means used by a user to access additional information or the original article from a selected summary.

[0015] "Feedback" is an evaluation or opinion given by a user to the system regarding the quality and content of a summary.

Brief Description of the Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Embodiments for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disk (e.g., hard disk), or magnetic tape, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention relates to a news summary agent aimed at enabling information gatherers and business professionals to quickly obtain the information they need. The system solves the problem of information overload by collecting appropriate digital information from online sources in response to information requests from users and summarizing it using a generative artificial intelligence model.

[0038] The user first opens the News Summary Assistant app on their device. They select information categories and topics of interest and submit a request. Based on the user's request, the server then consults reliable sources on the internet (such as news sites and APIs) and collects relevant digital information.

[0039] The collected digital information is sent to a generative artificial intelligence model running on a server. This AI model analyzes numerous news articles and documents, extracting key facts, figures, and conclusions, and creating summaries in an easy-to-understand format. The generated summaries undergo a ranking process to select those deemed most useful to the user.

[0040] The selected summaries are sent from the server to the user's device, where the user can view them. It's also possible to submit feedback on the quality and content of the summaries through the app, and this feedback is used to improve the generative AI model.

[0041] For example, if a user requests news about "the latest technology trends," the server crawls multiple technology news sites. The AI ​​model summarizes key information from the articles, such as "a certain company has announced new AI technology," and the user can receive this information along with a link to the full article. This process allows users to grasp important information in a short amount of time.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user launches the News Summary Assistant application on their device, selects information categories and topics of interest (e.g., "Financial Market Trends"), and creates a request.

[0045] Step 2:

[0046] The user's request is sent to the server. The server queries multiple online sources on the internet to collect news articles related to the selected topic. In doing so, it uses APIs and scraping techniques to obtain the latest digital information.

[0047] Step 3:

[0048] The server inputs the collected news article data into a generative AI model. The AI ​​model analyzes the text and extracts key information from the articles. For example, it extracts major facts, announcements, and statistics mentioned in the articles and generates a summary.

[0049] Step 4:

[0050] The server ranks the generated summaries based on their importance and relevance. From these ranked summaries, the most relevant ones are selected for the user. This results in the user receiving more focused information.

[0051] Step 5:

[0052] The selected summaries are sent from the server to the user's terminal. Users can review the summaries and access more in-depth content by clicking on links to detailed articles or information that interest them.

[0053] Step 6:

[0054] Users provide feedback on the quality and relevance of the summaries they view within the app. This feedback is collected by a server and used to personalize and improve the accuracy of the generative AI model, thereby improving the user experience in the future.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] In today's world, a vast amount of information exists online, making it difficult for information users to quickly find information that is useful to them. This invention aims to alleviate the burden of information overload by efficiently collecting the information that information users seek, quickly summarizing its important elements, and providing them to them.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes means for receiving information classifications requested by information users, means for collecting relevant information from online information sources, and means for inputting the collected information into a generative learning model, extracting important information, and generating a summary. This makes it possible to quickly and efficiently summarize and provide the information that information users need.

[0060] "Information user" refers to an individual or group that seeks and intends to obtain specific information.

[0061] "Information classification" refers to specific categories or topics that information users are particularly interested in, and information gathering is carried out based on these selections.

[0062] "Online information sources" refer to internet resources that provide information, such as news sites, databases, and APIs that are publicly available on the web.

[0063] A "generative learning model" refers to an artificial intelligence model that uses pre-trained data to perform summarization and content generation in response to new inputs.

[0064] "Important information" refers to facts, figures, and conclusions among the collected information that are deemed particularly useful and valuable to the information user.

[0065] "Generating summaries" refers to the process of creating condensed information from data analyzed by a generative learning model, making it easily understandable to information users.

[0066] "Ranking and selecting based on value" refers to evaluating the generated summaries and prioritizing and selecting those that are considered most relevant and beneficial to the information user.

[0067] "Displaying connections to information users" refers to providing information users with links or actions that offer more detailed information or additional materials related to the summary.

[0068] This invention is a news summary system that efficiently collects and summarizes information needed by information users. The main components of the system include a "server" that collects and processes information, a "terminal" that requests information, and a "user" that inputs information.

[0069] The user launches the News Summary Assistant app from their device, selects the information categories they are interested in, and sends a request. The device then acts as a proxy, sending the request to the server based on the user's selection.

[0070] The server collects relevant information from online information sources in response to incoming requests. These information sources include news sites, databases, and APIs on the internet. The server accesses these sources to obtain the necessary digital information in real time.

[0071] Next, the server inputs the collected information into a generative learning model, which extracts key information and creates a summary. This AI model uses natural language processing algorithms. The model tokenizes the information and evaluates the importance of sentences to generate the most useful summary for the user.

[0072] The generated summaries are further evaluated and ranked on the server. This selects the summary that best matches the information user's requirements and sends it to the user via their terminal. The user can view the information provided on their terminal and, if necessary, follow connections to obtain more detailed information.

[0073] For example, if a user requests a summary of "international news," the server retrieves relevant information from multiple international news sites, and a generative learning model summarizes key points such as "agreements on new international agreements in major countries." This summary helps users accurately grasp the information they are looking for.

[0074] A concrete example of a prompt message would be a request such as, "Please provide a summary of the latest developments regarding international news topics." This allows information users to obtain the necessary information in a short amount of time.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The user launches the News Summary Assistant app through their device. They select the information categories and topics of interest, enter their request into the input form, and then press the submit button to send the request to the server. At this point, the input is the topic specified by the user, and the output is the specific request sent to the server.

[0078] Step 2:

[0079] The server accesses online information sources based on information classification requests received from users. These information sources include news sites and public APIs. Here, the server queries the specified information sources and collects the relevant digital information. The input is a search query based on the request, and the output is raw data collected in the form of news articles or datasets.

[0080] Step 3:

[0081] The server begins the process of inputting the collected digital information into a generative learning model. The model utilizes natural language processing techniques to tokenize the information, extract key points, and generate summaries. Specifically, the model analyzes the information received as input, highlights elements deemed important to the user, and summarizes them. The input for this step is the raw data collected in the previous stage, and the generated summary is the output.

[0082] Step 4:

[0083] The generated summaries are ranked on the server. The server evaluates the generated summaries and selects those containing the key points that information users are most likely to be interested in. The selection is based on the relevance and importance of the information. The input to the ranking process is summaries from a generative learning model, and the final selected summaries are obtained as output.

[0084] Step 5:

[0085] The server sends the selected summaries to the terminal. The transmission process includes links to related details along with the final selected summaries. The end user can view the summaries on the terminal and access further information via the links as needed. The input is the selected summaries, and the output is the summary information and links that the user receives on the terminal.

[0086] Step 6:

[0087] Users evaluate summaries and send feedback to the server via the app through their device. This feedback concerns the quality and relevance of the summary. The server collects this feedback and uses it to improve future generative learning models. The input is user feedback, and the output is evaluation information fed into the improvement cycle.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] In recent years, the rapid advancement of information technology has exposed urban administrators and residents to vast amounts of information, requiring them to quickly extract important information and utilize it efficiently. However, in such an information-overloaded environment, selecting and summarizing appropriate information is not easy, which can hinder efficient urban management and the provision of public services. Therefore, a system is needed that can summarize important information in real time and visualize it appropriately.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes means for receiving specific information categories requested by a user, means for collecting the relevant digital data from online information sources, and means for inputting the collected digital data into a generative artificial intelligence model to extract important information and generate a summary. This makes it possible to quickly obtain important information needed by city administrators and citizens, thereby improving the efficient management of cities and the provision of citizen services.

[0093] A "user" refers to an individual or organization that seeks to obtain information from a system, and is responsible for making requests for specific information.

[0094] An "information category" is a specific type of information that a system collects and categorizes for use with users.

[0095] "Digital data" refers to information expressed in electronic form obtained from online sources.

[0096] A "generative artificial intelligence model" is an advanced algorithm used to analyze vast amounts of information data and generate summaries based on that analysis.

[0097] A "summary" is an abstract of information that extracts the most important elements from collected information and presents them in a concise format.

[0098] "Visualization means" refers to an interface or technology that displays information in a way that allows users to easily understand and view it.

[0099] "Communication means" are technical means for sending and receiving information between a user and a system, and play a role in quickly transmitting specific information.

[0100] A "hyperlink" is an online reference point that allows users to access additional or more detailed information related to the topic.

[0101] A "smart device" is a portable electronic device equipped with internet connectivity that enables real-time notifications and information display.

[0102] This embodiment of the invention relates to a news summary system specifically designed for urban information management. Users can use portable devices such as smartphones or smart glasses to select specific information categories (e.g., transportation, public services, event information) and request information.

[0103] The server collects digital data from online sources via the internet and inputs this data into a generative artificial intelligence model in the cloud, such as OpenAI®'s GPT. This extracts important information from the digital data and generates concise summaries. The generated summaries are then ranked and sorted based on relevance.

[0104] Selected summaries are sent to the user's terminal and displayed through a dedicated application. This application visualizes the summaries in an easy-to-understand format and provides the user with relevant hyperlinks. This allows the user to access detailed information as needed.

[0105] For example, if a user requests "Today's city events information," the server collects relevant information and uses a generative artificial intelligence model to generate a summary such as "A music festival will be held in the city center from 3 PM," and notifies the user. In this way, the user can quickly obtain the information they need.

[0106] An example of a prompt for a generative AI model is, "Please provide a summary of the latest traffic information for the city." This allows the system to begin the process of collecting and summarizing the appropriate information.

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The user uses a terminal to select a specific information category (e.g., transportation, public services) and submits a request. This input is initial information from the user that triggers processing on the server side.

[0110] Step 2:

[0111] The server collects relevant digital data from online sources on the internet based on the information categories received from the user. This process involves calling APIs from trusted sources to retrieve data. The input is the user's information categories, and the output is the collected raw data.

[0112] Step 3:

[0113] The server inputs the collected digital data into a generative artificial intelligence model. The generative AI model analyzes this data, extracts important information, and generates a summary. Data processing at this stage includes computational processing to organize and concisely summarize key points from a large amount of information. The input is the collected digital data, and the output is the generated summary.

[0114] Step 4:

[0115] The server ranks the generated summaries and sorts them based on relevance. This process uses machine learning algorithms to score importance and select the most useful summaries for the user. The input is the generated summaries, and the output is the sorted summaries.

[0116] Step 5:

[0117] The selected summaries are sent from the server to the terminal and displayed to the user by a dedicated application. The application visualizes the summaries in an easy-to-understand format, making them accessible through a user interface. The input is the selected summaries, and the output is the information displayed for the user to review.

[0118] Step 6:

[0119] Users can submit feedback on the information provided, which is aggregated on the server and used to improve the performance of the generative artificial intelligence model. This process includes analyzing the feedback data and inputting it into the model improvement algorithm. The input is the user's feedback, and the output is the improved AI model.

[0120] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0121] This invention is a news summary agent system incorporating an emotion engine, enabling users to receive more personalized news information. Specifically, it aims to optimize the presentation of information summaries by adjusting them according to the user's current emotional state, and further optimize the provision of useful information.

[0122] First, the user launches the news summary app on their device and selects information categories and topics of interest. The server receives this request and collects relevant digital information from appropriate sources on the internet. The collected information is summarized using a generative artificial intelligence model, and the main points are extracted.

[0123] The key feature of this system lies in the function of its emotion engine. While the user is using the device, the emotion engine analyzes the user's facial expressions, tone of voice, and the resulting emotional data. This data is sent to the server, where the user's emotional state is analyzed. For example, if the emotion engine determines that the user is feeling down, the server will improve the user experience by prioritizing the display of pre-prepared news summaries containing encouragement and positive content.

[0124] The server also provides other relevant perspectives and additional information based on emotional data and the user's interests and requests. For example, if a user expresses interest in "environmental issues" and the emotional engine determines that the user is stressed, the server will present summaries of articles reporting specific solutions or positive developments.

[0125] Finally, users review the summary they receive and provide feedback on its quality and content. The server accumulates this feedback and sentiment data, which is then used to tune the generative AI model, allowing it to continuously provide a more personalized experience. This system enables not only the delivery of information, but also the delivery of valuable information tailored to the user's emotions.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] Users launch the News Summary Assistant application on their device, select a specific information category or topic (e.g., "Healthcare," "Climate Change"), and send a request through the app.

[0129] Step 2:

[0130] The server receives requests from users and collects news articles related to the selected topic from online sources. This is done using news APIs and web scraping techniques to obtain reliable information.

[0131] Step 3:

[0132] The collected news article data is fed into a generative artificial intelligence model on a server. The generative AI model analyzes the article content, extracts important information, and generates a concise summary. This summary includes the main points and conclusions of the article.

[0133] Step 4:

[0134] An emotion engine built into the user's device analyzes the user's emotional state in real time. This process uses the camera and microphone to analyze the user's facial expressions and tone of voice to identify feelings such as joy, sadness, and excitement that the user is currently experiencing.

[0135] Step 5:

[0136] The analysis results from the emotion engine are sent to the server. The server uses the received emotion data to rank the generated summaries and select the summary that best suits the user's emotional state. For example, if the user is determined to be tired, the server will prioritize selecting summaries with less information and more positive content.

[0137] Step 6:

[0138] The server sends the selected summaries to the user's device. The user can view these summaries and, if more detailed information is needed, refer to the original articles using the provided links.

[0139] Step 7:

[0140] Users input feedback on the summary content into their device. This feedback is sent to the server and used, along with sentiment data, to improve the generative AI model. This allows the system to evolve, providing users with more relevant and personalized information.

[0141] (Example 2)

[0142] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0143] Conventional information delivery systems specialize in data collection and summarization from specific information domains, but they have limitations in providing personalized experiences that reflect the emotional state of users. As a result, appropriate information that is not tailored to the specific mental state and context of the information recipient is often not provided, making it difficult to improve user satisfaction.

[0144] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0145] In this invention, the server includes means for receiving a specific information area requested by a user, means for acquiring relevant digital data from information sources on a telecommunications network, and means for adjusting a summary formed based on the user's emotional state. This enables the provision of personalized information according to the user's emotional state, thereby achieving higher satisfaction and an optimized user experience.

[0146] "Information domain" refers to information in a specific field or category that a user requests.

[0147] "Telecommunications network" refers to the internet and other network infrastructure that enables the transmission and reception of information.

[0148] "Digital data" refers to information that is generated or stored electronically, including text, images, audio, and video.

[0149] A "generative artificial intelligence model" refers to an algorithm that generates information based on large amounts of data, extracts knowledge through learning, and creates new summaries and content.

[0150] "Emotional state" refers to a user's temporary psychological emotional state and is classified into categories such as positive, negative, and neutral.

[0151] A "summary" is a way of condensing the main points and knowledge of information and providing them in a short format.

[0152] "Related links" refer to hyperlinks that provide further information or details related to the generated summary.

[0153] This invention is a system for providing personalized information to users. The entire system includes a terminal, a server, a generative artificial intelligence model, and an emotion analysis engine.

[0154] The user launches a news summary app on their device and inputs information about specific areas of interest. This app sends the user's requests to a server. The device has built-in hardware such as a camera and microphone to collect emotional data.

[0155] The server uses telecommunications networks to collect relevant digital data from the internet. Web scraping tools and APIs may be used for this collection. The collected digital data is input into a generative artificial intelligence model to extract key knowledge and form a summary. This AI model leverages machine learning techniques to efficiently generate the main points of the content.

[0156] The device analyzes the user's emotional state in real time using an emotion analysis engine. The device's camera and microphone capture the user's facial expressions and voice tone, and send the data to a server for analysis of the user's psychological state.

[0157] The server uses this sentiment data to refine the summaries and rank them according to relevance. Because the refined summaries are edited to match the user's emotions, the quality of the user experience is improved.

[0158] For example, if a user expresses interest in "environmental issues" and the sentiment analysis engine determines that they are stressed, the server will prioritize summarizing and displaying articles that report positive developments.

[0159] Examples of prompts include, "Generate a summary of environmental articles with positive content based on the user's current emotional state," and "Generate a summary of the latest technology news and adjust the display order considering the user's emotional data."

[0160] In this way, the system goes beyond simply providing information and realizes value-added services that respond to the emotional state of the user.

[0161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0162] Step 1:

[0163] The user launches a news summary app on their device and selects a specific information area. The device receives the user's selected information category as input. The device sends this to the server, initiating a request for information retrieval. The server receives the user's areas of interest as output.

[0164] Step 2:

[0165] The server collects relevant digital data from various sources on the internet via telecommunications networks. It uses web scraping tools and APIs to retrieve data related to specified information categories. The collected data is stored on the server, and the process proceeds to the next step. As output, a collection of digital data is formed on the server.

[0166] Step 3:

[0167] The server inputs the collected digital data into a generative artificial intelligence model. The AI ​​model analyzes the input data, extracts important information, and generates a summary. In this process, it identifies the main points of the information and creates a summary sentence. As output, the summary is sent back to the server.

[0168] Step 4:

[0169] The device uses a camera and microphone to collect user emotion data, detecting facial expressions, voice tone, and other characteristics. Using this data, the device performs emotion analysis and sends the results to a server. Emotion data is acquired as input, and the analysis results are transferred to the server as output.

[0170] Step 5:

[0171] The server receives sentiment data and adjusts the summary based on that information. This adjustment involves filtering, such as prioritizing positive content in the summary according to the user's emotional state. The adjusted summary is then prepared as output.

[0172] Step 6:

[0173] The server sends the adjusted summary to the user's terminal, which then displays it. The user can view the displayed summary and provide feedback. As output, the final news summary displayed to the user is provided.

[0174] Step 7:

[0175] The server collects user feedback and sentiment data, which is then used to tune the generative artificial intelligence model. This information is used as training material to improve the model's performance. As an output, the model's accuracy improves.

[0176] (Application Example 2)

[0177] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0178] In modern society, users have access to a vast amount of information, but they are required to efficiently select the necessary and useful information from it. Furthermore, since the way information is received differs depending on an individual's emotional state, there is a need for means of providing information in an optimal form that corresponds to the user's current emotions. This invention aims to optimize information provision while taking into account the user's emotional state.

[0179] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0180] In this invention, the server includes means for acquiring a specific information genre requested by the user, means for collecting relevant digital materials from online information sources, means for inputting the collected digital materials into a generative artificial intelligence model to extract important points and generate a summary, and means for recognizing the user's current emotional state and adjusting the news summary accordingly. This makes it possible to provide valuable information optimized for the user's emotional state.

[0181] "User" refers to each individual user who uses this system to obtain information.

[0182] "Information genre" refers to a specific field or topic of information that a user is interested in and requests.

[0183] "Online information sources" refers to all media that provide digital information accessible on the internet, such as news sites, databases, and blogs.

[0184] "Digital materials" refer to data in electronically stored formats, including text, images, videos, and other information formats.

[0185] A "generative artificial intelligence model" is a computational model for artificial intelligence that generates new information based on large amounts of data.

[0186] "Key points" refer to the content of the collected information that is deemed particularly valuable.

[0187] A "summary" refers to a document that extracts the key points of information and presents them concisely.

[0188] "Ranking" is the process of ordering summaries based on specific criteria and determining their priority.

[0189] "Emotional state" refers to the psychological atmosphere or mood that a user exhibits when receiving information.

[0190] "Adjusting news summaries" refers to the act of modifying the wording and content of summarized news articles to take into account the user's emotional state.

[0191] This invention relates to a news summary system equipped with an emotion recognition engine. An application installed on the user's device can capture the user's facial expressions and voice in real time using the smartphone's camera and microphone. This captured data is analyzed on the device using machine learning libraries such as TENSORFLOW® to identify the user's emotional state.

[0192] The server collects digital materials from online information sources based on the information genre specified by the user. The collected materials are summarized using OpenAI's generative artificial intelligence model, and key points are extracted.

[0193] Furthermore, the emotion engine adjusts the generated summaries according to the user's emotional state, delivering news in the most optimal way for the user. For example, if a user is experiencing everyday stress, the server prioritizes delivering content that includes positive news and solutions.

[0194] A specific use case would be when a user requests information about environmental issues. The server would then search for relevant news articles and provide a summary generated using a generative AI model, adjusting the content to include positive stories and progress for users in a relaxed state.

[0195] An example of a prompt message would be, "Generate news summaries that are recommended when the user's mood is relaxed." This is how you would instruct the generation AI model.

[0196] Such a system allows users to efficiently receive information that fits their emotional state, enabling them to meaningfully process the latest news even in an information-saturated society.

[0197] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0198] Step 1:

[0199] The device captures the user's facial expressions and voice in real time using the user's camera and microphone. This data is analyzed using machine learning libraries such as TensorFlow to identify the user's emotional state. The input is the user's facial expressions and voice data, and the output is a tag indicating the emotional state. Specifically, facial recognition algorithms and spectral analysis of the voice are used to analyze subtle changes in facial expressions and tone of voice.

[0200] Step 2:

[0201] The user selects information genres on the device according to their interests. This action causes the device to send the selected information genres to the server. The input is the user's selected information genres, and the output is the transmission of the selected information to the server. Specifically, this involves selecting information categories from a list using the touch panel.

[0202] Step 3:

[0203] The server collects relevant digital materials from online information sources based on the selected information genre. The input in this process is the user's genre request, and the output is a collection of digital materials. Specifically, it uses web crawling technology to search for articles and blogs that match the specified keywords.

[0204] Step 4:

[0205] The server inputs the collected digital materials into an AI model, which extracts key points and generates a summary. Instructions are given to the AI ​​using prompts. The input is the text data of the digital materials, and the output is the summarized text. Specifically, the AI ​​model uses natural language processing to analyze keywords and context within the text and generate the summary.

[0206] Step 5:

[0207] The server adjusts the generated summary according to the user's emotional state. Based on this information, it makes adjustments such as emphasizing positive content. The input is the generated summary and the emotional state, and the output is the adjusted summary. Specifically, if the emotional state is determined to be "relaxed," the server will present many episodes containing words of encouragement.

[0208] Step 6:

[0209] The refined summary is sent to the device and displayed to the user. The user reviews the news and evaluates its content. The input is the refined summary, and the output is the user's feedback. Specifically, the summary is displayed in text format on the device's screen, and the user clicks a button to submit feedback.

[0210] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0211] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0212] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0216] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0217] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0218] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0219] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0220] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0221] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0222] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0223] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0224] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0225] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0226] This invention relates to a news summary agent aimed at enabling information gatherers and business professionals to quickly obtain the information they need. The system solves the problem of information overload by collecting appropriate digital information from online sources in response to information requests from users and summarizing it using a generative artificial intelligence model.

[0227] The user first opens the News Summary Assistant app on their device. They select information categories and topics of interest and submit a request. Based on the user's request, the server then consults reliable sources on the internet (such as news sites and APIs) and collects relevant digital information.

[0228] The collected digital information is sent to a generative artificial intelligence model running on a server. This AI model analyzes numerous news articles and documents, extracting key facts, figures, and conclusions, and creating summaries in an easy-to-understand format. The generated summaries undergo a ranking process to select those deemed most useful to the user.

[0229] The selected summaries are sent from the server to the user's device, where the user can view them. It's also possible to submit feedback on the quality and content of the summaries through the app, and this feedback is used to improve the generative AI model.

[0230] For example, if a user requests news about "the latest technology trends," the server crawls multiple technology news sites. The AI ​​model summarizes key information from the articles, such as "a certain company has announced new AI technology," and the user can receive this information along with a link to the full article. This process allows users to grasp important information in a short amount of time.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] The user launches the News Summary Assistant application on their device, selects information categories and topics of interest (e.g., "Financial Market Trends"), and creates a request.

[0234] Step 2:

[0235] The user's request is sent to the server. The server queries multiple online sources on the internet to collect news articles related to the selected topic. In doing so, it uses APIs and scraping techniques to obtain the latest digital information.

[0236] Step 3:

[0237] The server inputs the collected news article data into a generative AI model. The AI ​​model analyzes the text and extracts key information from the articles. For example, it extracts major facts, announcements, and statistics mentioned in the articles and generates a summary.

[0238] Step 4:

[0239] The server ranks the generated summaries based on their importance and relevance. From these ranked summaries, the most relevant ones are selected for the user. This results in the user receiving more focused information.

[0240] Step 5:

[0241] The selected summaries are sent from the server to the user's device. Users can review the summaries and access more in-depth content by clicking on links to detailed articles or information that interest them.

[0242] Step 6:

[0243] Users provide feedback on the quality and relevance of the summaries they view within the app. This feedback is collected by a server and used to personalize and improve the accuracy of the generative AI model, thereby improving the user experience in the future.

[0244] (Example 1)

[0245] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0246] In today's world, a vast amount of information exists online, making it difficult for information users to quickly find information that is useful to them. This invention aims to alleviate the burden of information overload by efficiently collecting the information that information users seek, quickly summarizing its important elements, and providing them to them.

[0247] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0248] In this invention, the server includes means for receiving information classifications requested by information users, means for collecting relevant information from online information sources, and means for inputting the collected information into a generative learning model, extracting important information, and generating a summary. This makes it possible to quickly and efficiently summarize and provide the information that information users need.

[0249] "Information user" refers to an individual or group that seeks and intends to obtain specific information.

[0250] "Information classification" refers to specific categories or topics that information users are particularly interested in, and information gathering is carried out based on these selections.

[0251] "Online information sources" refer to internet resources that provide information, such as news sites, databases, and APIs that are publicly available on the web.

[0252] A "generative learning model" refers to an artificial intelligence model that uses pre-trained data to perform summarization and content generation in response to new inputs.

[0253] "Important information" refers to facts, figures, and conclusions among the collected information that are deemed particularly useful and valuable to the information user.

[0254] "Generating summaries" refers to the process of creating condensed information from data analyzed by a generative learning model, making it easily understandable to information users.

[0255] "Ranking and selecting based on value" refers to evaluating the generated summaries and prioritizing and selecting those that are considered most relevant and beneficial to the information user.

[0256] "Displaying connections to information users" refers to providing information users with links or actions that offer more detailed information or additional materials related to the summary.

[0257] This invention is a news summary system that efficiently collects and summarizes information needed by information users. The main components of the system include a "server" that collects and processes information, a "terminal" that requests information, and a "user" that inputs information.

[0258] The user launches the News Summary Assistant app from their device, selects the information categories they are interested in, and sends a request. The device then acts as a proxy, sending the request to the server based on the user's selection.

[0259] The server collects relevant information from online information sources in response to incoming requests. These information sources include news sites, databases, and APIs on the internet. The server accesses these sources to obtain the necessary digital information in real time.

[0260] Next, the server inputs the collected information into a generative learning model, which extracts key information and creates a summary. This AI model uses natural language processing algorithms. The model tokenizes the information and evaluates the importance of sentences to generate the most useful summary for the user.

[0261] The generated summaries are further evaluated and ranked on the server. This selects the summary that best matches the information user's requirements and sends it to the user via their terminal. The user can view the information provided on their terminal and, if necessary, follow connections to obtain more detailed information.

[0262] For example, if a user requests a summary of "international news," the server retrieves relevant information from multiple international news sites, and a generative learning model summarizes key points such as "agreements on new international agreements in major countries." This summary helps users accurately grasp the information they are looking for.

[0263] A concrete example of a prompt message would be a request such as, "Please provide a summary of the latest developments regarding international news topics." This allows information users to obtain the necessary information in a short amount of time.

[0264] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0265] Step 1:

[0266] The user launches the News Summary Assistant app through their device. They select the information categories and topics of interest, enter their request into the input form, and then press the submit button to send the request to the server. At this point, the input is the topic specified by the user, and the output is the specific request sent to the server.

[0267] Step 2:

[0268] The server accesses online information sources based on information classification requests received from users. These information sources include news sites and public APIs. Here, the server queries the specified information sources and collects the relevant digital information. The input is a search query based on the request, and the output is raw data collected in the form of news articles or datasets.

[0269] Step 3:

[0270] The server begins the process of inputting the collected digital information into a generative learning model. The model utilizes natural language processing techniques to tokenize the information, extract key points, and generate summaries. Specifically, the model analyzes the information received as input, highlights elements deemed important to the user, and summarizes them. The input for this step is the raw data collected in the previous stage, and the generated summary is the output.

[0271] Step 4:

[0272] The generated summaries are ranked on the server. The server evaluates the generated summaries and selects those containing the key points that information users are most likely to be interested in. The selection is based on the relevance and importance of the information. The input to the ranking process is summaries from a generative learning model, and the final selected summaries are obtained as output.

[0273] Step 5:

[0274] The server sends the selected summaries to the terminal. The transmission process includes links to related details along with the final selected summaries. The end user can view the summaries on the terminal and access further information via the links as needed. The input is the selected summaries, and the output is the summary information and links that the user receives on the terminal.

[0275] Step 6:

[0276] Users evaluate summaries and send feedback to the server via the app through their device. This feedback concerns the quality and relevance of the summary. The server collects this feedback and uses it to improve future generative learning models. The input is user feedback, and the output is evaluation information fed into the improvement cycle.

[0277] (Application Example 1)

[0278] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0279] In recent years, the rapid advancement of information technology has exposed urban administrators and residents to vast amounts of information, requiring them to quickly extract important information and utilize it efficiently. However, in such an information-overloaded environment, selecting and summarizing appropriate information is not easy, which can hinder efficient urban management and the provision of public services. Therefore, a system is needed that can summarize important information in real time and visualize it appropriately.

[0280] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0281] In this invention, the server includes means for receiving specific information categories requested by a user, means for collecting the relevant digital data from online information sources, and means for inputting the collected digital data into a generative artificial intelligence model to extract important information and generate a summary. This makes it possible to quickly obtain important information needed by city administrators and citizens, thereby improving the efficient management of cities and the provision of citizen services.

[0282] A "user" refers to an individual or organization that seeks to obtain information from a system, and is responsible for making requests for specific information.

[0283] "Information Category" refers to a specific type of information that is collected by the system and classified for providing to users.

[0284] "Digital Data" refers to information expressed in an electronic form obtained from online information sources.

[0285] "Generative AI Model" is an advanced algorithm used to analyze vast amounts of information data and generate summaries based on it.

[0286] "Summary" is an abstract of information presented in a concise form by extracting important elements from the collected information.

[0287] "Visualization Means" refers to an interface or technology for displaying information so that users can easily understand and view it.

[0288] "Communication Means" is a technical means for transmitting and receiving information between users and the system, and plays a role in quickly transmitting specific information.

[0289] "Hyperlink" is an online reference point for users to access related additional information and detailed information.

[0290] "Smart Device" is a portable electronic device equipped with an Internet connection function and capable of real-time notifications and information display.

[0291] Embodiments of this invention relate to a news summary system specialized for urban information management. Users can use portable devices such as smartphones and smart glasses to select specific information categories (e.g., traffic, public services, event information) and request information.

[0292] The server collects digital data from online sources via the internet and inputs this data into a generative artificial intelligence model in the cloud, such as OpenAI's GPT. This extracts key information from the digital data and generates concise summaries. The generated summaries are then ranked and sorted based on relevance.

[0293] Selected summaries are sent to the user's terminal and displayed through a dedicated application. This application visualizes the summaries in an easy-to-understand format and provides the user with relevant hyperlinks. This allows the user to access detailed information as needed.

[0294] For example, if a user requests "Today's city events information," the server collects relevant information and uses a generative artificial intelligence model to generate a summary such as "A music festival will be held in the city center from 3 PM," and notifies the user. In this way, the user can quickly obtain the information they need.

[0295] An example of a prompt for a generative AI model is, "Please provide a summary of the latest traffic information for the city." This allows the system to begin the process of collecting and summarizing the appropriate information.

[0296] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0297] Step 1:

[0298] The user uses a terminal to select a specific information category (e.g., transportation, public services) and submits a request. This input is initial information from the user that triggers processing on the server side.

[0299] Step 2:

[0300] The server collects relevant digital data from online information sources on the Internet based on the information categories received from the user. In this process, it calls the API for reliable information sources to obtain data. The input is the user's information category, and the output is the raw data collected.

[0301] Step 3:

[0302] The server inputs the collected digital data into a generative artificial intelligence model. The generative AI model analyzes this data, extracts important information, and generates a summary. The data processing at this stage includes arithmetic operations to organize key points from a large amount of information and summarize them concisely. The input is the collected digital data, and the output is the generated summary.

[0303] Step 4:

[0304] The server ranks the generated summaries and selects them based on relevance. In this process, it uses a machine learning algorithm to score the importance and selects the most useful summary for the user. The input is the generated summary, and the output is the selected summary.

[0305] Step 5:

[0306] The selected summary is sent from the server to the terminal and displayed to the user by a dedicated application. The application visualizes the summary in an easy-to-understand format and makes it accessible through the user interface. The input is the selected summary, and the output is the display as information that the user can view.

[0307] Step 6:

[0308] Users can submit feedback on the information provided, which is aggregated on the server and used to improve the performance of the generative artificial intelligence model. This process includes analyzing the feedback data and inputting it into the model improvement algorithm. The input is the user's feedback, and the output is the improved AI model.

[0309] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0310] This invention is a news summary agent system incorporating an emotion engine, enabling users to receive more personalized news information. Specifically, it aims to optimize the presentation of information summaries by adjusting them according to the user's current emotional state, and further optimize the provision of useful information.

[0311] First, the user launches the news summary app on their device and selects information categories and topics of interest. The server receives this request and collects relevant digital information from appropriate sources on the internet. The collected information is summarized using a generative artificial intelligence model, and the main points are extracted.

[0312] The key feature of this system lies in the function of its emotion engine. While the user is using the device, the emotion engine analyzes the user's facial expressions, tone of voice, and the resulting emotional data. This data is sent to the server, where the user's emotional state is analyzed. For example, if the emotion engine determines that the user is feeling down, the server will improve the user experience by prioritizing the display of pre-prepared news summaries containing encouragement and positive content.

[0313] The server also provides other relevant perspectives and additional information based on emotional data and the user's interests and requests. For example, if a user expresses interest in "environmental issues" and the emotional engine determines that the user is stressed, the server will present summaries of articles reporting specific solutions or positive developments.

[0314] Finally, users review the summary they receive and provide feedback on its quality and content. The server accumulates this feedback and sentiment data, which is then used to tune the generative AI model, allowing it to continuously provide a more personalized experience. This system enables not only the delivery of information, but also the delivery of valuable information tailored to the user's emotions.

[0315] The following describes the processing flow.

[0316] Step 1:

[0317] Users launch the News Summary Assistant application on their device, select a specific information category or topic (e.g., "Healthcare," "Climate Change"), and send a request through the app.

[0318] Step 2:

[0319] The server receives requests from users and collects news articles related to the selected topic from online sources. This is done using news APIs and web scraping techniques to obtain reliable information.

[0320] Step 3:

[0321] The collected news article data is fed into a generative artificial intelligence model on a server. The generative AI model analyzes the article content, extracts important information, and generates a concise summary. This summary includes the main points and conclusions of the article.

[0322] Step 4:

[0323] An emotion engine built into the user's device analyzes the user's emotional state in real time. This process uses the camera and microphone to analyze the user's facial expressions and tone of voice to identify feelings such as joy, sadness, and excitement that the user is currently experiencing.

[0324] Step 5:

[0325] The analysis results from the emotion engine are sent to the server. The server uses the received emotion data to rank the generated summaries and select the summary that best suits the user's emotional state. For example, if the user is determined to be tired, the server will prioritize selecting summaries with less information and more positive content.

[0326] Step 6:

[0327] The server sends the selected summaries to the user's device. The user can view these summaries and, if more detailed information is needed, refer to the original articles using the provided links.

[0328] Step 7:

[0329] Users input feedback on the summary content into their device. This feedback is sent to the server and used, along with sentiment data, to improve the generative AI model. This allows the system to evolve, providing users with more relevant and personalized information.

[0330] (Example 2)

[0331] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0332] Conventional information delivery systems specialize in data collection and summarization from specific information domains, but they have limitations in providing personalized experiences that reflect the emotional state of users. As a result, appropriate information that is not tailored to the specific mental state and context of the information recipient is often not provided, making it difficult to improve user satisfaction.

[0333] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0334] In this invention, the server includes means for receiving a specific information area requested by a user, means for acquiring relevant digital data from information sources on a telecommunications network, and means for adjusting a summary formed based on the user's emotional state. This enables the provision of personalized information according to the user's emotional state, thereby achieving higher satisfaction and an optimized user experience.

[0335] "Information domain" refers to information in a specific field or category that a user requests.

[0336] "Telecommunications network" refers to the internet and other network infrastructure that enables the transmission and reception of information.

[0337] "Digital data" refers to information that is generated or stored electronically, including text, images, audio, and video.

[0338] A "generative artificial intelligence model" refers to an algorithm that generates information based on large amounts of data, extracts knowledge through learning, and creates new summaries and content.

[0339] "Emotional state" refers to a user's temporary psychological emotional state and is classified into categories such as positive, negative, and neutral.

[0340] A "summary" is a way of condensing the main points and knowledge of information and providing them in a short format.

[0341] "Related links" refer to hyperlinks that provide further information or details related to the generated summary.

[0342] This invention is a system for providing personalized information to users. The entire system includes a terminal, a server, a generative artificial intelligence model, and an emotion analysis engine.

[0343] The user launches a news summary app on their device and inputs information about specific areas of interest. This app sends the user's requests to a server. The device has built-in hardware such as a camera and microphone to collect emotional data.

[0344] The server uses telecommunications networks to collect relevant digital data from the internet. Web scraping tools and APIs may be used for this collection. The collected digital data is input into a generative artificial intelligence model to extract key knowledge and form a summary. This AI model leverages machine learning techniques to efficiently generate the main points of the content.

[0345] The device analyzes the user's emotional state in real time using an emotion analysis engine. The device's camera and microphone capture the user's facial expressions and voice tone, and send the data to a server for analysis of the user's psychological state.

[0346] The server uses this sentiment data to refine the summaries and rank them according to relevance. Because the refined summaries are edited to match the user's emotions, the quality of the user experience is improved.

[0347] For example, if a user expresses interest in "environmental issues" and the sentiment analysis engine determines that they are stressed, the server will prioritize summarizing and displaying articles that report positive developments.

[0348] Examples of prompts include, "Generate a summary of environmental articles with positive content based on the user's current emotional state," and "Generate a summary of the latest technology news and adjust the display order considering the user's emotional data."

[0349] In this way, the system goes beyond simply providing information and realizes value-added services that respond to the emotional state of the user.

[0350] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0351] Step 1:

[0352] The user launches a news summary app on their device and selects a specific information area. The device receives the user's selected information category as input. The device sends this to the server, initiating a request for information retrieval. The server receives the user's areas of interest as output.

[0353] Step 2:

[0354] The server collects relevant digital data from various sources on the internet via telecommunications networks. It uses web scraping tools and APIs to retrieve data related to specified information categories. The collected data is stored on the server, and the process proceeds to the next step. As output, a collection of digital data is formed on the server.

[0355] Step 3:

[0356] The server inputs the collected digital data into a generative artificial intelligence model. The AI ​​model analyzes the input data, extracts important information, and generates a summary. In this process, it identifies the main points of the information and creates a summary sentence. As output, the summary is sent back to the server.

[0357] Step 4:

[0358] The device uses a camera and microphone to collect user emotion data, detecting facial expressions, voice tone, and other characteristics. Using this data, the device performs emotion analysis and sends the results to a server. Emotion data is acquired as input, and the analysis results are transferred to the server as output.

[0359] Step 5:

[0360] The server receives sentiment data and adjusts the summary based on that information. This adjustment involves filtering, such as prioritizing positive content in the summary according to the user's emotional state. The adjusted summary is then prepared as output.

[0361] Step 6:

[0362] The server sends the adjusted summary to the user's terminal, which then displays it. The user can view the displayed summary and provide feedback. As output, the final news summary displayed to the user is provided.

[0363] Step 7:

[0364] The server collects user feedback and sentiment data, which is then used to tune the generative artificial intelligence model. This information is used as training material to improve the model's performance. As an output, the model's accuracy improves.

[0365] (Application Example 2)

[0366] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0367] In modern society, users have access to a vast amount of information, but they are required to efficiently select the necessary and useful information from it. Furthermore, since the way information is received differs depending on an individual's emotional state, there is a need for means of providing information in an optimal form that corresponds to the user's current emotions. This invention aims to optimize information provision while taking into account the user's emotional state.

[0368] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0369] In this invention, the server includes means for acquiring a specific information genre requested by the user, means for collecting relevant digital materials from online information sources, means for inputting the collected digital materials into a generative artificial intelligence model to extract important points and generate a summary, and means for recognizing the user's current emotional state and adjusting the news summary accordingly. This makes it possible to provide valuable information optimized for the user's emotional state.

[0370] "User" refers to each individual user who uses this system to obtain information.

[0371] "Information genre" refers to a specific field or topic of information that a user is interested in and requests.

[0372] "Online information sources" refers to all media that provide digital information accessible on the internet, such as news sites, databases, and blogs.

[0373] "Digital materials" refer to data in electronically stored formats, including text, images, videos, and other information formats.

[0374] A "generative artificial intelligence model" is a computational model for artificial intelligence that generates new information based on large amounts of data.

[0375] "Key points" refer to the content of the collected information that is deemed particularly valuable.

[0376] A "summary" refers to a document that extracts the key points of information and presents them concisely.

[0377] "Ranking" is the process of ordering summaries based on specific criteria and determining their priority.

[0378] "Emotional state" refers to the psychological atmosphere or mood that a user exhibits when receiving information.

[0379] "Adjusting news summaries" refers to the act of modifying the wording and content of summarized news articles to take into account the user's emotional state.

[0380] This invention relates to a news summary system equipped with an emotion recognition engine. An application installed on the user's device can capture the user's facial expressions and voice in real time using the smartphone's camera and microphone. This captured data is analyzed on the device using machine learning libraries such as TensorFlow to identify the user's emotional state.

[0381] The server collects digital materials from online information sources based on the information genre specified by the user. The collected materials are summarized using OpenAI's generative artificial intelligence model, and key points are extracted.

[0382] Furthermore, the emotion engine adjusts the generated summaries according to the user's emotional state, delivering news in the most optimal way for the user. For example, if a user is experiencing everyday stress, the server prioritizes delivering content that includes positive news and solutions.

[0383] A specific use case would be when a user requests information about environmental issues. The server would then search for relevant news articles and provide a summary generated using a generative AI model, adjusting the content to include positive stories and progress for users in a relaxed state.

[0384] An example of a prompt message would be, "Generate news summaries that are recommended when the user's mood is relaxed." This is how you would instruct the generation AI model.

[0385] Such a system allows users to efficiently receive information that fits their emotional state, enabling them to meaningfully process the latest news even in an information-saturated society.

[0386] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0387] Step 1:

[0388] The device captures the user's facial expressions and voice in real time using the user's camera and microphone. This data is analyzed using machine learning libraries such as TensorFlow to identify the user's emotional state. The input is the user's facial expressions and voice data, and the output is a tag indicating the emotional state. Specifically, facial recognition algorithms and spectral analysis of the voice are used to analyze subtle changes in facial expressions and tone of voice.

[0389] Step 2:

[0390] The user selects information genres on the device according to their interests. This action causes the device to send the selected information genres to the server. The input is the user's selected information genres, and the output is the transmission of the selected information to the server. Specifically, this involves selecting information categories from a list using the touch panel.

[0391] Step 3:

[0392] The server collects relevant digital materials from online information sources based on the selected information genre. The input in this process is the user's genre request, and the output is a collection of digital materials. Specifically, it uses web crawling technology to search for articles and blogs that match the specified keywords.

[0393] Step 4:

[0394] The server inputs the collected digital materials into an AI model, which extracts key points and generates a summary. Instructions are given to the AI ​​using prompts. The input is the text data of the digital materials, and the output is the summarized text. Specifically, the AI ​​model uses natural language processing to analyze keywords and context within the text and generate the summary.

[0395] Step 5:

[0396] The server adjusts the generated summary according to the user's emotional state. Based on this information, it makes adjustments such as emphasizing positive content. The input is the generated summary and the emotional state, and the output is the adjusted summary. Specifically, if the emotional state is determined to be "relaxed," the server will present many episodes containing words of encouragement.

[0397] Step 6:

[0398] The refined summary is sent to the device and displayed to the user. The user reviews the news and evaluates its content. The input is the refined summary, and the output is the user's feedback. Specifically, the summary is displayed in text format on the device's screen, and the user clicks a button to submit feedback.

[0399] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0400] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0401] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0402] [Third Embodiment]

[0403] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0404] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0405] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0406] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0407] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0408] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0409] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0410] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0411] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0412] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0413] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0414] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0415] This invention relates to a news summary agent aimed at enabling information gatherers and business professionals to quickly obtain the information they need. The system solves the problem of information overload by collecting appropriate digital information from online sources in response to information requests from users and summarizing it using a generative artificial intelligence model.

[0416] The user first opens the News Summary Assistant app on their device. They select information categories and topics of interest and submit a request. Based on the user's request, the server then consults reliable sources on the internet (such as news sites and APIs) and collects relevant digital information.

[0417] The collected digital information is sent to a generative artificial intelligence model running on a server. This AI model analyzes numerous news articles and documents, extracting key facts, figures, and conclusions, and creating summaries in an easy-to-understand format. The generated summaries undergo a ranking process to select those deemed most useful to the user.

[0418] The selected summaries are sent from the server to the user's device, where the user can view them. It's also possible to submit feedback on the quality and content of the summaries through the app, and this feedback is used to improve the generative AI model.

[0419] For example, if a user requests news about "the latest technology trends," the server crawls multiple technology news sites. The AI ​​model summarizes key information from the articles, such as "a certain company has announced new AI technology," and the user can receive this information along with a link to the full article. This process allows users to grasp important information in a short amount of time.

[0420] The following describes the processing flow.

[0421] Step 1:

[0422] The user launches the News Summary Assistant application on their device, selects information categories and topics of interest (e.g., "Financial Market Trends"), and creates a request.

[0423] Step 2:

[0424] The user's request is sent to the server. The server queries multiple online sources on the internet to collect news articles related to the selected topic. In doing so, it uses APIs and scraping techniques to obtain the latest digital information.

[0425] Step 3:

[0426] The server inputs the collected news article data into a generative AI model. The AI ​​model analyzes the text and extracts key information from the articles. For example, it extracts major facts, announcements, and statistics mentioned in the articles and generates a summary.

[0427] Step 4:

[0428] The server ranks the generated summaries based on their importance and relevance. From these ranked summaries, the most relevant ones are selected for the user. This results in the user receiving more focused information.

[0429] Step 5:

[0430] The selected summaries are sent from the server to the user's device. Users can review the summaries and access more in-depth content by clicking on links to detailed articles or information that interest them.

[0431] Step 6:

[0432] Users provide feedback on the quality and relevance of the summaries they view within the app. This feedback is collected by a server and used to personalize and improve the accuracy of the generative AI model, thereby improving the user experience in the future.

[0433] (Example 1)

[0434] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0435] In today's world, a vast amount of information exists online, making it difficult for information users to quickly find information that is useful to them. This invention aims to alleviate the burden of information overload by efficiently collecting the information that information users seek, quickly summarizing its important elements, and providing them to them.

[0436] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0437] In this invention, the server includes means for receiving information classifications requested by information users, means for collecting relevant information from online information sources, and means for inputting the collected information into a generative learning model, extracting important information, and generating a summary. This makes it possible to quickly and efficiently summarize and provide the information that information users need.

[0438] "Information user" refers to an individual or group that seeks and intends to obtain specific information.

[0439] "Information classification" refers to specific categories or topics that information users are particularly interested in, and information gathering is carried out based on these selections.

[0440] "Online information sources" refer to internet resources that provide information, such as news sites, databases, and APIs that are publicly available on the web.

[0441] A "generative learning model" refers to an artificial intelligence model that uses pre-trained data to perform summarization and content generation in response to new inputs.

[0442] "Important information" refers to facts, figures, and conclusions among the collected information that are deemed particularly useful and valuable to the information user.

[0443] "Generating summaries" refers to the process of creating condensed information from data analyzed by a generative learning model, making it easily understandable to information users.

[0444] "Ranking and selecting based on value" refers to evaluating the generated summaries and prioritizing and selecting those that are considered most relevant and beneficial to the information user.

[0445] "Displaying connections to information users" refers to providing information users with links or actions that offer more detailed information or additional materials related to the summary.

[0446] This invention is a news summary system that efficiently collects and summarizes information needed by information users. The main components of the system include a "server" that collects and processes information, a "terminal" that requests information, and a "user" that inputs information.

[0447] The user launches the News Summary Assistant app from their device, selects the information categories they are interested in, and sends a request. The device then acts as a proxy, sending the request to the server based on the user's selection.

[0448] The server collects relevant information from online information sources in response to incoming requests. These information sources include news sites, databases, and APIs on the internet. The server accesses these sources to obtain the necessary digital information in real time.

[0449] Next, the server inputs the collected information into a generative learning model, which extracts key information and creates a summary. This AI model uses natural language processing algorithms. The model tokenizes the information and evaluates the importance of sentences to generate the most useful summary for the user.

[0450] The generated summaries are further evaluated and ranked on the server. This selects the summary that best matches the information user's requirements and sends it to the user via their terminal. The user can view the information provided on their terminal and, if necessary, follow connections to obtain more detailed information.

[0451] For example, if a user requests a summary of "international news," the server retrieves relevant information from multiple international news sites, and a generative learning model summarizes key points such as "agreements on new international agreements in major countries." This summary helps users accurately grasp the information they are looking for.

[0452] A concrete example of a prompt message would be a request such as, "Please provide a summary of the latest developments regarding international news topics." This allows information users to obtain the necessary information in a short amount of time.

[0453] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0454] Step 1:

[0455] The user launches the News Summary Assistant app through their device. They select the information categories and topics of interest, enter their request into the input form, and then press the submit button to send the request to the server. At this point, the input is the topic specified by the user, and the output is the specific request sent to the server.

[0456] Step 2:

[0457] The server accesses online information sources based on information classification requests received from users. These information sources include news sites and public APIs. Here, the server queries the specified information sources and collects the relevant digital information. The input is a search query based on the request, and the output is raw data collected in the form of news articles or datasets.

[0458] Step 3:

[0459] The server begins the process of inputting the collected digital information into a generative learning model. The model utilizes natural language processing techniques to tokenize the information, extract key points, and generate summaries. Specifically, the model analyzes the information received as input, highlights elements deemed important to the user, and summarizes them. The input for this step is the raw data collected in the previous stage, and the generated summary is the output.

[0460] Step 4:

[0461] The generated summaries are ranked on the server. The server evaluates the generated summaries and selects those containing the key points that information users are most likely to be interested in. The selection is based on the relevance and importance of the information. The input to the ranking process is summaries from a generative learning model, and the final selected summaries are obtained as output.

[0462] Step 5:

[0463] The server sends the selected summaries to the terminal. The transmission process includes links to related details along with the final selected summaries. The end user can view the summaries on the terminal and access further information via the links as needed. The input is the selected summaries, and the output is the summary information and links that the user receives on the terminal.

[0464] Step 6:

[0465] Users evaluate summaries and send feedback to the server via the app through their device. This feedback concerns the quality and relevance of the summary. The server collects this feedback and uses it to improve future generative learning models. The input is user feedback, and the output is evaluation information fed into the improvement cycle.

[0466] (Application Example 1)

[0467] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0468] In recent years, the rapid advancement of information technology has exposed urban administrators and residents to vast amounts of information, requiring them to quickly extract important information and utilize it efficiently. However, in such an information-overloaded environment, selecting and summarizing appropriate information is not easy, which can hinder efficient urban management and the provision of public services. Therefore, a system is needed that can summarize important information in real time and visualize it appropriately.

[0469] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0470] In this invention, the server includes means for receiving specific information categories requested by a user, means for collecting the relevant digital data from online information sources, and means for inputting the collected digital data into a generative artificial intelligence model to extract important information and generate a summary. This makes it possible to quickly obtain important information needed by city administrators and citizens, thereby improving the efficient management of cities and the provision of citizen services.

[0471] A "user" refers to an individual or organization that seeks to obtain information from a system, and is responsible for making requests for specific information.

[0472] An "information category" is a specific type of information that a system collects and categorizes for use with users.

[0473] "Digital data" refers to information expressed in electronic form obtained from online sources.

[0474] A "generative artificial intelligence model" is an advanced algorithm used to analyze vast amounts of information data and generate summaries based on that analysis.

[0475] A "summary" is an abstract of information that extracts the most important elements from collected information and presents them in a concise format.

[0476] "Visualization means" refers to an interface or technology that displays information in a way that allows users to easily understand and view it.

[0477] "Communication means" are technical means for sending and receiving information between a user and a system, and play a role in quickly transmitting specific information.

[0478] A "hyperlink" is an online reference point that allows users to access additional or more detailed information related to the topic.

[0479] A "smart device" is a portable electronic device equipped with internet connectivity that enables real-time notifications and information display.

[0480] This embodiment of the invention relates to a news summary system specifically designed for urban information management. Users can use portable devices such as smartphones or smart glasses to select specific information categories (e.g., transportation, public services, event information) and request information.

[0481] The server collects digital data from online sources via the internet and inputs this data into a generative artificial intelligence model in the cloud, such as OpenAI's GPT. This extracts key information from the digital data and generates concise summaries. The generated summaries are then ranked and sorted based on relevance.

[0482] Selected summaries are sent to the user's terminal and displayed through a dedicated application. This application visualizes the summaries in an easy-to-understand format and provides the user with relevant hyperlinks. This allows the user to access detailed information as needed.

[0483] For example, if a user requests "Today's city events information," the server collects relevant information and uses a generative artificial intelligence model to generate a summary such as "A music festival will be held in the city center from 3 PM," and notifies the user. In this way, the user can quickly obtain the information they need.

[0484] An example of a prompt for a generative AI model is, "Please provide a summary of the latest traffic information for the city." This allows the system to begin the process of collecting and summarizing the appropriate information.

[0485] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0486] Step 1:

[0487] The user uses a terminal to select a specific information category (e.g., transportation, public services) and submits a request. This input is initial information from the user that triggers processing on the server side.

[0488] Step 2:

[0489] The server collects relevant digital data from online sources on the internet based on the information categories received from the user. This process involves calling APIs from trusted sources to retrieve data. The input is the user's information categories, and the output is the collected raw data.

[0490] Step 3:

[0491] The server inputs the collected digital data into a generative artificial intelligence model. The generative AI model analyzes this data, extracts important information, and generates a summary. Data processing at this stage includes computational processing to organize and concisely summarize key points from a large amount of information. The input is the collected digital data, and the output is the generated summary.

[0492] Step 4:

[0493] The server ranks the generated summaries and sorts them based on relevance. This process uses machine learning algorithms to score importance and select the most useful summaries for the user. The input is the generated summaries, and the output is the sorted summaries.

[0494] Step 5:

[0495] The selected summaries are sent from the server to the terminal and displayed to the user by a dedicated application. The application visualizes the summaries in an easy-to-understand format, making them accessible through a user interface. The input is the selected summaries, and the output is the information displayed for the user to review.

[0496] Step 6:

[0497] Users can submit feedback on the information provided, which is aggregated on the server and used to improve the performance of the generative artificial intelligence model. This process includes analyzing the feedback data and inputting it into the model improvement algorithm. The input is the user's feedback, and the output is the improved AI model.

[0498] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0499] This invention is a news summary agent system incorporating an emotion engine, enabling users to receive more personalized news information. Specifically, it aims to optimize the presentation of information summaries by adjusting them according to the user's current emotional state, and further optimize the provision of useful information.

[0500] First, the user launches the news summary app on their device and selects information categories and topics of interest. The server receives this request and collects relevant digital information from appropriate sources on the internet. The collected information is summarized using a generative artificial intelligence model, and the main points are extracted.

[0501] The key feature of this system lies in the function of its emotion engine. While the user is using the device, the emotion engine analyzes the user's facial expressions, tone of voice, and the resulting emotional data. This data is sent to the server, where the user's emotional state is analyzed. For example, if the emotion engine determines that the user is feeling down, the server will improve the user experience by prioritizing the display of pre-prepared news summaries containing encouragement and positive content.

[0502] The server also provides other relevant perspectives and additional information based on emotional data and the user's interests and requests. For example, if a user expresses interest in "environmental issues" and the emotional engine determines that the user is stressed, the server will present summaries of articles reporting specific solutions or positive developments.

[0503] Finally, users review the summary they receive and provide feedback on its quality and content. The server accumulates this feedback and sentiment data, which is then used to tune the generative AI model, allowing it to continuously provide a more personalized experience. This system enables not only the delivery of information, but also the delivery of valuable information tailored to the user's emotions.

[0504] The following describes the processing flow.

[0505] Step 1:

[0506] Users launch the News Summary Assistant application on their device, select a specific information category or topic (e.g., "Healthcare," "Climate Change"), and send a request through the app.

[0507] Step 2:

[0508] The server receives requests from users and collects news articles related to the selected topic from online sources. This is done using news APIs and web scraping techniques to obtain reliable information.

[0509] Step 3:

[0510] The collected news article data is fed into a generative artificial intelligence model on a server. The generative AI model analyzes the article content, extracts important information, and generates a concise summary. This summary includes the main points and conclusions of the article.

[0511] Step 4:

[0512] An emotion engine built into the user's device analyzes the user's emotional state in real time. This process uses the camera and microphone to analyze the user's facial expressions and tone of voice to identify feelings such as joy, sadness, and excitement that the user is currently experiencing.

[0513] Step 5:

[0514] The analysis results from the emotion engine are sent to the server. The server uses the received emotion data to rank the generated summaries and select the summary that best suits the user's emotional state. For example, if the user is determined to be tired, the server will prioritize selecting summaries with less information and more positive content.

[0515] Step 6:

[0516] The server sends the selected summaries to the user's device. The user can view these summaries and, if more detailed information is needed, refer to the original articles using the provided links.

[0517] Step 7:

[0518] Users input feedback on the summary content into their device. This feedback is sent to the server and used, along with sentiment data, to improve the generative AI model. This allows the system to evolve, providing users with more relevant and personalized information.

[0519] (Example 2)

[0520] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0521] Conventional information delivery systems specialize in data collection and summarization from specific information domains, but they have limitations in providing personalized experiences that reflect the emotional state of users. As a result, appropriate information that is not tailored to the specific mental state and context of the information recipient is often not provided, making it difficult to improve user satisfaction.

[0522] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0523] In this invention, the server includes means for receiving a specific information area requested by a user, means for acquiring relevant digital data from information sources on a telecommunications network, and means for adjusting a summary formed based on the user's emotional state. This enables the provision of personalized information according to the user's emotional state, thereby achieving higher satisfaction and an optimized user experience.

[0524] "Information domain" refers to information in a specific field or category that a user requests.

[0525] "Telecommunications network" refers to the internet and other network infrastructure that enables the transmission and reception of information.

[0526] "Digital data" refers to information that is generated or stored electronically, including text, images, audio, and video.

[0527] A "generative artificial intelligence model" refers to an algorithm that generates information based on large amounts of data, extracts knowledge through learning, and creates new summaries and content.

[0528] "Emotional state" refers to a user's temporary psychological emotional state and is classified into categories such as positive, negative, and neutral.

[0529] A "summary" is a way of condensing the main points and knowledge of information and providing them in a short format.

[0530] "Related links" refer to hyperlinks that provide further information or details related to the generated summary.

[0531] This invention is a system for providing personalized information to users. The entire system includes a terminal, a server, a generative artificial intelligence model, and an emotion analysis engine.

[0532] The user launches a news summary app on their device and inputs information about specific areas of interest. This app sends the user's requests to a server. The device has built-in hardware such as a camera and microphone to collect emotional data.

[0533] The server uses telecommunications networks to collect relevant digital data from the internet. Web scraping tools and APIs may be used for this collection. The collected digital data is input into a generative artificial intelligence model to extract key knowledge and form a summary. This AI model leverages machine learning techniques to efficiently generate the main points of the content.

[0534] The device analyzes the user's emotional state in real time using an emotion analysis engine. The device's camera and microphone capture the user's facial expressions and voice tone, and send the data to a server for analysis of the user's psychological state.

[0535] The server uses this sentiment data to refine the summaries and rank them according to relevance. Because the refined summaries are edited to match the user's emotions, the quality of the user experience is improved.

[0536] For example, if a user expresses interest in "environmental issues" and the sentiment analysis engine determines that they are stressed, the server will prioritize summarizing and displaying articles that report positive developments.

[0537] Examples of prompts include, "Generate a summary of environmental articles with positive content based on the user's current emotional state," and "Generate a summary of the latest technology news and adjust the display order considering the user's emotional data."

[0538] In this way, the system goes beyond simply providing information and realizes value-added services that respond to the emotional state of the user.

[0539] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0540] Step 1:

[0541] The user launches a news summary app on their device and selects a specific information area. The device receives the user's selected information category as input. The device sends this to the server, initiating a request for information retrieval. The server receives the user's areas of interest as output.

[0542] Step 2:

[0543] The server collects relevant digital data from various sources on the internet via telecommunications networks. It uses web scraping tools and APIs to retrieve data related to specified information categories. The collected data is stored on the server, and the process proceeds to the next step. As output, a collection of digital data is formed on the server.

[0544] Step 3:

[0545] The server inputs the collected digital data into a generative artificial intelligence model. The AI ​​model analyzes the input data, extracts important information, and generates a summary. In this process, it identifies the main points of the information and creates a summary sentence. As output, the summary is sent back to the server.

[0546] Step 4:

[0547] The device uses a camera and microphone to collect user emotion data, detecting facial expressions, voice tone, and other characteristics. Using this data, the device performs emotion analysis and sends the results to a server. Emotion data is acquired as input, and the analysis results are transferred to the server as output.

[0548] Step 5:

[0549] The server receives sentiment data and adjusts the summary based on that information. This adjustment involves filtering, such as prioritizing positive content in the summary according to the user's emotional state. The adjusted summary is then prepared as output.

[0550] Step 6:

[0551] The server sends the adjusted summary to the user's terminal, which then displays it. The user can view the displayed summary and provide feedback. As output, the final news summary displayed to the user is provided.

[0552] Step 7:

[0553] The server collects user feedback and sentiment data, which is then used to tune the generative artificial intelligence model. This information is used as training material to improve the model's performance. As an output, the model's accuracy improves.

[0554] (Application Example 2)

[0555] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0556] In modern society, users have access to a vast amount of information, but they are required to efficiently select the necessary and useful information from it. Furthermore, since the way information is received differs depending on an individual's emotional state, there is a need for means of providing information in an optimal form that corresponds to the user's current emotions. This invention aims to optimize information provision while taking into account the user's emotional state.

[0557] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0558] In this invention, the server includes means for acquiring a specific information genre requested by the user, means for collecting relevant digital materials from online information sources, means for inputting the collected digital materials into a generative artificial intelligence model to extract important points and generate a summary, and means for recognizing the user's current emotional state and adjusting the news summary accordingly. This makes it possible to provide valuable information optimized for the user's emotional state.

[0559] "User" refers to each individual user who uses this system to obtain information.

[0560] "Information genre" refers to a specific field or topic of information that a user is interested in and requests.

[0561] "Online information sources" refers to all media that provide digital information accessible on the internet, such as news sites, databases, and blogs.

[0562] "Digital materials" refer to data in electronically stored formats, including text, images, videos, and other information formats.

[0563] A "generative artificial intelligence model" is a computational model for artificial intelligence that generates new information based on large amounts of data.

[0564] "Key points" refer to the content of the collected information that is deemed particularly valuable.

[0565] A "summary" refers to a document that extracts the key points of information and presents them concisely.

[0566] "Ranking" is the process of ordering summaries based on specific criteria and determining their priority.

[0567] "Emotional state" refers to the psychological atmosphere or mood that a user exhibits when receiving information.

[0568] "Adjusting news summaries" refers to the act of modifying the wording and content of summarized news articles to take into account the user's emotional state.

[0569] This invention relates to a news summary system equipped with an emotion recognition engine. An application installed on the user's device can capture the user's facial expressions and voice in real time using the smartphone's camera and microphone. This captured data is analyzed on the device using machine learning libraries such as TensorFlow to identify the user's emotional state.

[0570] The server collects digital materials from online information sources based on the information genre specified by the user. The collected materials are summarized using OpenAI's generative artificial intelligence model, and key points are extracted.

[0571] Furthermore, the emotion engine adjusts the generated summaries according to the user's emotional state, delivering news in the most optimal way for the user. For example, if a user is experiencing everyday stress, the server prioritizes delivering content that includes positive news and solutions.

[0572] A specific use case would be when a user requests information about environmental issues. The server would then search for relevant news articles and provide a summary generated using a generative AI model, adjusting the content to include positive stories and progress for users in a relaxed state.

[0573] An example of a prompt message would be, "Generate news summaries that are recommended when the user's mood is relaxed." This is how you would instruct the generation AI model.

[0574] Such a system allows users to efficiently receive information that fits their emotional state, enabling them to meaningfully process the latest news even in an information-saturated society.

[0575] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0576] Step 1:

[0577] The device captures the user's facial expressions and voice in real time using the user's camera and microphone. This data is analyzed using machine learning libraries such as TensorFlow to identify the user's emotional state. The input is the user's facial expressions and voice data, and the output is a tag indicating the emotional state. Specifically, facial recognition algorithms and spectral analysis of the voice are used to analyze subtle changes in facial expressions and tone of voice.

[0578] Step 2:

[0579] The user selects information genres on the device according to their interests. This action causes the device to send the selected information genres to the server. The input is the user's selected information genres, and the output is the transmission of the selected information to the server. Specifically, this involves selecting information categories from a list using the touch panel.

[0580] Step 3:

[0581] The server collects relevant digital materials from online information sources based on the selected information genre. The input in this process is the user's genre request, and the output is a collection of digital materials. Specifically, it uses web crawling technology to search for articles and blogs that match the specified keywords.

[0582] Step 4:

[0583] The server inputs the collected digital materials into an AI model, which extracts key points and generates a summary. Instructions are given to the AI ​​using prompts. The input is the text data of the digital materials, and the output is the summarized text. Specifically, the AI ​​model uses natural language processing to analyze keywords and context within the text and generate the summary.

[0584] Step 5:

[0585] The server adjusts the generated summary according to the user's emotional state. Based on this information, it makes adjustments such as emphasizing positive content. The input is the generated summary and the emotional state, and the output is the adjusted summary. Specifically, if the emotional state is determined to be "relaxed," the server will present many episodes containing words of encouragement.

[0586] Step 6:

[0587] The refined summary is sent to the device and displayed to the user. The user reviews the news and evaluates its content. The input is the refined summary, and the output is the user's feedback. Specifically, the summary is displayed in text format on the device's screen, and the user clicks a button to submit feedback.

[0588] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0589] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0590] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0591] [Fourth Embodiment]

[0592] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0593] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0594] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0595] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0596] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0597] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0598] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0599] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0600] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0601] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0602] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0603] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0604] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0605] This invention relates to a news summary agent aimed at enabling information gatherers and business professionals to quickly obtain the information they need. The system solves the problem of information overload by collecting appropriate digital information from online sources in response to information requests from users and summarizing it using a generative artificial intelligence model.

[0606] The user first opens the News Summary Assistant app on their device. They select information categories and topics of interest and submit a request. Based on the user's request, the server then consults reliable sources on the internet (such as news sites and APIs) and collects relevant digital information.

[0607] The collected digital information is sent to a generative artificial intelligence model running on a server. This AI model analyzes numerous news articles and documents, extracting key facts, figures, and conclusions, and creating summaries in an easy-to-understand format. The generated summaries undergo a ranking process to select those deemed most useful to the user.

[0608] The selected summaries are sent from the server to the user's device, where the user can view them. It's also possible to submit feedback on the quality and content of the summaries through the app, and this feedback is used to improve the generative AI model.

[0609] For example, if a user requests news about "the latest technology trends," the server crawls multiple technology news sites. The AI ​​model summarizes key information from the articles, such as "a certain company has announced new AI technology," and the user can receive this information along with a link to the full article. This process allows users to grasp important information in a short amount of time.

[0610] The following describes the processing flow.

[0611] Step 1:

[0612] The user launches the News Summary Assistant application on their device, selects information categories and topics of interest (e.g., "Financial Market Trends"), and creates a request.

[0613] Step 2:

[0614] The user's request is sent to the server. The server queries multiple online sources on the internet to collect news articles related to the selected topic. In doing so, it uses APIs and scraping techniques to obtain the latest digital information.

[0615] Step 3:

[0616] The server inputs the collected news article data into a generative AI model. The AI ​​model analyzes the text and extracts key information from the articles. For example, it extracts major facts, announcements, and statistics mentioned in the articles and generates a summary.

[0617] Step 4:

[0618] The server ranks the generated summaries based on their importance and relevance. From these ranked summaries, the most relevant ones are selected for the user. This results in the user receiving more focused information.

[0619] Step 5:

[0620] The selected summaries are sent from the server to the user's device. Users can review the summaries and access more in-depth content by clicking on links to detailed articles or information that interest them.

[0621] Step 6:

[0622] Users provide feedback on the quality and relevance of the summaries they view within the app. This feedback is collected by a server and used to personalize and improve the accuracy of the generative AI model, thereby improving the user experience in the future.

[0623] (Example 1)

[0624] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0625] In today's world, a vast amount of information exists online, making it difficult for information users to quickly find information that is useful to them. This invention aims to alleviate the burden of information overload by efficiently collecting the information that information users seek, quickly summarizing its important elements, and providing them to them.

[0626] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0627] In this invention, the server includes means for receiving information classifications requested by information users, means for collecting relevant information from online information sources, and means for inputting the collected information into a generative learning model, extracting important information, and generating a summary. This makes it possible to quickly and efficiently summarize and provide the information that information users need.

[0628] "Information user" refers to an individual or group that seeks and intends to obtain specific information.

[0629] "Information classification" refers to specific categories or topics that information users are particularly interested in, and information gathering is carried out based on these selections.

[0630] "Online information sources" refer to internet resources that provide information, such as news sites, databases, and APIs that are publicly available on the web.

[0631] A "generative learning model" refers to an artificial intelligence model that uses pre-trained data to perform summarization and content generation in response to new inputs.

[0632] "Important information" refers to facts, figures, and conclusions among the collected information that are deemed particularly useful and valuable to the information user.

[0633] "Generating summaries" refers to the process of creating condensed information from data analyzed by a generative learning model, making it easily understandable to information users.

[0634] "Ranking and selecting based on value" refers to evaluating the generated summaries and prioritizing and selecting those that are considered most relevant and beneficial to the information user.

[0635] "Displaying connections to information users" refers to providing information users with links or actions that offer more detailed information or additional materials related to the summary.

[0636] This invention is a news summary system that efficiently collects and summarizes information needed by information users. The main components of the system include a "server" that collects and processes information, a "terminal" that requests information, and a "user" that inputs information.

[0637] The user launches the News Summary Assistant app from their device, selects the information categories they are interested in, and sends a request. The device then acts as a proxy, sending the request to the server based on the user's selection.

[0638] The server collects relevant information from online information sources in response to incoming requests. These information sources include news sites, databases, and APIs on the internet. The server accesses these sources to obtain the necessary digital information in real time.

[0639] Next, the server inputs the collected information into a generative learning model, which extracts key information and creates a summary. This AI model uses natural language processing algorithms. The model tokenizes the information and evaluates the importance of sentences to generate the most useful summary for the user.

[0640] The generated summaries are further evaluated and ranked on the server. This selects the summary that best matches the information user's requirements and sends it to the user via their terminal. The user can view the information provided on their terminal and, if necessary, follow connections to obtain more detailed information.

[0641] For example, if a user requests a summary of "international news," the server retrieves relevant information from multiple international news sites, and a generative learning model summarizes key points such as "agreements on new international agreements in major countries." This summary helps users accurately grasp the information they are looking for.

[0642] A concrete example of a prompt message would be a request such as, "Please provide a summary of the latest developments regarding international news topics." This allows information users to obtain the necessary information in a short amount of time.

[0643] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0644] Step 1:

[0645] The user launches the News Summary Assistant app through their device. They select the information categories and topics of interest, enter their request into the input form, and then press the submit button to send the request to the server. At this point, the input is the topic specified by the user, and the output is the specific request sent to the server.

[0646] Step 2:

[0647] The server accesses online information sources based on information classification requests received from users. These information sources include news sites and public APIs. Here, the server queries the specified information sources and collects the relevant digital information. The input is a search query based on the request, and the output is raw data collected in the form of news articles or datasets.

[0648] Step 3:

[0649] The server begins the process of inputting the collected digital information into a generative learning model. The model utilizes natural language processing techniques to tokenize the information, extract key points, and generate summaries. Specifically, the model analyzes the information received as input, highlights elements deemed important to the user, and summarizes them. The input for this step is the raw data collected in the previous stage, and the generated summary is the output.

[0650] Step 4:

[0651] The generated summaries are ranked on the server. The server evaluates the generated summaries and selects those containing the key points that information users are most likely to be interested in. The selection is based on the relevance and importance of the information. The input to the ranking process is summaries from a generative learning model, and the final selected summaries are obtained as output.

[0652] Step 5:

[0653] The server sends the selected summaries to the terminal. The transmission process includes links to related details along with the final selected summaries. The end user can view the summaries on the terminal and access further information via the links as needed. The input is the selected summaries, and the output is the summary information and links that the user receives on the terminal.

[0654] Step 6:

[0655] Users evaluate summaries and send feedback to the server via the app through their device. This feedback concerns the quality and relevance of the summary. The server collects this feedback and uses it to improve future generative learning models. The input is user feedback, and the output is evaluation information fed into the improvement cycle.

[0656] (Application Example 1)

[0657] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0658] In recent years, the rapid advancement of information technology has exposed urban administrators and residents to vast amounts of information, requiring them to quickly extract important information and utilize it efficiently. However, in such an information-overloaded environment, selecting and summarizing appropriate information is not easy, which can hinder efficient urban management and the provision of public services. Therefore, a system is needed that can summarize important information in real time and visualize it appropriately.

[0659] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0660] In this invention, the server includes means for receiving specific information categories requested by a user, means for collecting the relevant digital data from online information sources, and means for inputting the collected digital data into a generative artificial intelligence model to extract important information and generate a summary. This makes it possible to quickly obtain important information needed by city administrators and citizens, thereby improving the efficient management of cities and the provision of citizen services.

[0661] A "user" refers to an individual or organization that seeks to obtain information from a system, and is responsible for making requests for specific information.

[0662] An "information category" is a specific type of information that a system collects and categorizes for use with users.

[0663] "Digital data" refers to information expressed in electronic form obtained from online sources.

[0664] A "generative artificial intelligence model" is an advanced algorithm used to analyze vast amounts of information data and generate summaries based on that analysis.

[0665] A "summary" is an abstract of information that extracts the most important elements from collected information and presents them in a concise format.

[0666] "Visualization means" refers to an interface or technology that displays information in a way that allows users to easily understand and view it.

[0667] "Communication means" are technical means for sending and receiving information between a user and a system, and play a role in quickly transmitting specific information.

[0668] A "hyperlink" is an online reference point that allows users to access additional or more detailed information related to the topic.

[0669] A "smart device" is a portable electronic device equipped with internet connectivity that enables real-time notifications and information display.

[0670] This embodiment of the invention relates to a news summary system specifically designed for urban information management. Users can use portable devices such as smartphones or smart glasses to select specific information categories (e.g., transportation, public services, event information) and request information.

[0671] The server collects digital data from online sources via the internet and inputs this data into a generative artificial intelligence model in the cloud, such as OpenAI's GPT. This extracts key information from the digital data and generates concise summaries. The generated summaries are then ranked and sorted based on relevance.

[0672] Selected summaries are sent to the user's terminal and displayed through a dedicated application. This application visualizes the summaries in an easy-to-understand format and provides the user with relevant hyperlinks. This allows the user to access detailed information as needed.

[0673] For example, if a user requests "Today's city events information," the server collects relevant information and uses a generative artificial intelligence model to generate a summary such as "A music festival will be held in the city center from 3 PM," and notifies the user. In this way, the user can quickly obtain the information they need.

[0674] An example of a prompt for a generative AI model is, "Please provide a summary of the latest traffic information for the city." This allows the system to begin the process of collecting and summarizing the appropriate information.

[0675] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0676] Step 1:

[0677] The user uses a terminal to select a specific information category (e.g., transportation, public services) and submits a request. This input is initial information from the user that triggers processing on the server side.

[0678] Step 2:

[0679] The server collects relevant digital data from online sources on the internet based on the information categories received from the user. This process involves calling APIs from trusted sources to retrieve data. The input is the user's information categories, and the output is the collected raw data.

[0680] Step 3:

[0681] The server inputs the collected digital data into a generative artificial intelligence model. The generative AI model analyzes this data, extracts important information, and generates a summary. Data processing at this stage includes computational processing to organize and concisely summarize key points from a large amount of information. The input is the collected digital data, and the output is the generated summary.

[0682] Step 4:

[0683] The server ranks the generated summaries and sorts them based on relevance. This process uses machine learning algorithms to score importance and select the most useful summaries for the user. The input is the generated summaries, and the output is the sorted summaries.

[0684] Step 5:

[0685] The selected summaries are sent from the server to the terminal and displayed to the user by a dedicated application. The application visualizes the summaries in an easy-to-understand format, making them accessible through a user interface. The input is the selected summaries, and the output is the information displayed for the user to review.

[0686] Step 6:

[0687] Users can submit feedback on the information provided, which is aggregated on the server and used to improve the performance of the generative artificial intelligence model. This process includes analyzing the feedback data and inputting it into the model improvement algorithm. The input is the user's feedback, and the output is the improved AI model.

[0688] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0689] This invention is a news summary agent system incorporating an emotion engine, enabling users to receive more personalized news information. Specifically, it aims to optimize the presentation of information summaries by adjusting them according to the user's current emotional state, and further optimize the provision of useful information.

[0690] First, the user launches the news summary app on their device and selects information categories and topics of interest. The server receives this request and collects relevant digital information from appropriate sources on the internet. The collected information is summarized using a generative artificial intelligence model, and the main points are extracted.

[0691] The key feature of this system lies in the function of its emotion engine. While the user is using the device, the emotion engine analyzes the user's facial expressions, tone of voice, and the resulting emotional data. This data is sent to the server, where the user's emotional state is analyzed. For example, if the emotion engine determines that the user is feeling down, the server will improve the user experience by prioritizing the display of pre-prepared news summaries containing encouragement and positive content.

[0692] The server also provides other relevant perspectives and additional information based on emotional data and the user's interests and requests. For example, if a user expresses interest in "environmental issues" and the emotional engine determines that the user is stressed, the server will present summaries of articles reporting specific solutions or positive developments.

[0693] Finally, users review the summary they receive and provide feedback on its quality and content. The server accumulates this feedback and sentiment data, which is then used to tune the generative AI model, allowing it to continuously provide a more personalized experience. This system enables not only the delivery of information, but also the delivery of valuable information tailored to the user's emotions.

[0694] The following describes the processing flow.

[0695] Step 1:

[0696] Users launch the News Summary Assistant application on their device, select a specific information category or topic (e.g., "Healthcare," "Climate Change"), and send a request through the app.

[0697] Step 2:

[0698] The server receives requests from users and collects news articles related to the selected topic from online sources. This is done using news APIs and web scraping techniques to obtain reliable information.

[0699] Step 3:

[0700] The collected news article data is fed into a generative artificial intelligence model on a server. The generative AI model analyzes the article content, extracts important information, and generates a concise summary. This summary includes the main points and conclusions of the article.

[0701] Step 4:

[0702] An emotion engine built into the user's device analyzes the user's emotional state in real time. This process uses the camera and microphone to analyze the user's facial expressions and tone of voice to identify feelings such as joy, sadness, and excitement that the user is currently experiencing.

[0703] Step 5:

[0704] The analysis results from the emotion engine are sent to the server. The server uses the received emotion data to rank the generated summaries and select the summary that best suits the user's emotional state. For example, if the user is determined to be tired, the server will prioritize selecting summaries with less information and more positive content.

[0705] Step 6:

[0706] The server sends the selected summaries to the user's device. The user can view these summaries and, if more detailed information is needed, refer to the original articles using the provided links.

[0707] Step 7:

[0708] Users input feedback on the summary content into their device. This feedback is sent to the server and used, along with sentiment data, to improve the generative AI model. This allows the system to evolve, providing users with more relevant and personalized information.

[0709] (Example 2)

[0710] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0711] Conventional information delivery systems specialize in data collection and summarization from specific information domains, but they have limitations in providing personalized experiences that reflect the emotional state of users. As a result, appropriate information that is not tailored to the specific mental state and context of the information recipient is often not provided, making it difficult to improve user satisfaction.

[0712] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0713] In this invention, the server includes means for receiving a specific information area requested by a user, means for acquiring relevant digital data from information sources on a telecommunications network, and means for adjusting a summary formed based on the user's emotional state. This enables the provision of personalized information according to the user's emotional state, thereby achieving higher satisfaction and an optimized user experience.

[0714] "Information domain" refers to information in a specific field or category that a user requests.

[0715] "Telecommunications network" refers to the internet and other network infrastructure that enables the transmission and reception of information.

[0716] "Digital data" refers to information that is generated or stored electronically, including text, images, audio, and video.

[0717] A "generative artificial intelligence model" refers to an algorithm that generates information based on large amounts of data, extracts knowledge through learning, and creates new summaries and content.

[0718] "Emotional state" refers to a user's temporary psychological emotional state and is classified into categories such as positive, negative, and neutral.

[0719] A "summary" is a way of condensing the main points and knowledge of information and providing them in a short format.

[0720] "Related links" refer to hyperlinks that provide further information or details related to the generated summary.

[0721] This invention is a system for providing personalized information to users. The entire system includes a terminal, a server, a generative artificial intelligence model, and an emotion analysis engine.

[0722] The user launches a news summary app on their device and inputs information about specific areas of interest. This app sends the user's requests to a server. The device has built-in hardware such as a camera and microphone to collect emotional data.

[0723] The server uses telecommunications networks to collect relevant digital data from the internet. Web scraping tools and APIs may be used for this collection. The collected digital data is input into a generative artificial intelligence model to extract key knowledge and form a summary. This AI model leverages machine learning techniques to efficiently generate the main points of the content.

[0724] The device analyzes the user's emotional state in real time using an emotion analysis engine. The device's camera and microphone capture the user's facial expressions and voice tone, and send the data to a server for analysis of the user's psychological state.

[0725] The server uses this sentiment data to refine the summaries and rank them according to relevance. Because the refined summaries are edited to match the user's emotions, the quality of the user experience is improved.

[0726] For example, if a user expresses interest in "environmental issues" and the sentiment analysis engine determines that they are stressed, the server will prioritize summarizing and displaying articles that report positive developments.

[0727] Examples of prompts include, "Generate a summary of environmental articles with positive content based on the user's current emotional state," and "Generate a summary of the latest technology news and adjust the display order considering the user's emotional data."

[0728] In this way, the system goes beyond simply providing information and realizes value-added services that respond to the emotional state of the user.

[0729] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0730] Step 1:

[0731] The user launches a news summary app on their device and selects a specific information area. The device receives the user's selected information category as input. The device sends this to the server, initiating a request for information retrieval. The server receives the user's areas of interest as output.

[0732] Step 2:

[0733] The server collects relevant digital data from various sources on the internet via telecommunications networks. It uses web scraping tools and APIs to retrieve data related to specified information categories. The collected data is stored on the server, and the process proceeds to the next step. As output, a collection of digital data is formed on the server.

[0734] Step 3:

[0735] The server inputs the collected digital data into a generative artificial intelligence model. The AI ​​model analyzes the input data, extracts important information, and generates a summary. In this process, it identifies the main points of the information and creates a summary sentence. As output, the summary is sent back to the server.

[0736] Step 4:

[0737] The device uses a camera and microphone to collect user emotion data, detecting facial expressions, voice tone, and other characteristics. Using this data, the device performs emotion analysis and sends the results to a server. Emotion data is acquired as input, and the analysis results are transferred to the server as output.

[0738] Step 5:

[0739] The server receives sentiment data and adjusts the summary based on that information. This adjustment involves filtering, such as prioritizing positive content in the summary according to the user's emotional state. The adjusted summary is then prepared as output.

[0740] Step 6:

[0741] The server sends the adjusted summary to the user's terminal, which then displays it. The user can view the displayed summary and provide feedback. As output, the final news summary displayed to the user is provided.

[0742] Step 7:

[0743] The server collects user feedback and sentiment data, which is then used to tune the generative artificial intelligence model. This information is used as training material to improve the model's performance. As an output, the model's accuracy improves.

[0744] (Application Example 2)

[0745] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0746] In modern society, users have access to a vast amount of information, but they are required to efficiently select the necessary and useful information from it. Furthermore, since the way information is received differs depending on an individual's emotional state, there is a need for means of providing information in an optimal form that corresponds to the user's current emotions. This invention aims to optimize information provision while taking into account the user's emotional state.

[0747] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0748] In this invention, the server includes means for acquiring a specific information genre requested by the user, means for collecting relevant digital materials from online information sources, means for inputting the collected digital materials into a generative artificial intelligence model to extract important points and generate a summary, and means for recognizing the user's current emotional state and adjusting the news summary accordingly. This makes it possible to provide valuable information optimized for the user's emotional state.

[0749] "User" refers to each individual user who uses this system to obtain information.

[0750] "Information genre" refers to a specific field or topic of information that a user is interested in and requests.

[0751] "Online information sources" refers to all media that provide digital information accessible on the internet, such as news sites, databases, and blogs.

[0752] "Digital materials" refer to data in electronically stored formats, including text, images, videos, and other information formats.

[0753] A "generative artificial intelligence model" is a computational model for artificial intelligence that generates new information based on large amounts of data.

[0754] "Key points" refer to the content of the collected information that is deemed particularly valuable.

[0755] A "summary" refers to a document that extracts the key points of information and presents them concisely.

[0756] "Ranking" is the process of ordering summaries based on specific criteria and determining their priority.

[0757] "Emotional state" refers to the psychological atmosphere or mood that a user exhibits when receiving information.

[0758] "Adjusting news summaries" refers to the act of modifying the wording and content of summarized news articles to take into account the user's emotional state.

[0759] This invention relates to a news summary system equipped with an emotion recognition engine. An application installed on the user's device can capture the user's facial expressions and voice in real time using the smartphone's camera and microphone. This captured data is analyzed on the device using machine learning libraries such as TensorFlow to identify the user's emotional state.

[0760] The server collects digital materials from online information sources based on the information genre specified by the user. The collected materials are summarized using OpenAI's generative artificial intelligence model, and key points are extracted.

[0761] Furthermore, the emotion engine adjusts the generated summaries according to the user's emotional state, delivering news in the most optimal way for the user. For example, if a user is experiencing everyday stress, the server prioritizes delivering content that includes positive news and solutions.

[0762] A specific use case would be when a user requests information about environmental issues. The server would then search for relevant news articles and provide a summary generated using a generative AI model, adjusting the content to include positive stories and progress for users in a relaxed state.

[0763] An example of a prompt message would be, "Generate news summaries that are recommended when the user's mood is relaxed." This is how you would instruct the generation AI model.

[0764] Such a system allows users to efficiently receive information that fits their emotional state, enabling them to meaningfully process the latest news even in an information-saturated society.

[0765] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0766] Step 1:

[0767] The device captures the user's facial expressions and voice in real time using the user's camera and microphone. This data is analyzed using machine learning libraries such as TensorFlow to identify the user's emotional state. The input is the user's facial expressions and voice data, and the output is a tag indicating the emotional state. Specifically, facial recognition algorithms and spectral analysis of the voice are used to analyze subtle changes in facial expressions and tone of voice.

[0768] Step 2:

[0769] The user selects information genres on the device according to their interests. This action causes the device to send the selected information genres to the server. The input is the user's selected information genres, and the output is the transmission of the selected information to the server. Specifically, this involves selecting information categories from a list using the touch panel.

[0770] Step 3:

[0771] The server collects relevant digital materials from online information sources based on the selected information genre. The input in this process is the user's genre request, and the output is a collection of digital materials. Specifically, it uses web crawling technology to search for articles and blogs that match the specified keywords.

[0772] Step 4:

[0773] The server inputs the collected digital materials into an AI model, which extracts key points and generates a summary. Instructions are given to the AI ​​using prompts. The input is the text data of the digital materials, and the output is the summarized text. Specifically, the AI ​​model uses natural language processing to analyze keywords and context within the text and generate the summary.

[0774] Step 5:

[0775] The server adjusts the generated summary according to the user's emotional state. Based on this information, it makes adjustments such as emphasizing positive content. The input is the generated summary and the emotional state, and the output is the adjusted summary. Specifically, if the emotional state is determined to be "relaxed," the server will present many episodes containing words of encouragement.

[0776] Step 6:

[0777] The refined summary is sent to the device and displayed to the user. The user reviews the news and evaluates its content. The input is the refined summary, and the output is the user's feedback. Specifically, the summary is displayed in text format on the device's screen, and the user clicks a button to submit feedback.

[0778] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0779] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0780] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0781] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0782] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0783] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0784] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0785] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0786] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0787] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0788] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0789] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0790] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0791] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0792] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0793] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0794] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0795] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0796] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0797] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0798] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0799] The following is further disclosed regarding the embodiments described above.

[0800] (Claim 1)

[0801] A means of receiving specific information categories requested by the user,

[0802] Means of collecting relevant digital information from online sources,

[0803] A means of inputting collected digital information into a generative artificial intelligence model, extracting important information, and generating a summary,

[0804] A means for ranking the generated summaries and selecting them based on relevance,

[0805] A means of sending the selected summary to the user,

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, comprising means for receiving user feedback on the quality and relevance of the generated summaries and reflecting it in improving the performance of the generative artificial intelligence model.

[0809] (Claim 3)

[0810] The system according to claim 1, further comprising means for displaying relevant links to the user in order to provide details of a summary generated based on information requested by the user.

[0811] "Example 1"

[0812] (Claim 1)

[0813] A means of receiving information classification requested by information users,

[0814] Means of collecting relevant information from online information sources,

[0815] A means of inputting collected information into a generative learning model, extracting important information, and generating a summary,

[0816] A means for evaluating and ranking the generated summaries and selecting them based on value,

[0817] A means of sending the selected summaries to information users,

[0818] A system that includes this.

[0819] (Claim 2)

[0820] The system according to claim 1, comprising means for receiving feedback from information users regarding the quality and importance of the generated summaries and reflecting this feedback in improving the performance of the generative learning model.

[0821] (Claim 3)

[0822] The system according to claim 1, comprising means for displaying relevant connections to an information user in order to provide details of a summary generated based on information requested by the information user.

[0823] "Application Example 1"

[0824] (Claim 1)

[0825] A means of receiving a specific information category requested by the user,

[0826] Means for collecting relevant digital data from online sources,

[0827] A means of inputting collected digital data into a generative artificial intelligence model, extracting important information, and generating a summary,

[0828] A means for ranking the generated summaries and selecting them based on relevance,

[0829] A means of sending the selected summary to the user,

[0830] A visualization method that dynamically displays selected summaries and makes them accessible through a user interface,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, comprising means for receiving user feedback on the quality and relevance of the generated summaries and reflecting it in improving the performance of the generative artificial intelligence model, and further comprising means for rapidly transmitting urban management information.

[0834] (Claim 3)

[0835] The system according to claim 1, further comprising the function of displaying relevant hyperlinks to the user and enabling real-time notifications using a smart device in order to provide details of a summary generated based on information requested by the user.

[0836] "Example 2 of combining an emotion engine"

[0837] (Claim 1)

[0838] A means for receiving a specific information area requested by the user,

[0839] Means for obtaining relevant digital data from information sources on telecommunications networks,

[0840] A means of supplying acquired digital data to a generative artificial intelligence model, extracting important knowledge, and forming a summary,

[0841] A means for adjusting a summary formed based on the user's emotional state,

[0842] A means for evaluating and classifying adjusted summaries according to their relevance,

[0843] A means of delivering classified summaries to users,

[0844] A system that includes this.

[0845] (Claim 2)

[0846] The system according to claim 1, comprising means for receiving user feedback on the quality and relevance of the generated summaries and reflecting it in improving the performance of the generative artificial intelligence model, and means for analyzing sentiment data and providing personalized summaries to improve the user experience.

[0847] (Claim 3)

[0848] The system according to claim 1, comprising means for displaying relevant links to the user in order to provide the user with details of a summary formed based on the information requested by the user.

[0849] "Application example 2 when combining with an emotional engine"

[0850] (Claim 1)

[0851] A means of obtaining a specific information category requested by the user,

[0852] Means of collecting relevant digital materials from online information sources,

[0853] A method for inputting collected digital materials into a generative artificial intelligence model, extracting key points, and generating a summary,

[0854] A method for ranking the generated summaries and selecting them based on relevance,

[0855] A means of recognizing the user's current emotional state and adjusting the news summary accordingly,

[0856] A means of sending a refined summary to the user,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, comprising means for collecting user feedback on the quality and relevance of the generated summaries and reflecting this feedback in improving the performance of the generative artificial intelligence model.

[0860] (Claim 3)

[0861] The system according to claim 1, further comprising means for displaying relevant links to the user in order to present details of a summary generated based on the information requested by the user. [Explanation of Symbols]

[0862] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving a specific information category requested by the user, Means for collecting relevant digital data from online sources, A means of inputting collected digital data into a generative artificial intelligence model, extracting important information, and generating a summary, A means for ranking the generated summaries and selecting them based on relevance, A means of sending the selected summary to the user, A visualization method that dynamically displays selected summaries and makes them accessible through a user interface, A system that includes this.

2. The system according to claim 1, comprising means for receiving user feedback on the quality and relevance of the generated summaries and reflecting it in improving the performance of the generative artificial intelligence model, and further comprising means for rapidly transmitting urban management information.

3. The system according to claim 1, further comprising a function to display relevant hyperlinks to the user in order to provide details of a summary generated based on information requested by the user, and to enable real-time notifications using a smart device.

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